Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model

Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall−runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate c...
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Autor*in:	Mohammad Ehteram [verfasserIn] Amr H. El-Shafie [verfasserIn] Lai Sai Hin [verfasserIn] Faridah Othman [verfasserIn] Suhana Koting [verfasserIn] Hojat Karami [verfasserIn] Sayed-Farhad Mousavi [verfasserIn] Saeed Farzin [verfasserIn] Ali Najah Ahmed [verfasserIn] Mohd Hafiz Bin Zawawi [verfasserIn] Md Shabbir Hossain [verfasserIn] Nuruol Syuhadaa Mohd [verfasserIn] Haitham Abdulmohsin Afan [verfasserIn] Ahmed El-Shafie [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	water resource management shark algorithm IHACRES model reservoir operation

Übergeordnetes Werk:	In: Applied Sciences - MDPI AG, 2012, 9(2019), 19, p 3960
Übergeordnetes Werk:	volume:9 ; year:2019 ; number:19, p 3960

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/app9193960

Katalog-ID:	DOAJ014251795

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spelling	10.3390/app9193960 doi (DE-627)DOAJ014251795 (DE-599)DOAJ8f0e29b01b804285a5a7ab0bce5e8ad4 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Mohammad Ehteram verfasserin aut Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall−runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A<sub<1</sub<B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam. water resource management shark algorithm IHACRES model reservoir operation Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Amr H. El-Shafie verfasserin aut Lai Sai Hin verfasserin aut Faridah Othman verfasserin aut Suhana Koting verfasserin aut Hojat Karami verfasserin aut Sayed-Farhad Mousavi verfasserin aut Saeed Farzin verfasserin aut Ali Najah Ahmed verfasserin aut Mohd Hafiz Bin Zawawi verfasserin aut Md Shabbir Hossain verfasserin aut Nuruol Syuhadaa Mohd verfasserin aut Haitham Abdulmohsin Afan verfasserin aut Ahmed El-Shafie verfasserin aut In Applied Sciences MDPI AG, 2012 9(2019), 19, p 3960 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:9 year:2019 number:19, p 3960 https://doi.org/10.3390/app9193960 kostenfrei https://doaj.org/article/8f0e29b01b804285a5a7ab0bce5e8ad4 kostenfrei https://www.mdpi.com/2076-3417/9/19/3960 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 19, p 3960
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allfieldsSound	10.3390/app9193960 doi (DE-627)DOAJ014251795 (DE-599)DOAJ8f0e29b01b804285a5a7ab0bce5e8ad4 DE-627 ger DE-627 rakwb eng TA1-2040 QH301-705.5 QC1-999 QD1-999 Mohammad Ehteram verfasserin aut Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall−runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A<sub<1</sub<B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam. water resource management shark algorithm IHACRES model reservoir operation Technology T Engineering (General). Civil engineering (General) Biology (General) Physics Chemistry Amr H. El-Shafie verfasserin aut Lai Sai Hin verfasserin aut Faridah Othman verfasserin aut Suhana Koting verfasserin aut Hojat Karami verfasserin aut Sayed-Farhad Mousavi verfasserin aut Saeed Farzin verfasserin aut Ali Najah Ahmed verfasserin aut Mohd Hafiz Bin Zawawi verfasserin aut Md Shabbir Hossain verfasserin aut Nuruol Syuhadaa Mohd verfasserin aut Haitham Abdulmohsin Afan verfasserin aut Ahmed El-Shafie verfasserin aut In Applied Sciences MDPI AG, 2012 9(2019), 19, p 3960 (DE-627)737287640 (DE-600)2704225-X 20763417 nnns volume:9 year:2019 number:19, p 3960 https://doi.org/10.3390/app9193960 kostenfrei https://doaj.org/article/8f0e29b01b804285a5a7ab0bce5e8ad4 kostenfrei https://www.mdpi.com/2076-3417/9/19/3960 kostenfrei https://doaj.org/toc/2076-3417 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2019 19, p 3960
language	English
source	In Applied Sciences 9(2019), 19, p 3960 volume:9 year:2019 number:19, p 3960
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authorswithroles_txt_mv	Mohammad Ehteram @@aut@@ Amr H. El-Shafie @@aut@@ Lai Sai Hin @@aut@@ Faridah Othman @@aut@@ Suhana Koting @@aut@@ Hojat Karami @@aut@@ Sayed-Farhad Mousavi @@aut@@ Saeed Farzin @@aut@@ Ali Najah Ahmed @@aut@@ Mohd Hafiz Bin Zawawi @@aut@@ Md Shabbir Hossain @@aut@@ Nuruol Syuhadaa Mohd @@aut@@ Haitham Abdulmohsin Afan @@aut@@ Ahmed El-Shafie @@aut@@
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spellingShingle	Mohammad Ehteram misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc water resource management misc shark algorithm misc IHACRES model misc reservoir operation misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model
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topic_title	TA1-2040 QH301-705.5 QC1-999 QD1-999 Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model water resource management shark algorithm IHACRES model reservoir operation
topic	misc TA1-2040 misc QH301-705.5 misc QC1-999 misc QD1-999 misc water resource management misc shark algorithm misc IHACRES model misc reservoir operation misc Technology misc T misc Engineering (General). Civil engineering (General) misc Biology (General) misc Physics misc Chemistry
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title_sort	toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model
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title_auth	Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model
abstract	Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall−runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A<sub<1</sub<B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.
abstractGer	Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall−runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A<sub<1</sub<B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.
abstract_unstemmed	Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall−runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A<sub<1</sub<B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam.
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container_issue	19, p 3960
title_short	Toward Bridging Future Irrigation Deficits Utilizing the Shark Algorithm Integrated with a Climate Change Model
url	https://doi.org/10.3390/app9193960 https://doaj.org/article/8f0e29b01b804285a5a7ab0bce5e8ad4 https://www.mdpi.com/2076-3417/9/19/3960 https://doaj.org/toc/2076-3417
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author2	Amr H. El-Shafie Lai Sai Hin Faridah Othman Suhana Koting Hojat Karami Sayed-Farhad Mousavi Saeed Farzin Ali Najah Ahmed Mohd Hafiz Bin Zawawi Md Shabbir Hossain Nuruol Syuhadaa Mohd Haitham Abdulmohsin Afan Ahmed El-Shafie
author2Str	Amr H. El-Shafie Lai Sai Hin Faridah Othman Suhana Koting Hojat Karami Sayed-Farhad Mousavi Saeed Farzin Ali Najah Ahmed Mohd Hafiz Bin Zawawi Md Shabbir Hossain Nuruol Syuhadaa Mohd Haitham Abdulmohsin Afan Ahmed El-Shafie
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doi_str	10.3390/app9193960
callnumber-a	TA1-2040
up_date	2024-07-03T22:05:08.559Z
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