Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches
Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various cli...
Ausführliche Beschreibung
Autor*in: |
Arefinia, Ali [verfasserIn] |
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Englisch |
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2021 |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
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Übergeordnetes Werk: |
Enthalten in: Environment, development and sustainability - Springer Netherlands, 1999, 24(2021), 6 vom: 01. Sept., Seite 8378-8396 |
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Übergeordnetes Werk: |
volume:24 ; year:2021 ; number:6 ; day:01 ; month:09 ; pages:8378-8396 |
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DOI / URN: |
10.1007/s10668-021-01788-0 |
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OLC2078633585 |
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520 | |a Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. | ||
650 | 4 | |a Virtual water content | |
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10.1007/s10668-021-01788-0 doi (DE-627)OLC2078633585 (DE-He213)s10668-021-01788-0-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Arefinia, Ali verfasserin aut Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. Virtual water content Crop yield CanESM2 SVM GP ANN Bozorg-Haddad, Omid (orcid)0000-0001-6607-9581 aut Ahmadaali, Khaled aut Bazrafshan, Javad aut Zolghadr-Asli, Babak (orcid)0000-0002-3392-2672 aut Chu, Xuefeng aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)OLC2078633585 (DE-He213)s10668-021-01788-0-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Arefinia, Ali verfasserin aut Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. Virtual water content Crop yield CanESM2 SVM GP ANN Bozorg-Haddad, Omid (orcid)0000-0001-6607-9581 aut Ahmadaali, Khaled aut Bazrafshan, Javad aut Zolghadr-Asli, Babak (orcid)0000-0002-3392-2672 aut Chu, Xuefeng aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)OLC2078633585 (DE-He213)s10668-021-01788-0-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Arefinia, Ali verfasserin aut Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. Virtual water content Crop yield CanESM2 SVM GP ANN Bozorg-Haddad, Omid (orcid)0000-0001-6607-9581 aut Ahmadaali, Khaled aut Bazrafshan, Javad aut Zolghadr-Asli, Babak (orcid)0000-0002-3392-2672 aut Chu, Xuefeng aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)OLC2078633585 (DE-He213)s10668-021-01788-0-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Arefinia, Ali verfasserin aut Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. Virtual water content Crop yield CanESM2 SVM GP ANN Bozorg-Haddad, Omid (orcid)0000-0001-6607-9581 aut Ahmadaali, Khaled aut Bazrafshan, Javad aut Zolghadr-Asli, Babak (orcid)0000-0002-3392-2672 aut Chu, Xuefeng aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)OLC2078633585 (DE-He213)s10668-021-01788-0-p DE-627 ger DE-627 rakwb eng 333.7 VZ 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 74.60$jRaumordnung$jStädtebau: Allgemeines bkl 83.46$jEntwicklungsökonomie bkl Arefinia, Ali verfasserin aut Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. Virtual water content Crop yield CanESM2 SVM GP ANN Bozorg-Haddad, Omid (orcid)0000-0001-6607-9581 aut Ahmadaali, Khaled aut Bazrafshan, Javad aut Zolghadr-Asli, Babak (orcid)0000-0002-3392-2672 aut Chu, Xuefeng aut Enthalten in Environment, development and sustainability Springer Netherlands, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)247370592 (DE-600)1438730-X (DE-576)27365103X 1387-585X nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GGO SSG-OLC-WIW SSG-OLC-FOR 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 74.60$jRaumordnung$jStädtebau: Allgemeines VZ 106413708 (DE-625)106413708 83.46$jEntwicklungsökonomie VZ 106414925 (DE-625)106414925 AR 24 2021 6 01 09 8378-8396 |
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Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches |
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Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
abstractGer |
Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
abstract_unstemmed |
Abstract One of the most crucial issues in water and food security is to assess the impacts of climate change on virtual water content (VWC) and crop yield of agricultural products. The objective of this study is to efficiently predict the VWC patterns and yields of different crops under various climate change conditions using three data mining approaches including artificial neural network (ANN), genetic programming (GP), and support vector machine (SVM). The study region included the eastern provinces of Iran containing North Khorasan, Khorasan Razavi, South Khorasan, Sistan-Baluchestan (in a range of latitude from 25 to 40°N). Specifically, VWC and crop yields were estimated for both baseline period (1985–2005) and several climate change conditions including four time horizons (2030, 2050, 2070, and 2090) under RCPs 2.6, 4.5, and 8.5 based on the second generation Canadian Earth System Model (CanESM2). The data mining models were evaluated with the RMSE and NSE goodness-of-fit criteria. The results showed that the SVM model achieved the highest NSE and lowest RMSE values. It was also found that under the climate change conditions, VWC increased from 6 to 42%, while crop yield decreased from 8 to 53% for all products in the southern regions. An opposite trend was observed in the northern regions for wheat and barley with an increase from 12 to 72% for VWC and from 4 to 27% for the yield. © The Author(s), under exclusive licence to Springer Nature B.V. 2021 |
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