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

Yaseen, Zaher and Ehteram, Mohammad and Hossain, Md. and Fai, Chow and Koting, Suhana and Mohd, Nuruol Syuhadaa and Jaafar, Wan Zurina Wan and Afan, Haitham Abdulmohsin and Lai, Sai Hin and Zaini, Nuratiah and Ahmed, Ali and El-Shafie, Ahmed (2019) A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems. Sustainability, 11 (7). p. 1953. ISSN 2071-1050, DOI https://doi.org/10.3390/su11071953.

Full text not available from this repository.
Official URL: https://doi.org/10.3390/su11071953


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. © 2019 by the authors.

Item Type: Article
Funders: Ministry of Education (MOE) research grant Fundamental Research Grant Scheme (FRGS) 20180120FRGS, University of Malaya Research Grant (UMRG) coded RP025A-18SUS, University of Malaya, Malaysia, TNB Seeding Fund, Institute of Power Energy (IPE) and Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, Malaysia, through research grants coded U-TG-CR-18-03 and U-TG-RD-17-06
Uncontrolled Keywords: hybrid expert system; bat algorithm; particle swarm optimization algorithm; multi-purpose system; water resource management
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 27 Nov 2019 06:38
Last Modified: 27 Nov 2019 06:38
URI: http://eprints.um.edu.my/id/eprint/23118

Actions (login required)

View Item View Item