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

Author

Listed:
  • Zaher Mundher Yaseen

    (Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam)

  • Mohammad Ehteram

    (Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, Iran)

  • Md. Shabbir Hossain

    (School of Energy, Geoscience, Infrastructure and Society, Department of Civil Engineering, Heriot-Watt University, Putrajaya 62200, Malaysia)

  • Chow Ming Fai

    (Institute of Energy Infrastructure (IEI), Civil Engineering Department, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Suhana Binti Koting

    (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Nuruol Syuhadaa Mohd

    (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Wan Zurina Binti Jaafar

    (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Haitham Abdulmohsin Afan

    (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Lai Sai Hin

    (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Nuratiah Zaini

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Ali Najah Ahmed

    (Institute of Energy Infrastructure (IEI), Civil Engineering Department, Universiti Tenaga Nasional, Kajang 43000, Selangor, Malaysia)

  • Ahmed El-Shafie

    (Civil Engineering Department, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

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 (10 6 m 3 ), and the amount of released water based on the new hybrid algorithm (NHA) is 141.25 (10 6 m 3 ). 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 (10 6 kwh), whereas the NHA produces 18.09 (10 6 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.

Suggested Citation

  • Zaher Mundher Yaseen & Mohammad Ehteram & Md. Shabbir Hossain & Chow Ming Fai & Suhana Binti Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Jaafar & Haitham Abdulmohsin Afan & Lai Sai Hin & Nuratiah, 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems," Sustainability, MDPI, vol. 11(7), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:1953-:d:219203
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    References listed on IDEAS

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    Cited by:

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    3. Mohammad Ehteram & Ali Najah Ahmed & Chow Ming Fai & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2019. "Accuracy Enhancement for Zone Mapping of a Solar Radiation Forecasting Based Multi-Objective Model for Better Management of the Generation of Renewable Energy," Energies, MDPI, vol. 12(14), pages 1-26, July.

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