1. bookVolume 11 (2021): Issue 1 (May 2021)
Journal Details
License
Format
Journal
First Published
30 Jun 2015
Publication timeframe
2 times per year
Languages
English
access type Open Access

Optimization in Water Resources At Dry Weather Conditions Before and After the Dam Failure By Using Dummy Variable Regression Approach

Published Online: 26 May 2021
Page range: 61 - 68
Received: 14 Dec 2020
Accepted: 04 Jan 2021
Journal Details
License
Format
Journal
First Published
30 Jun 2015
Publication timeframe
2 times per year
Languages
English
Abstract

One of the direct economic consequences of dam failure (DF) is that water supply for irrigation is affected and incomes of the agriculture sector (AS) are reduced. The main purpose of this study is to apply a linear programming model (LPM), which, the objective function of the model was set to maximize the income function of the region AS with accessible water sources and function of crops production before and after the DF by using dummy variable (DV) regression models to optimize water supply for irrigation. The results obtained indicate that the consumption of surface water(SW) and groundwaters (GW), before the DF has not been optimized, as there are 15.5 % source loss in SW and 14.5 % in GW. After the DF, the allocation of SW in the best possible situation of access to SW sources is independent of the model input. It has a fixed value equivalent to 86 million cubic meters (MCM), which indicates a 116% decrease in comparison with the optimized value. Total accessible water sources are decreased by 36 % and using GW is 15 % more than an average long period time. A based on the finding from this research and its comparison with previous studies, this model is appropriate for water supply programming after DF and for dry weather Conditions.

Keywords

Alok, K.P., 2014. Measuring Energy Intensity and Elasticity in India, A DummyVariablea Approach for Unit LevelHouseh. Jindal Journal of Business Research, 3(1&2), pp.77-92. Search in Google Scholar

Ashofteh, P.S., Rajaee, T., Golfam, P., 2017. Assessment of Water Resources Development Projects under Conditions of Climate Change Using Efficiency Indexes (EIs). Water Resour Manage, 31, pp.3723-3744. Search in Google Scholar

Christer, N., Catherine, A., Reidy, M.D., 2005. Fragmentation and Flow Regulation of the World’s Large River Systems. Science, 308, pp.405-407.www.sciencemag.org (15 April 2005). Search in Google Scholar

Christina, T.,Susan, R., 2016. Planning and implementing small dam removals: lessons learned from dam removals across the eastern United States. Water Resource Managment, 2, pp.489–493. Search in Google Scholar

Doorenbos, J., KASSAN, A. H.,1979. Yield response to water. Roma, FAO. 193p. Search in Google Scholar

English, M.J., NUSS, G.S., 1982. Designing for deficit irrigation.Journal of the Irrigation and Drainage Engineering, 08(02), pp91-106. Search in Google Scholar

English, M.J., NUSS, G.S., 1982. Designing for deficit irrigation.Journal of the Irrigation and Drainage Engineering, 08(02), pp91-106. Search in Google Scholar

Frizzone, R.D., Coelho, D., Dourado, N., 1997. Linear Programming Model to Optimize the Water Resource Use in. Irrigation Projects:an Application to the Senator Nilo Coelho. Sci agric Piracicaba, 54, pp.136-148. Search in Google Scholar

Ghahraman, B., Sepaskhah, A.R., 2004. Linear and Non linear optimization model for allocation of limited water supply.Irrig Darin Jornal, 53(3), pp39-34. Search in Google Scholar

Hargreaves, G.H., Samani, Z.A., 1984. Economics considerations on deficit irrigation.Journal of the Irrigation and Drainage Engineering,10(4), pp.343-258. Search in Google Scholar

Hasan, S., Mohammad, F.A., 2015. Linear Programming Model to Optimize Water Supply and Cropping Area for Irrigation A Case Study for Kalihati.Global Journal of Researches Engineering: G Industrial Engineering, 15 (2), PP.19-24. Search in Google Scholar

Hassani, N.,Yadollahi, P., Mortazavi, A., 2017. Farmers’ Perception of the Seriousness of the Declining Groundwater Volume and their Reactions to Mitigation of its undesirable Outcomes. Journal Management System, 10 (Issue 34), Page 1-10. Search in Google Scholar

HSPD., 2011. Estimating Economic Consequences for Dam Failure Scenarios. Homeland Security Presidential Directive (HSPD), USA, pp.1-45. Search in Google Scholar

Huai, Z.u., Jiang, H., Zhi, P.W., 2013. Optimizationof reinforcement strategies for dangerous dams considering time-averaged system failure probability and benefit–cost ratio using a life quality index. Natural Hazards, 65, PP.799–817. Search in Google Scholar

Jonkman, S.N., Vrijling, J.K.,Vrouwenvelder, A., 2008. Methods for the estimation of loss of life due to floods: a literature review and a proposal for a new method. Natural Hazards, 3, PP.353–389. Search in Google Scholar

Kassahun, B.,Tena, A., Megersa, O., Dinka, S., 2014. Optimizing Reservoir Operation Policy Using Chance Constraint Nonlinear Programming for Koga Irrigation Dam, Ethiopia. Water Resour. Manag, 28, PP.4957–4961. Search in Google Scholar

Kumar, D.N., Raju, K.S., Ashok, B., 2012. Optimalreservoir operation for irrigation of multiple crops using genetic algorithms. ASCE J Irrig Drain Eng, 132(2), pp.123-129. Search in Google Scholar

Michael, L. D., Gary, H.M., 1992. Predicting Loss of Life in Cases of Dam Failure and Flash flood. RiskAnalysk, 13(2),PP193-204. Search in Google Scholar

Palacios,V. H., 1976. Strategies to improve water management in Mexican irrigation districts: A case study in Sonora. Tucson, 197p. Thesis (Ph.D.) the University of Arizona,USA. Search in Google Scholar

Rao, S.S., 1984. Optimization Theory and Application Second edition. John Wiley and Sons, IEEE,PP1247-1248. Search in Google Scholar

The American Statistician, 2012. Use of Dummy Variables in Testing for Equality Between Sets of Coefficients in linear Regressions: A Generalization. The American Statistician City University of New York, New York, USA. Regressions: A Generalization. The American Statistician City University of New York, New York, USA. Search in Google Scholar

Yavary, G.R., Sashvary, N., 2005. Determining surface and subsurface water productivity in the agriculture sector of Iran. Agriculture Economy &development, 76, pp.167-181. Search in Google Scholar

U.S.Water Resources Council, 2012. Economic and Environmental Principles and Guidelines for Water and Related Land Resources Implementation Studies, U.S. Government Printing Office, Washington, USA. Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo