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Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms

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  • Hamid Safavi
  • Mahdieh Esmikhani

Abstract

Combined simulation-optimization models have been widely used to address the management of water resources issues. This paper presents a simulation-optimization model for conjunctive use of surface water and groundwater at a basin-wide scale, the Zayandehrood river basin in west central Iran. In the Zayandehrood basin, in the past 10 years, a historical low rainfall in the head of the basin, combined with growing demand for water, has triggered great changes in water management at basin and irrigation system level. The conjunctive use model that coupled numerical simulation with nonlinear optimization is used to minimize shortages of water in meeting irrigation demands for four irrigation systems. Constraints guarantee the maximum/minimum cumulative groundwater drawdown and maximum capacity of irrigation systems. A support vector machines (SVMs) model is developed as a simulator of surface water and groundwater interaction model while a genetic algorithm (GA) is used as the optimization model. Conjunctive use model runs for three scenarios. Results show that the accuracy of SVMs as a simulator for surface water and groundwater interaction model is good and that it is possible to decrease the water shortage for irrigation systems with application of proposed SVMs-GA model. Copyright Springer Science+Business Media Dordrecht 2013

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  • Hamid Safavi & Mahdieh Esmikhani, 2013. "Conjunctive Use of Surface Water and Groundwater: Application of Support Vector Machines (SVMs) and Genetic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 2623-2644, May.
    • Handle: RePEc:spr:waterr:v:27:y:2013:i:7:p:2623-2644
    DOI: 10.1007/s11269-013-0307-2
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    5. Saeed Mozaffari & Saman Javadi & Hamid Kardan Moghaddam & Timothy O. Randhir, 2022. "Forecasting Groundwater Levels using a Hybrid of Support Vector Regression and Particle Swarm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 1955-1972, April.
    6. Mehrabi, Ahmad & Heidarpour, Manouchehr & Safavi, Hamid R. & Rezaei, Farshad, 2021. "Assessment of the optimized scenarios for economic-environmental conjunctive water use utilizing gravitational search algorithm," Agricultural Water Management, Elsevier, vol. 246(C).
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    8. Zhang, Chenglong & Engel, Bernard A. & Guo, Ping, 2018. "An Interval-based Fuzzy Chance-constrained Irrigation Water Allocation model with double-sided fuzziness," Agricultural Water Management, Elsevier, vol. 210(C), pages 22-31.
    9. Jiyang Tian & Chuanzhe Li & Jia Liu & Fuliang Yu & Shuanghu Cheng & Nana Zhao & Wan Zurina Wan Jaafar, 2016. "Groundwater Depth Prediction Using Data-Driven Models with the Assistance of Gamma Test," Sustainability, MDPI, vol. 8(11), pages 1-17, October.
    10. T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
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    12. Elahe Fallah-Mehdipour & Omid Bozorg Haddad & Saeed Alimohammadi & Hugo Loáiciga, 2015. "Development of Real-Time Conjunctive Use Operation Rules for Aquifer-Reservoir Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1887-1906, April.
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    14. Qian Zhang & Xiujuan Liang & Zhang Fang & Tao Jiang & Yubo Wang & Lei Wang, 2016. "Urban water resources allocation and shortage risk mapping with support vector machine method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 1209-1228, March.
    15. Hsi-Ting Fang & Bing-Chen Jhong & Yih-Chi Tan & Kai-Yuan Ke & Mo-Hsiung Chuang, 2019. "A Two-Stage Approach Integrating SOM- and MOGA-SVM-Based Algorithms to Forecast Spatial-temporal Groundwater Level with Meteorological Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 797-818, January.
    16. Wang, Rong & Huang, Guanhua & Xu, Xu & Ren, Dongyang & Gou, Jiachao & Wu, Zhangsheng, 2022. "Significant differences in agro-hydrological processes and water productivity between canal- and well-irrigated areas in an arid region," Agricultural Water Management, Elsevier, vol. 267(C).
    17. García Nieto, P.J. & García-Gonzalo, E. & Sánchez Lasheras, F. & de Cos Juez, F.J., 2015. "Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability," Reliability Engineering and System Safety, Elsevier, vol. 138(C), pages 219-231.
    18. Nieto, P.J. García & Fernández, J.R. Alonso & Suárez, V.M. González & Muñiz, C. Díaz & García-Gonzalo, E. & Bayón, R. Mayo, 2015. "A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: A case study in Northern Spain," Applied Mathematics and Computation, Elsevier, vol. 260(C), pages 170-187.
    19. Zahra Kayhomayoon & Sami Ghordoyee Milan & Naser Arya Azar & Pete Bettinger & Faezeh Babaian & Abolfazl Jaafari, 2022. "A Simulation-Optimization Modeling Approach for Conjunctive Water Use Management in a Semi-Arid Region of Iran," Sustainability, MDPI, vol. 14(5), pages 1-20, February.
    20. Qian Zhang & Xiujuan Liang & Zhang Fang & Tao Jiang & Yubo Wang & Lei Wang, 2016. "Urban water resources allocation and shortage risk mapping with support vector machine method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(2), pages 1209-1228, March.
    21. Akram Rahbar & Ali Mirarabi & Mohammad Nakhaei & Mahdi Talkhabi & Maryam Jamali, 2022. "A Comparative Analysis of Data-Driven Models (SVR, ANFIS, and ANNs) for Daily Karst Spring Discharge Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(2), pages 589-609, January.
    22. Shu Chen & Dongguo Shao & Xudong Li & Caixiu Lei, 2016. "Simulation-Optimization Modeling of Conjunctive Operation of Reservoirs and Ponds for Irrigation of Multiple Crops Using an Improved Artificial Bee Colony Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(9), pages 2887-2905, July.
    23. Singh, Ajay, 2014. "Simulation–optimization modeling for conjunctive water use management," Agricultural Water Management, Elsevier, vol. 141(C), pages 23-29.
    24. Safavi, Hamid R. & Enteshari, Sajad, 2016. "Conjunctive use of surface and ground water resources using the ant system optimization," Agricultural Water Management, Elsevier, vol. 173(C), pages 23-34.

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