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Modeling of Fuzzy Cognitive Maps with a Metaheuristics-Based Rainfall Prediction System

Author

Listed:
  • Mesfer Al Duhayyim

    (Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia)

  • Heba G. Mohamed

    (Department of Electrical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Jaber S. Alzahrani

    (Department of Industrial Engineering, College of Engineering at Alqunfudah, Umm Al-Qura University, Mecca 24382, Saudi Arabia)

  • Rana Alabdan

    (Department of Information Systems, College of Computer and Information Science, Majmaah University, Al-Majmaah 11952, Saudi Arabia)

  • Mohamed Mousa

    (Department of Electrical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo 11845, Egypt)

  • Abu Sarwar Zamani

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Ishfaq Yaseen

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

  • Mohamed Ibrahim Alsaid

    (Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia)

Abstract

Rainfall prediction remains a hot research topic in smart city environments. Precise rainfall prediction in smart cities becomes essential for planning security measures before construction and transportation activities, flight operations, water reservoir systems, and agricultural tasks. Precise rainfall forecasting now becomes more complex than before because of extreme climatic changes. Machine learning (ML) approaches can forecast rainfall by deriving hidden patterns from historic meteorological datasets. Selecting a suitable classification method for forecasting has become a tough job. This article introduces the Fuzzy Cognitive Maps with a Metaheuristics-based Rainfall Prediction System (FCMM-RPS) technique. The intention of the FCMM-RPS technique is to predict rainfall automatically and efficiently. To accomplish this, the presented FCMM-RPS technique primarily pre-processes the rainfall data to make it compatible. In addition, the presented FCMM-RPS technique predicts rainfall using the FCM model. To enhance the rainfall prediction outcomes of the FCM model, the parameter optimization process is performed using a modified butterfly optimization algorithm (MBOA). The performance assessment of the FCMM-RPS technique is tested on a rainfall dataset. A widespread comparison study highlights the improvements of the FCMM-RPS technique in the rainfall forecasting process compared to existing techniques with a maximum accuracy of 94.22%.

Suggested Citation

  • Mesfer Al Duhayyim & Heba G. Mohamed & Jaber S. Alzahrani & Rana Alabdan & Mohamed Mousa & Abu Sarwar Zamani & Ishfaq Yaseen & Mohamed Ibrahim Alsaid, 2022. "Modeling of Fuzzy Cognitive Maps with a Metaheuristics-Based Rainfall Prediction System," Sustainability, MDPI, vol. 15(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2022:i:1:p:25-:d:1008924
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    References listed on IDEAS

    as
    1. Farinaz Behrooz & Norman Mariun & Mohammad Hamiruce Marhaban & Mohd Amran Mohd Radzi & Abdul Rahman Ramli, 2018. "Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps," Energies, MDPI, vol. 11(3), pages 1-41, February.
    2. Long, Wen & Wu, Tiebin & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2021. "Parameters identification of photovoltaic models by using an enhanced adaptive butterfly optimization algorithm," Energy, Elsevier, vol. 229(C).
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