A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems
algorithm; artificial intelligence; comparative study; decision making; evolutionary theory; expert system; irrigation; management practice; methodology; numerical model; power generation; reservoir; water management; water resource; India; Karnataka; Chondrichthyes
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my.uniten.dspace-247252023-05-29T15:26:17Z A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems Yaseen Z.M. Ehteram M. Hossain M.S. Fai C.M. Koting S.B. Mohd N.S. Jaafar W.Z.B. Afan H.A. Hin L.S. Zaini N. Ahmed A.N. El-Shafie A. 56436206700 57113510800 55579596900 57214146115 55839645200 57192892703 55006925400 56436626600 57201523473 56905328500 57214837520 16068189400 algorithm; artificial intelligence; comparative study; decision making; evolutionary theory; expert system; irrigation; management practice; methodology; numerical model; power generation; reservoir; water management; water resource; India; Karnataka; Chondrichthyes 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. Final 2023-05-29T07:26:17Z 2023-05-29T07:26:17Z 2019 Article 10.3390/su11071953 2-s2.0-85064053968 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064053968&doi=10.3390%2fsu11071953&partnerID=40&md5=966dadcdc8ef72da88b2bb772610c778 https://irepository.uniten.edu.my/handle/123456789/24725 11 7 1953 All Open Access, Gold, Green MDPI Scopus |
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algorithm; artificial intelligence; comparative study; decision making; evolutionary theory; expert system; irrigation; management practice; methodology; numerical model; power generation; reservoir; water management; water resource; India; Karnataka; Chondrichthyes |
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56436206700 |
| author_facet |
56436206700 Yaseen Z.M. Ehteram M. Hossain M.S. Fai C.M. Koting S.B. Mohd N.S. Jaafar W.Z.B. Afan H.A. Hin L.S. Zaini N. Ahmed A.N. El-Shafie A. |
| format |
Article |
| author |
Yaseen Z.M. Ehteram M. Hossain M.S. Fai C.M. Koting S.B. Mohd N.S. Jaafar W.Z.B. Afan H.A. Hin L.S. Zaini N. Ahmed A.N. El-Shafie A. |
| spellingShingle |
Yaseen Z.M. Ehteram M. Hossain M.S. Fai C.M. Koting S.B. Mohd N.S. Jaafar W.Z.B. 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 |
| author_sort |
Yaseen Z.M. |
| title |
A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems |
| title_short |
A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems |
| title_full |
A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems |
| title_fullStr |
A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems |
| title_full_unstemmed |
A novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: Application to multi-purpose reservoir systems |
| title_sort |
novel hybrid evolutionary data-intelligence algorithm for irrigation and power production management: application to multi-purpose reservoir systems |
| publisher |
MDPI |
| publishDate |
2023 |
| _version_ |
1806424147955286016 |
| score |
13.188404 |