Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil

Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventu...
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Gespeichert in:

Autor*in:	Maria, Tamara Ribeiro Botelho de Carvalho [verfasserIn] Biondi, Daniela [verfasserIn] Behling, Alexandre [verfasserIn] Reis, Allan Rodrigo Nunho dos [verfasserIn] Zamproni, Kendra [verfasserIn] Ho, Tatiane Lima [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Decision tree algorithm Tree risk management Urban forests Urban trees

Übergeordnetes Werk:	Enthalten in: Urban forestry & urban greening - Jena : Urban & Fischer, 2002, 81
Übergeordnetes Werk:	volume:81

DOI / URN:	10.1016/j.ufug.2023.127844

Katalog-ID:	ELV064819515

Internformat


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700	1		\|a Ho, Tatiane Lima \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.ufug.2023.127844 doi (DE-627)ELV064819515 (ELSEVIER)S1618-8667(23)00015-8 DE-627 ger DE-627 rda eng 630 640 570 VZ BIODIV DE-30 fid Maria, Tamara Ribeiro Botelho de Carvalho verfasserin aut Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices. Decision tree algorithm Tree risk management Urban forests Urban trees Biondi, Daniela verfasserin aut Behling, Alexandre verfasserin aut Reis, Allan Rodrigo Nunho dos verfasserin aut Zamproni, Kendra verfasserin aut Ho, Tatiane Lima verfasserin aut Enthalten in Urban forestry & urban greening Jena : Urban & Fischer, 2002 81 Online-Ressource (DE-627)354193562 (DE-600)2088186-1 (DE-576)259485969 1610-8167 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA SSG-OPC-FOR 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
spelling	10.1016/j.ufug.2023.127844 doi (DE-627)ELV064819515 (ELSEVIER)S1618-8667(23)00015-8 DE-627 ger DE-627 rda eng 630 640 570 VZ BIODIV DE-30 fid Maria, Tamara Ribeiro Botelho de Carvalho verfasserin aut Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices. Decision tree algorithm Tree risk management Urban forests Urban trees Biondi, Daniela verfasserin aut Behling, Alexandre verfasserin aut Reis, Allan Rodrigo Nunho dos verfasserin aut Zamproni, Kendra verfasserin aut Ho, Tatiane Lima verfasserin aut Enthalten in Urban forestry & urban greening Jena : Urban & Fischer, 2002 81 Online-Ressource (DE-627)354193562 (DE-600)2088186-1 (DE-576)259485969 1610-8167 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA SSG-OPC-FOR 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
allfields_unstemmed	10.1016/j.ufug.2023.127844 doi (DE-627)ELV064819515 (ELSEVIER)S1618-8667(23)00015-8 DE-627 ger DE-627 rda eng 630 640 570 VZ BIODIV DE-30 fid Maria, Tamara Ribeiro Botelho de Carvalho verfasserin aut Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices. Decision tree algorithm Tree risk management Urban forests Urban trees Biondi, Daniela verfasserin aut Behling, Alexandre verfasserin aut Reis, Allan Rodrigo Nunho dos verfasserin aut Zamproni, Kendra verfasserin aut Ho, Tatiane Lima verfasserin aut Enthalten in Urban forestry & urban greening Jena : Urban & Fischer, 2002 81 Online-Ressource (DE-627)354193562 (DE-600)2088186-1 (DE-576)259485969 1610-8167 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA SSG-OPC-FOR 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
allfieldsGer	10.1016/j.ufug.2023.127844 doi (DE-627)ELV064819515 (ELSEVIER)S1618-8667(23)00015-8 DE-627 ger DE-627 rda eng 630 640 570 VZ BIODIV DE-30 fid Maria, Tamara Ribeiro Botelho de Carvalho verfasserin aut Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices. Decision tree algorithm Tree risk management Urban forests Urban trees Biondi, Daniela verfasserin aut Behling, Alexandre verfasserin aut Reis, Allan Rodrigo Nunho dos verfasserin aut Zamproni, Kendra verfasserin aut Ho, Tatiane Lima verfasserin aut Enthalten in Urban forestry & urban greening Jena : Urban & Fischer, 2002 81 Online-Ressource (DE-627)354193562 (DE-600)2088186-1 (DE-576)259485969 1610-8167 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA SSG-OPC-FOR 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
allfieldsSound	10.1016/j.ufug.2023.127844 doi (DE-627)ELV064819515 (ELSEVIER)S1618-8667(23)00015-8 DE-627 ger DE-627 rda eng 630 640 570 VZ BIODIV DE-30 fid Maria, Tamara Ribeiro Botelho de Carvalho verfasserin aut Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices. Decision tree algorithm Tree risk management Urban forests Urban trees Biondi, Daniela verfasserin aut Behling, Alexandre verfasserin aut Reis, Allan Rodrigo Nunho dos verfasserin aut Zamproni, Kendra verfasserin aut Ho, Tatiane Lima verfasserin aut Enthalten in Urban forestry & urban greening Jena : Urban & Fischer, 2002 81 Online-Ressource (DE-627)354193562 (DE-600)2088186-1 (DE-576)259485969 1610-8167 nnns volume:81 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA SSG-OPC-FOR 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 81
language	English
source	Enthalten in Urban forestry & urban greening 81 volume:81
sourceStr	Enthalten in Urban forestry & urban greening 81 volume:81
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Decision tree algorithm Tree risk management Urban forests Urban trees
dewey-raw	630
isfreeaccess_bool	false
container_title	Urban forestry & urban greening
authorswithroles_txt_mv	Maria, Tamara Ribeiro Botelho de Carvalho @@aut@@ Biondi, Daniela @@aut@@ Behling, Alexandre @@aut@@ Reis, Allan Rodrigo Nunho dos @@aut@@ Zamproni, Kendra @@aut@@ Ho, Tatiane Lima @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	354193562
dewey-sort	3630
id	ELV064819515
language_de	englisch
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author	Maria, Tamara Ribeiro Botelho de Carvalho
spellingShingle	Maria, Tamara Ribeiro Botelho de Carvalho ddc 630 fid BIODIV misc Decision tree algorithm misc Tree risk management misc Urban forests misc Urban trees Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil
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topic_title	630 640 570 VZ BIODIV DE-30 fid Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil Decision tree algorithm Tree risk management Urban forests Urban trees
topic	ddc 630 fid BIODIV misc Decision tree algorithm misc Tree risk management misc Urban forests misc Urban trees
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title	Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil
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title_full	Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil
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author_browse	Maria, Tamara Ribeiro Botelho de Carvalho Biondi, Daniela Behling, Alexandre Reis, Allan Rodrigo Nunho dos Zamproni, Kendra Ho, Tatiane Lima
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author-letter	Maria, Tamara Ribeiro Botelho de Carvalho
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title_sort	application of artificial intelligence for tree risk assessment optimization in itanhaém – são paulo, brazil
title_auth	Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil
abstract	Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices.
abstractGer	Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices.
abstract_unstemmed	Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices.
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title_short	Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil
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author2	Biondi, Daniela Behling, Alexandre Reis, Allan Rodrigo Nunho dos Zamproni, Kendra Ho, Tatiane Lima
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up_date	2024-07-06T20:51:58.450Z
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Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo, Brazil

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