{"title":"Decision support for scenario analysis in a complex water resource project","authors":"Rimpa Jaiswal, A. K. Lohani, R. Galkate","doi":"10.1080/23249676.2020.1844604","DOIUrl":null,"url":null,"abstract":"Multi-criteria decision support (MCDS) was developed and demonstrated for a complex reservoir system for a comprehensive assessment of performance for a complex reservoir system. A management model for the Tandula complex system in India was developed in MIKE HYDRO Basin. This system has three interconnected reservoirs, three irrigation commands, and one industrial user. The results from simulations were analyzed in multi-criteria decision support using an economic and six performance indicators to get a comprehensive performance index for the system. The results of the analysis confirmed that the yearly deficit of the Tandula command maybe about 6.36 MCM with 88% reliability under the present efficiency of 51% and no groundwater use (SCN-1). The comprehensive performance index for scenario SCN-1 was 0.13 which can be increased to 0.86 by increasing efficiencies and consumptive use. Abbreviations: AHP: Analytical hierarchical process; ANN: Artificial Neural Network; BSP: Bhilai Steel Plant; CEA: Cost effectiveness analysis; DAPP: Dynamic adaptive policy pathways; DS: Decision scaling; GW: Groundwater; Ha: Hectare; MORDM: Many objective robust decision making; MOVA: Many-objective visual analytics; MCDA: Multi Criteria Decision Analysis; MCM: Million cubic meter; MCDA: Multi-criteria decision analysis; NB-DSS: Nile Basin Decision Support System; ROA: Real options analysis; RRV: Reliability, resilience, vulnerability; RDM: Robust decision making; WEAP: Water Evaluation and Planning; SCN: Scenario; VIDEO Visually interactive decision-making and design using evolutionary multi-objective optimization","PeriodicalId":51911,"journal":{"name":"Journal of Applied Water Engineering and Research","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/23249676.2020.1844604","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Applied Water Engineering and Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/23249676.2020.1844604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 1
Abstract
Multi-criteria decision support (MCDS) was developed and demonstrated for a complex reservoir system for a comprehensive assessment of performance for a complex reservoir system. A management model for the Tandula complex system in India was developed in MIKE HYDRO Basin. This system has three interconnected reservoirs, three irrigation commands, and one industrial user. The results from simulations were analyzed in multi-criteria decision support using an economic and six performance indicators to get a comprehensive performance index for the system. The results of the analysis confirmed that the yearly deficit of the Tandula command maybe about 6.36 MCM with 88% reliability under the present efficiency of 51% and no groundwater use (SCN-1). The comprehensive performance index for scenario SCN-1 was 0.13 which can be increased to 0.86 by increasing efficiencies and consumptive use. Abbreviations: AHP: Analytical hierarchical process; ANN: Artificial Neural Network; BSP: Bhilai Steel Plant; CEA: Cost effectiveness analysis; DAPP: Dynamic adaptive policy pathways; DS: Decision scaling; GW: Groundwater; Ha: Hectare; MORDM: Many objective robust decision making; MOVA: Many-objective visual analytics; MCDA: Multi Criteria Decision Analysis; MCM: Million cubic meter; MCDA: Multi-criteria decision analysis; NB-DSS: Nile Basin Decision Support System; ROA: Real options analysis; RRV: Reliability, resilience, vulnerability; RDM: Robust decision making; WEAP: Water Evaluation and Planning; SCN: Scenario; VIDEO Visually interactive decision-making and design using evolutionary multi-objective optimization
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