Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review

Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wang, Hannah Szu-Han [verfasserIn] Yao, Yuan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Biomass-derived material Machine learning Sustainability Water Agriculture Biochar

Übergeordnetes Werk:	Enthalten in: Resources, conservation and recycling - Amsterdam [u.a.] : Elsevier Science, 1988, 190
Übergeordnetes Werk:	volume:190

DOI / URN:	10.1016/j.resconrec.2022.106847

Katalog-ID:	ELV061829897

Internformat


LEADER	01000caa a22002652 4500
001	ELV061829897
003	DE-627
005	20230927091953.0
007	cr uuu---uuuuu
008	230818s2022 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1016/j.resconrec.2022.106847 \|2 doi
035			\|a (DE-627)ELV061829897
035			\|a (ELSEVIER)S0921-3449(22)00679-6
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
082	0	4	\|a 690 \|q VZ
084			\|a 43.33 \|2 bkl
084			\|a 58.53 \|2 bkl
084			\|a 83.63 \|2 bkl
100	1		\|a Wang, Hannah Szu-Han \|e verfasserin \|4 aut
245	1	0	\|a Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
264		1	\|c 2022
336			\|a nicht spezifiziert \|b zzz \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
520			\|a Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.
650		4	\|a Biomass-derived material
650		4	\|a Machine learning
650		4	\|a Sustainability
650		4	\|a Water
650		4	\|a Agriculture
650		4	\|a Biochar
700	1		\|a Yao, Yuan \|e verfasserin \|0 (orcid)0000-0001-9359-2030 \|4 aut
773	0	8	\|i Enthalten in \|t Resources, conservation and recycling \|d Amsterdam [u.a.] : Elsevier Science, 1988 \|g 190 \|h Online-Ressource \|w (DE-627)306591359 \|w (DE-600)1498716-8 \|w (DE-576)259484199 \|7 nnns
773	1	8	\|g volume:190
912			\|a GBV_USEFLAG_U
912			\|a GBV_ELV
912			\|a SYSFLAG_U
912			\|a SSG-OPC-GGO
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_32
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_74
912			\|a GBV_ILN_90
912			\|a GBV_ILN_95
912			\|a GBV_ILN_100
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_150
912			\|a GBV_ILN_151
912			\|a GBV_ILN_224
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_702
912			\|a GBV_ILN_2003
912			\|a GBV_ILN_2004
912			\|a GBV_ILN_2005
912			\|a GBV_ILN_2011
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2015
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2025
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2038
912			\|a GBV_ILN_2044
912			\|a GBV_ILN_2048
912			\|a GBV_ILN_2049
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2056
912			\|a GBV_ILN_2059
912			\|a GBV_ILN_2061
912			\|a GBV_ILN_2064
912			\|a GBV_ILN_2065
912			\|a GBV_ILN_2068
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_2112
912			\|a GBV_ILN_2113
912			\|a GBV_ILN_2118
912			\|a GBV_ILN_2122
912			\|a GBV_ILN_2129
912			\|a GBV_ILN_2143
912			\|a GBV_ILN_2147
912			\|a GBV_ILN_2148
912			\|a GBV_ILN_2152
912			\|a GBV_ILN_2153
912			\|a GBV_ILN_2190
912			\|a GBV_ILN_2336
912			\|a GBV_ILN_2507
912			\|a GBV_ILN_2522
912			\|a GBV_ILN_4035
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4242
912			\|a GBV_ILN_4251
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4333
912			\|a GBV_ILN_4334
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4393
936	b	k	\|a 43.33 \|j Umweltfreundliche Nutzung natürlicher Ressourcen \|q VZ
936	b	k	\|a 58.53 \|j Abfallwirtschaft \|q VZ
936	b	k	\|a 83.63 \|j Volkswirtschaftliche Ressourcen \|j Umweltökonomie \|q VZ
951			\|a AR
952			\|d 190

Indexfelder

author_variant	h s h w hsh hshw y y yy
matchkey_str	wanghannahszuhanyaoyuan:2022----:ahnlannfrutialdvlpetnapiainoboasnboaseiecroaeumtras
hierarchy_sort_str	2022
bklnumber	43.33 58.53 83.63
publishDate	2022
allfields	10.1016/j.resconrec.2022.106847 doi (DE-627)ELV061829897 (ELSEVIER)S0921-3449(22)00679-6 DE-627 ger DE-627 rda eng 690 VZ 43.33 bkl 58.53 bkl 83.63 bkl Wang, Hannah Szu-Han verfasserin aut Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems. Biomass-derived material Machine learning Sustainability Water Agriculture Biochar Yao, Yuan verfasserin (orcid)0000-0001-9359-2030 aut Enthalten in Resources, conservation and recycling Amsterdam [u.a.] : Elsevier Science, 1988 190 Online-Ressource (DE-627)306591359 (DE-600)1498716-8 (DE-576)259484199 nnns volume:190 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.33 Umweltfreundliche Nutzung natürlicher Ressourcen VZ 58.53 Abfallwirtschaft VZ 83.63 Volkswirtschaftliche Ressourcen Umweltökonomie VZ AR 190
spelling	10.1016/j.resconrec.2022.106847 doi (DE-627)ELV061829897 (ELSEVIER)S0921-3449(22)00679-6 DE-627 ger DE-627 rda eng 690 VZ 43.33 bkl 58.53 bkl 83.63 bkl Wang, Hannah Szu-Han verfasserin aut Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems. Biomass-derived material Machine learning Sustainability Water Agriculture Biochar Yao, Yuan verfasserin (orcid)0000-0001-9359-2030 aut Enthalten in Resources, conservation and recycling Amsterdam [u.a.] : Elsevier Science, 1988 190 Online-Ressource (DE-627)306591359 (DE-600)1498716-8 (DE-576)259484199 nnns volume:190 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.33 Umweltfreundliche Nutzung natürlicher Ressourcen VZ 58.53 Abfallwirtschaft VZ 83.63 Volkswirtschaftliche Ressourcen Umweltökonomie VZ AR 190
allfields_unstemmed	10.1016/j.resconrec.2022.106847 doi (DE-627)ELV061829897 (ELSEVIER)S0921-3449(22)00679-6 DE-627 ger DE-627 rda eng 690 VZ 43.33 bkl 58.53 bkl 83.63 bkl Wang, Hannah Szu-Han verfasserin aut Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems. Biomass-derived material Machine learning Sustainability Water Agriculture Biochar Yao, Yuan verfasserin (orcid)0000-0001-9359-2030 aut Enthalten in Resources, conservation and recycling Amsterdam [u.a.] : Elsevier Science, 1988 190 Online-Ressource (DE-627)306591359 (DE-600)1498716-8 (DE-576)259484199 nnns volume:190 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.33 Umweltfreundliche Nutzung natürlicher Ressourcen VZ 58.53 Abfallwirtschaft VZ 83.63 Volkswirtschaftliche Ressourcen Umweltökonomie VZ AR 190
allfieldsGer	10.1016/j.resconrec.2022.106847 doi (DE-627)ELV061829897 (ELSEVIER)S0921-3449(22)00679-6 DE-627 ger DE-627 rda eng 690 VZ 43.33 bkl 58.53 bkl 83.63 bkl Wang, Hannah Szu-Han verfasserin aut Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems. Biomass-derived material Machine learning Sustainability Water Agriculture Biochar Yao, Yuan verfasserin (orcid)0000-0001-9359-2030 aut Enthalten in Resources, conservation and recycling Amsterdam [u.a.] : Elsevier Science, 1988 190 Online-Ressource (DE-627)306591359 (DE-600)1498716-8 (DE-576)259484199 nnns volume:190 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.33 Umweltfreundliche Nutzung natürlicher Ressourcen VZ 58.53 Abfallwirtschaft VZ 83.63 Volkswirtschaftliche Ressourcen Umweltökonomie VZ AR 190
allfieldsSound	10.1016/j.resconrec.2022.106847 doi (DE-627)ELV061829897 (ELSEVIER)S0921-3449(22)00679-6 DE-627 ger DE-627 rda eng 690 VZ 43.33 bkl 58.53 bkl 83.63 bkl Wang, Hannah Szu-Han verfasserin aut Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems. Biomass-derived material Machine learning Sustainability Water Agriculture Biochar Yao, Yuan verfasserin (orcid)0000-0001-9359-2030 aut Enthalten in Resources, conservation and recycling Amsterdam [u.a.] : Elsevier Science, 1988 190 Online-Ressource (DE-627)306591359 (DE-600)1498716-8 (DE-576)259484199 nnns volume:190 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 43.33 Umweltfreundliche Nutzung natürlicher Ressourcen VZ 58.53 Abfallwirtschaft VZ 83.63 Volkswirtschaftliche Ressourcen Umweltökonomie VZ AR 190
language	English
source	Enthalten in Resources, conservation and recycling 190 volume:190
sourceStr	Enthalten in Resources, conservation and recycling 190 volume:190
format_phy_str_mv	Article
bklname	Umweltfreundliche Nutzung natürlicher Ressourcen Abfallwirtschaft Volkswirtschaftliche Ressourcen Umweltökonomie
institution	findex.gbv.de
topic_facet	Biomass-derived material Machine learning Sustainability Water Agriculture Biochar
dewey-raw	690
isfreeaccess_bool	false
container_title	Resources, conservation and recycling
authorswithroles_txt_mv	Wang, Hannah Szu-Han @@aut@@ Yao, Yuan @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
hierarchy_top_id	306591359
dewey-sort	3690
id	ELV061829897
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV061829897</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230927091953.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230818s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.resconrec.2022.106847</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV061829897</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0921-3449(22)00679-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.33</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.53</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.63</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Hannah Szu-Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomass-derived material</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sustainability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Water</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Agriculture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biochar</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Yuan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9359-2030</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Resources, conservation and recycling</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1988</subfield><subfield code="g">190</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306591359</subfield><subfield code="w">(DE-600)1498716-8</subfield><subfield code="w">(DE-576)259484199</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">43.33</subfield><subfield code="j">Umweltfreundliche Nutzung natürlicher Ressourcen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.53</subfield><subfield code="j">Abfallwirtschaft</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.63</subfield><subfield code="j">Volkswirtschaftliche Ressourcen</subfield><subfield code="j">Umweltökonomie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">190</subfield></datafield></record></collection>
author	Wang, Hannah Szu-Han
spellingShingle	Wang, Hannah Szu-Han ddc 690 bkl 43.33 bkl 58.53 bkl 83.63 misc Biomass-derived material misc Machine learning misc Sustainability misc Water misc Agriculture misc Biochar Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
authorStr	Wang, Hannah Szu-Han
ppnlink_with_tag_str_mv	@@773@@(DE-627)306591359
format	electronic Article
dewey-ones	690 - Buildings
delete_txt_mv	keep
author_role	aut aut
collection	elsevier
remote_str	true
illustrated	Not Illustrated
topic_title	690 VZ 43.33 bkl 58.53 bkl 83.63 bkl Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review Biomass-derived material Machine learning Sustainability Water Agriculture Biochar
topic	ddc 690 bkl 43.33 bkl 58.53 bkl 83.63 misc Biomass-derived material misc Machine learning misc Sustainability misc Water misc Agriculture misc Biochar
topic_unstemmed	ddc 690 bkl 43.33 bkl 58.53 bkl 83.63 misc Biomass-derived material misc Machine learning misc Sustainability misc Water misc Agriculture misc Biochar
topic_browse	ddc 690 bkl 43.33 bkl 58.53 bkl 83.63 misc Biomass-derived material misc Machine learning misc Sustainability misc Water misc Agriculture misc Biochar
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Resources, conservation and recycling
hierarchy_parent_id	306591359
dewey-tens	690 - Building & construction
hierarchy_top_title	Resources, conservation and recycling
isfreeaccess_txt	false
familylinks_str_mv	(DE-627)306591359 (DE-600)1498716-8 (DE-576)259484199
title	Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
ctrlnum	(DE-627)ELV061829897 (ELSEVIER)S0921-3449(22)00679-6
title_full	Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
author_sort	Wang, Hannah Szu-Han
journal	Resources, conservation and recycling
journalStr	Resources, conservation and recycling
lang_code	eng
isOA_bool	false
dewey-hundreds	600 - Technology
recordtype	marc
publishDateSort	2022
contenttype_str_mv	zzz
author_browse	Wang, Hannah Szu-Han Yao, Yuan
container_volume	190
class	690 VZ 43.33 bkl 58.53 bkl 83.63 bkl
format_se	Elektronische Aufsätze
author-letter	Wang, Hannah Szu-Han
doi_str_mv	10.1016/j.resconrec.2022.106847
normlink	(ORCID)0000-0001-9359-2030
normlink_prefix_str_mv	(orcid)0000-0001-9359-2030
dewey-full	690
author2-role	verfasserin
title_sort	machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: a review
title_auth	Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
abstract	Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.
abstractGer	Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.
abstract_unstemmed	Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.
collection_details	GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-GGO GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393
title_short	Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review
remote_bool	true
author2	Yao, Yuan
author2Str	Yao, Yuan
ppnlink	306591359
mediatype_str_mv	c
isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1016/j.resconrec.2022.106847
up_date	2024-07-06T18:05:56.929Z
_version_	1803853923018080256
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">ELV061829897</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230927091953.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230818s2022 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1016/j.resconrec.2022.106847</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)ELV061829897</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ELSEVIER)S0921-3449(22)00679-6</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">690</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">43.33</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">58.53</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">83.63</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wang, Hannah Szu-Han</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">nicht spezifiziert</subfield><subfield code="b">zzz</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Biomass-derived materials (BDM) have broad applications in water and agricultural systems. As an emerging tool, Machine learning (ML) has been applied to BDM systems to address material, process, and supply chain design challenges. This paper reviewed 53 papers published since 2008 to understand the capabilities, current limitations, and future potentials of ML in supporting sustainable development and applications of BDM. Previous ML applications were classified into three categories based on their objectives – material and process design, end-use performance prediction, and sustainability assessment. These ML applications focus on identifying critical factors for optimizing BDM systems, predicting material features and performances, reverse engineering, and addressing data challenges for sustainability assessments. BDM datasets show large variations, and ∼75% of them possess < 600 data points. Ensemble models and state-of-the-art neural networks (NNs) perform and generalize well on such datasets. Limitations for scaling up ML for BDM systems lie in the low interpretability of the ensemble and NN models and the lack of studies in sustainability assessment that consider geo-temporal dynamics. A workflow is recommended for future ML studies for BDM systems. More research is needed to explore ML applications for sustainable development, assessment, and optimization of BDM systems.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biomass-derived material</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sustainability</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Water</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Agriculture</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Biochar</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Yao, Yuan</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-9359-2030</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Resources, conservation and recycling</subfield><subfield code="d">Amsterdam [u.a.] : Elsevier Science, 1988</subfield><subfield code="g">190</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)306591359</subfield><subfield code="w">(DE-600)1498716-8</subfield><subfield code="w">(DE-576)259484199</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ELV</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-GGO</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_32</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_74</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_90</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_100</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_150</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_224</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_702</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2003</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2004</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2005</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2011</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2015</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2025</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2038</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2044</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2048</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2049</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2056</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2059</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2061</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2064</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2065</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2068</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2113</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2118</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2122</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2129</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2143</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2147</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2148</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2153</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2190</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2336</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2507</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2522</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4035</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4242</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4251</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4333</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4334</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4393</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">43.33</subfield><subfield code="j">Umweltfreundliche Nutzung natürlicher Ressourcen</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">58.53</subfield><subfield code="j">Abfallwirtschaft</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="936" ind1="b" ind2="k"><subfield code="a">83.63</subfield><subfield code="j">Volkswirtschaftliche Ressourcen</subfield><subfield code="j">Umweltökonomie</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">190</subfield></datafield></record></collection>
score	7.4022093

Nicht das Richtige dabei?

Schreiben Sie uns!

Machine learning for sustainable development and applications of biomass and biomass-derived carbonaceous materials in water and agricultural systems: A review

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?