Conditional Value at Risk-Based Model for Planning Agricultural Water and Return Flow Allocation in River Systems
In this study, a new methodology is presented for simultaneous agricultural water and return flow (waste load) allocation in rivers. In this methodology, an objective function based on Conditional Value at Risk (CVaR) and a Nonlinear Interval Number Programming (NINP) technique are utilized. The CVa...
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| Veröffentlicht in: | Water resources management 2016-01, Vol.30 (1), p.427-443 |
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| creator | Soltani, Maryam Kerachian, Reza Nikoo, Mohammad Reza Noory, Hamideh |
| description | In this study, a new methodology is presented for simultaneous agricultural water and return flow (waste load) allocation in rivers. In this methodology, an objective function based on Conditional Value at Risk (CVaR) and a Nonlinear Interval Number Programming (NINP) technique are utilized. The CVaR can handle uncertainties in the form of probability distributions, while NINP incorporates uncertain inputs which are only available as intervals. This CVaR-NINP framework is used for agricultural water and return flow allocation planning under uncertainty. In this paper, to reduce the amount of saline return flow discharged into the river, a part of return flow of each agricultural network is diverted to an evaporation pond. Some meta-models based on Artificial Neural Network (ANN) are trained and validated using the results of Soil, Water, Atmosphere and Plant (SWAP) simulation model to reliably approximate the quantity and Total Dissolved Solids (TDS) load of agricultural return flows in a critical 7-day period. The effectiveness of the proposed methodology is examined through applying it to a part of Karkheh River catchment in the southwestern part of Iran. The results confirm the applicability of the model in incorporating the main uncertainties and generating water and waste load allocation policies in the form of interval numbers. |
| doi_str_mv | 10.1007/s11269-015-1170-0 |
| format | Article |
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In this methodology, an objective function based on Conditional Value at Risk (CVaR) and a Nonlinear Interval Number Programming (NINP) technique are utilized. The CVaR can handle uncertainties in the form of probability distributions, while NINP incorporates uncertain inputs which are only available as intervals. This CVaR-NINP framework is used for agricultural water and return flow allocation planning under uncertainty. In this paper, to reduce the amount of saline return flow discharged into the river, a part of return flow of each agricultural network is diverted to an evaporation pond. Some meta-models based on Artificial Neural Network (ANN) are trained and validated using the results of Soil, Water, Atmosphere and Plant (SWAP) simulation model to reliably approximate the quantity and Total Dissolved Solids (TDS) load of agricultural return flows in a critical 7-day period. The effectiveness of the proposed methodology is examined through applying it to a part of Karkheh River catchment in the southwestern part of Iran. The results confirm the applicability of the model in incorporating the main uncertainties and generating water and waste load allocation policies in the form of interval numbers.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-015-1170-0</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Agricultural pollution ; Agricultural production ; Allocations ; Atmospheric Sciences ; Civil Engineering ; drainage water ; Earth and Environmental Science ; Earth Sciences ; Engineering schools ; Environment ; Evaporation ; Freshwater ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrology/Water Resources ; Intervals ; Irrigation ; issues and policy ; Learning theory ; Linear programming ; Load ; Methodology ; Methods ; Neural networks ; Nonlinear programming ; Objective function ; Optimization ; planning ; probability distribution ; Return flow ; risk ; River catchments ; River systems ; Rivers ; Salinity ; Simulation ; simulation models ; soil ; Stormwater management ; Studies ; Total dissolved solids ; Uncertainty ; Waste load ; Wastes ; wastewater ; Water quality ; Water resources management ; Watersheds</subject><ispartof>Water resources management, 2016-01, Vol.30 (1), p.427-443</ispartof><rights>Springer Science+Business Media Dordrecht 2015</rights><rights>Springer Science+Business Media Dordrecht 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c476t-a597120b4207a0d95f1306c04d41f7d42bfec830208f8b582a1afbace349d593</citedby><cites>FETCH-LOGICAL-c476t-a597120b4207a0d95f1306c04d41f7d42bfec830208f8b582a1afbace349d593</cites><orcidid>0000-0002-6284-1062</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-015-1170-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-015-1170-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids></links><search><creatorcontrib>Soltani, Maryam</creatorcontrib><creatorcontrib>Kerachian, Reza</creatorcontrib><creatorcontrib>Nikoo, Mohammad Reza</creatorcontrib><creatorcontrib>Noory, Hamideh</creatorcontrib><title>Conditional Value at Risk-Based Model for Planning Agricultural Water and Return Flow Allocation in River Systems</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>In this study, a new methodology is presented for simultaneous agricultural water and return flow (waste load) allocation in rivers. In this methodology, an objective function based on Conditional Value at Risk (CVaR) and a Nonlinear Interval Number Programming (NINP) technique are utilized. The CVaR can handle uncertainties in the form of probability distributions, while NINP incorporates uncertain inputs which are only available as intervals. This CVaR-NINP framework is used for agricultural water and return flow allocation planning under uncertainty. In this paper, to reduce the amount of saline return flow discharged into the river, a part of return flow of each agricultural network is diverted to an evaporation pond. Some meta-models based on Artificial Neural Network (ANN) are trained and validated using the results of Soil, Water, Atmosphere and Plant (SWAP) simulation model to reliably approximate the quantity and Total Dissolved Solids (TDS) load of agricultural return flows in a critical 7-day period. The effectiveness of the proposed methodology is examined through applying it to a part of Karkheh River catchment in the southwestern part of Iran. The results confirm the applicability of the model in incorporating the main uncertainties and generating water and waste load allocation policies in the form of interval numbers.</description><subject>Agricultural pollution</subject><subject>Agricultural production</subject><subject>Allocations</subject><subject>Atmospheric Sciences</subject><subject>Civil Engineering</subject><subject>drainage water</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Engineering schools</subject><subject>Environment</subject><subject>Evaporation</subject><subject>Freshwater</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>Hydrogeology</subject><subject>Hydrology/Water Resources</subject><subject>Intervals</subject><subject>Irrigation</subject><subject>issues and policy</subject><subject>Learning theory</subject><subject>Linear programming</subject><subject>Load</subject><subject>Methodology</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Nonlinear programming</subject><subject>Objective function</subject><subject>Optimization</subject><subject>planning</subject><subject>probability distribution</subject><subject>Return flow</subject><subject>risk</subject><subject>River catchments</subject><subject>River systems</subject><subject>Rivers</subject><subject>Salinity</subject><subject>Simulation</subject><subject>simulation models</subject><subject>soil</subject><subject>Stormwater management</subject><subject>Studies</subject><subject>Total dissolved solids</subject><subject>Uncertainty</subject><subject>Waste load</subject><subject>Wastes</subject><subject>wastewater</subject><subject>Water quality</subject><subject>Water resources 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Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><jtitle>Water resources management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Soltani, Maryam</au><au>Kerachian, Reza</au><au>Nikoo, Mohammad Reza</au><au>Noory, Hamideh</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Conditional Value at Risk-Based Model for Planning Agricultural Water and Return Flow Allocation in River Systems</atitle><jtitle>Water resources management</jtitle><stitle>Water Resour Manage</stitle><date>2016-01-01</date><risdate>2016</risdate><volume>30</volume><issue>1</issue><spage>427</spage><epage>443</epage><pages>427-443</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><abstract>In this study, a new methodology is presented for simultaneous agricultural water and return flow (waste load) allocation in rivers. In this methodology, an objective function based on Conditional Value at Risk (CVaR) and a Nonlinear Interval Number Programming (NINP) technique are utilized. The CVaR can handle uncertainties in the form of probability distributions, while NINP incorporates uncertain inputs which are only available as intervals. This CVaR-NINP framework is used for agricultural water and return flow allocation planning under uncertainty. In this paper, to reduce the amount of saline return flow discharged into the river, a part of return flow of each agricultural network is diverted to an evaporation pond. Some meta-models based on Artificial Neural Network (ANN) are trained and validated using the results of Soil, Water, Atmosphere and Plant (SWAP) simulation model to reliably approximate the quantity and Total Dissolved Solids (TDS) load of agricultural return flows in a critical 7-day period. The effectiveness of the proposed methodology is examined through applying it to a part of Karkheh River catchment in the southwestern part of Iran. The results confirm the applicability of the model in incorporating the main uncertainties and generating water and waste load allocation policies in the form of interval numbers.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-015-1170-0</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0002-6284-1062</orcidid></addata></record> |
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| subjects | Agricultural pollution Agricultural production Allocations Atmospheric Sciences Civil Engineering drainage water Earth and Environmental Science Earth Sciences Engineering schools Environment Evaporation Freshwater Geotechnical Engineering & Applied Earth Sciences Hydrogeology Hydrology/Water Resources Intervals Irrigation issues and policy Learning theory Linear programming Load Methodology Methods Neural networks Nonlinear programming Objective function Optimization planning probability distribution Return flow risk River catchments River systems Rivers Salinity Simulation simulation models soil Stormwater management Studies Total dissolved solids Uncertainty Waste load Wastes wastewater Water quality Water resources management Watersheds |
| title | Conditional Value at Risk-Based Model for Planning Agricultural Water and Return Flow Allocation in River Systems |
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