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...
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| Autor*in: |
Arefinia, Ali [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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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 - [S.l.] : Proquest, 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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SPR046969632 |
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| 245 | 1 | 0 | |a Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches |
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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. | ||
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| 650 | 4 | |a SVM |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a GP |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Chu, Xuefeng |4 aut | |
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10.1007/s10668-021-01788-0 doi (DE-627)SPR046969632 (SPR)s10668-021-01788-0-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Crop yield (dpeaa)DE-He213 CanESM2 (dpeaa)DE-He213 SVM (dpeaa)DE-He213 GP (dpeaa)DE-He213 ANN (dpeaa)DE-He213 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 [S.l.] : Proquest, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)320526984 (DE-600)2015291-7 1573-2975 nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://dx.doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)SPR046969632 (SPR)s10668-021-01788-0-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Crop yield (dpeaa)DE-He213 CanESM2 (dpeaa)DE-He213 SVM (dpeaa)DE-He213 GP (dpeaa)DE-He213 ANN (dpeaa)DE-He213 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 [S.l.] : Proquest, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)320526984 (DE-600)2015291-7 1573-2975 nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://dx.doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)SPR046969632 (SPR)s10668-021-01788-0-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Crop yield (dpeaa)DE-He213 CanESM2 (dpeaa)DE-He213 SVM (dpeaa)DE-He213 GP (dpeaa)DE-He213 ANN (dpeaa)DE-He213 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 [S.l.] : Proquest, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)320526984 (DE-600)2015291-7 1573-2975 nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://dx.doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)SPR046969632 (SPR)s10668-021-01788-0-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Crop yield (dpeaa)DE-He213 CanESM2 (dpeaa)DE-He213 SVM (dpeaa)DE-He213 GP (dpeaa)DE-He213 ANN (dpeaa)DE-He213 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 [S.l.] : Proquest, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)320526984 (DE-600)2015291-7 1573-2975 nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://dx.doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 6 01 09 8378-8396 |
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10.1007/s10668-021-01788-0 doi (DE-627)SPR046969632 (SPR)s10668-021-01788-0-e DE-627 ger DE-627 rakwb eng 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 Computermedien c rdamedia Online-Ressource cr 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 (dpeaa)DE-He213 Crop yield (dpeaa)DE-He213 CanESM2 (dpeaa)DE-He213 SVM (dpeaa)DE-He213 GP (dpeaa)DE-He213 ANN (dpeaa)DE-He213 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 [S.l.] : Proquest, 1999 24(2021), 6 vom: 01. Sept., Seite 8378-8396 (DE-627)320526984 (DE-600)2015291-7 1573-2975 nnns volume:24 year:2021 number:6 day:01 month:09 pages:8378-8396 https://dx.doi.org/10.1007/s10668-021-01788-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 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_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 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_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 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_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 24 2021 6 01 09 8378-8396 |
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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. 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Arefinia, Ali |
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Arefinia, Ali misc Virtual water content misc Crop yield misc CanESM2 misc SVM misc GP misc ANN Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches |
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Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches Virtual water content (dpeaa)DE-He213 Crop yield (dpeaa)DE-He213 CanESM2 (dpeaa)DE-He213 SVM (dpeaa)DE-He213 GP (dpeaa)DE-He213 ANN (dpeaa)DE-He213 |
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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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Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches |
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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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Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches |
| abstract |
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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Estimation of geographical variations in virtual water content and crop yield under climate change: comparison of three data mining approaches |
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Bozorg-Haddad, Omid Ahmadaali, Khaled Bazrafshan, Javad Zolghadr-Asli, Babak Chu, Xuefeng |
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| score |
7.399453 |