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Hybrid dynamic arithmetic city council optimization for improved rainfall prediction

Author

Listed:
  • P. Lathika

    (Noorul Islam Centre for Higher Education)

  • D. Sheeba Singh

    (Noorul Islam Centre for Higher Education)

Abstract

In the meteorological department rainfall prediction is one of the complex tasks because it is directly linked to human life and the Indian economy. There is a significant demand for accurate and effective rainfall prediction methods to make better decisions regarding precautionary measures. To predict rainfall amounts effectively, this study proposed a novel rainfall prediction method named the Hybrid Dynamic Arithmetic City Council Optimization (HDACO) algorithm. The proposed HDACO method is a combination of two algorithms namely the Dynamic Arithmetic Optimization (DAO) algorithm and the City Councils Evolution (CCE) algorithm. The study utilizes preprocessing steps namely data cleaning, filling missing values, and data normalization. After preprocessing, the features closely related to rainfall prediction are selected by the computation of the correlation matrix. Finally, based on the features selected the HDACO algorithm predicts the amount of rainfall. The HDACO algorithm is evaluated using an open weather dataset and the effectiveness of the HDACO algorithm is validated using measures such as rainfall rate, Mean Absolute Error (MAE), coefficient of determination (R2), and Root Mean Square Error (RMSE). As a result, the HDACO algorithm achieved RMSE of 0.272, MAE of 0.184, and R2 of 0.97 respectively. The performance of the HDACO algorithm is compared with existing methods and the results demonstrate the better performance of the HDACO algorithm in rainfall prediction.

Suggested Citation

  • P. Lathika & D. Sheeba Singh, 2024. "Hybrid dynamic arithmetic city council optimization for improved rainfall prediction," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 15(7), pages 3182-3192, July.
    • Handle: RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02324-9
    DOI: 10.1007/s13198-024-02324-9
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    References listed on IDEAS

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    1. Duong Tran Anh & Thanh Duc Dang & Song Pham Van, 2019. "Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks," J, MDPI, vol. 2(1), pages 1-19, February.
    2. Kumar, Nikhil & Poonia, Vikas & Gupta, B.B. & Goyal, Manish Kumar, 2021. "A novel framework for risk assessment and resilience of critical infrastructure towards climate change," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
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