The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction
Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kisi, Ozgur [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Umfang: |
13 |
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| Übergeordnetes Werk: |
Enthalten in: Towards unravelling the Rosette agent enigma: Spread and emergence of the co-invasive host-pathogen complex, - Marine, Combe ELSEVIER, 2021, an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution, New York, NY [u.a.] |
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| Übergeordnetes Werk: |
volume:174 ; year:2019 ; pages:11-23 ; extent:13 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.catena.2018.10.047 |
|---|
| Katalog-ID: |
ELV045277338 |
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| 245 | 1 | 4 | |a The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction |
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| 520 | |a Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. | ||
| 520 | |a Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. | ||
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10.1016/j.catena.2018.10.047 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001248.pica (DE-627)ELV045277338 (ELSEVIER)S0341-8162(18)30482-X DE-627 ger DE-627 rakwb eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Kisi, Ozgur verfasserin aut The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Yaseen, Zaher Mundher oth Enthalten in Elsevier Marine, Combe ELSEVIER Towards unravelling the Rosette agent enigma: Spread and emergence of the co-invasive host-pathogen complex, 2021 an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution New York, NY [u.a.] (DE-627)ELV006991912 volume:174 year:2019 pages:11-23 extent:13 https://doi.org/10.1016/j.catena.2018.10.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 174 2019 11-23 13 |
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10.1016/j.catena.2018.10.047 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001248.pica (DE-627)ELV045277338 (ELSEVIER)S0341-8162(18)30482-X DE-627 ger DE-627 rakwb eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Kisi, Ozgur verfasserin aut The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Yaseen, Zaher Mundher oth Enthalten in Elsevier Marine, Combe ELSEVIER Towards unravelling the Rosette agent enigma: Spread and emergence of the co-invasive host-pathogen complex, 2021 an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution New York, NY [u.a.] (DE-627)ELV006991912 volume:174 year:2019 pages:11-23 extent:13 https://doi.org/10.1016/j.catena.2018.10.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 174 2019 11-23 13 |
| allfields_unstemmed |
10.1016/j.catena.2018.10.047 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001248.pica (DE-627)ELV045277338 (ELSEVIER)S0341-8162(18)30482-X DE-627 ger DE-627 rakwb eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Kisi, Ozgur verfasserin aut The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Yaseen, Zaher Mundher oth Enthalten in Elsevier Marine, Combe ELSEVIER Towards unravelling the Rosette agent enigma: Spread and emergence of the co-invasive host-pathogen complex, 2021 an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution New York, NY [u.a.] (DE-627)ELV006991912 volume:174 year:2019 pages:11-23 extent:13 https://doi.org/10.1016/j.catena.2018.10.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 174 2019 11-23 13 |
| allfieldsGer |
10.1016/j.catena.2018.10.047 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001248.pica (DE-627)ELV045277338 (ELSEVIER)S0341-8162(18)30482-X DE-627 ger DE-627 rakwb eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Kisi, Ozgur verfasserin aut The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Yaseen, Zaher Mundher oth Enthalten in Elsevier Marine, Combe ELSEVIER Towards unravelling the Rosette agent enigma: Spread and emergence of the co-invasive host-pathogen complex, 2021 an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution New York, NY [u.a.] (DE-627)ELV006991912 volume:174 year:2019 pages:11-23 extent:13 https://doi.org/10.1016/j.catena.2018.10.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 174 2019 11-23 13 |
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10.1016/j.catena.2018.10.047 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001248.pica (DE-627)ELV045277338 (ELSEVIER)S0341-8162(18)30482-X DE-627 ger DE-627 rakwb eng 333.7 610 VZ 43.12 bkl 43.13 bkl 44.13 bkl Kisi, Ozgur verfasserin aut The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction 2019transfer abstract 13 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. Yaseen, Zaher Mundher oth Enthalten in Elsevier Marine, Combe ELSEVIER Towards unravelling the Rosette agent enigma: Spread and emergence of the co-invasive host-pathogen complex, 2021 an interdisciplinary journal of soil science, hydrology, geomorphology focusing on geoecology and landscape evolution New York, NY [u.a.] (DE-627)ELV006991912 volume:174 year:2019 pages:11-23 extent:13 https://doi.org/10.1016/j.catena.2018.10.047 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA SSG-OPC-GGO 43.12 Umweltchemie VZ 43.13 Umwelttoxikologie VZ 44.13 Medizinische Ökologie VZ AR 174 2019 11-23 13 |
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The potential of hybrid evolutionary fuzzy intelligence model for suspended sediment concentration prediction |
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Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. |
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Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. |
| abstract_unstemmed |
Providing a robust and reliable prediction model for suspended sediment concentration (SSC) is an essential task for several environmental and geomorphology prospective including water quality, river bed engineering sustainability, and aquatic habitats. In this research, a novel hybrid intelligence approach based on evolutionary fuzzy (EF) approach is developed to predict river suspended sediment concentration. To demonstrate the modeling application, one of the highly affected rivers located in the north-western part of California is selected as a case study (i.e., Eel River). Eel River is considered as one of the most polluted river due to the streamside land sliding, owing to the highly stochastic water river discharge. Thus, the predictive model is constructed using discharge information as it is the main trigger for the SSC amount. The prediction conducted on different locations of the stream (i.e., up-stream and down-stream stations). Three different well-established integrative fuzzy models are developed for the validation purpose including adaptive neuro-fuzzy inference system coupled with subtractive clustering (ANFIS-SC), grid partition (ANFIS-GP), and fuzzy c-means (ANFIS-FCM) models. The predictive models evaluated based on several numerical indicators and two-dimension graphical diagram (i.e., Taylor diagram) that vividly exhibits the observed and predicted values. The attained results evidenced the predictability of the EF model for the SSC over the other models. The discharge information provided an excellent input attributes for the predictive models. In summary, the discovered model showed an outstanding data-intelligence model for the environmental perspective and particularly for Eel River. The methodology is highly qualified to be implemented as a real-time prediction model that can provide a brilliant approach for the river engineering sustainability. |
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