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A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems

1
Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam
2
Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran
3
School of Energy, Geoscience, Infrastructure and Society, Department of Civil Engineering, Heriot-Watt University, Putrajaya 62200, Malaysia
4
Institute of Energy Infrastructure (IEI), Civil Engineering Department, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
5
Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
6
Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia
*
Author to whom correspondence should be addressed.
Sustainability 2019, 11(7), 1953; https://doi.org/10.3390/su11071953
Received: 25 December 2018 / Revised: 12 February 2019 / Accepted: 19 February 2019 / Published: 2 April 2019
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Abstract

Multi-purpose advanced systems are considered a complex problem in water resource management, and the use of data-intelligence methodologies in operating such systems provides major advantages for decision-makers. The current research is devoted to the implementation of hybrid novel meta-heuristic algorithms (e.g., the bat algorithm (BA) and particle swarm optimization (PSO) algorithm) to formulate multi-purpose systems for power production and irrigation supply. The proposed hybrid modelling method was applied for the multi-purpose reservoir system of Bhadra Dam, which is located in the state of Karnataka, India. The average monthly demand for irrigation is 142.14 (106 m3), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (106 m3). Compared with the shark algorithm (SA), BA, weed algorithm (WA), PSO algorithm, and genetic algorithm (GA), the NHA decreased the computation time by 28%, 36%, 39%, 82%, and 88%, respectively, which represents an excellent enhancement result. The amount of released water based on the proposed hybrid method attains a more reliable index for the volumetric percentage and provides a more effective operation rule for supplying the irrigation demand. Additionally, the average demand for power production is 18.90 (106 kwh), whereas the NHA produces 18.09 (106 kwh) of power. Power production utilizing the NHA’s operation rule achieved a sufficient magnitude relative to that of stand-alone models, such as the BA, PSO, WA, SA, and GA. The excellent proficiency of the developed intelligence expert system is the result of the hybrid structure of the BA and PSO algorithm and the substitution of weaker solutions in each algorithm with better solutions from other algorithms. The main advantage of the proposed NHA is its ability to increase the diversity of solutions and hence avoid the worst possible solutions obtained using BA, that is, preventing a decrease in local optima. In addition, the NHA enhances the convergence rate obtained using the PSO algorithm. Hence, the proposed NHA as an intelligence model could contribute to providing reliable solutions for complex multi-purpose reservoir systems to optimize the operation rule for similar reservoir systems worldwide. View Full-Text
Keywords: hybrid expert system; bat algorithm; particle swarm optimization algorithm; multi-purpose system; water resource management hybrid expert system; bat algorithm; particle swarm optimization algorithm; multi-purpose system; water resource management
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Yaseen, Z.M.; Ehteram, M.; Hossain, M.S.; Fai, C.M.; Binti Koting, S.; Mohd, N.S.; Binti Jaafar, W.Z.; Afan, H.A.; Hin, L.S.; Zaini, N.; Ahmed, A.N.; El-Shafie, A. A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems. Sustainability 2019, 11, 1953.

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