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Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model

Author

Listed:
  • Fabio Di Nunno

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Italy)

  • Francesco Granata

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Italy)

  • Quoc Bao Pham

    (Faculty of Natural Sciences, Institute of Earth Sciences, University of Silesia in Katowice, Będzińska Street 60, 41-200 Sosnowiec, Poland)

  • Giovanni de Marinis

    (Department of Civil and Mechanical Engineering (DICEM), University of Cassino and Southern Lazio, Via Di Biasio, 43, 03043 Cassino, Italy)

Abstract

Precipitation forecasting is essential for the assessment of several hydrological processes. This study shows that based on a machine learning approach, reliable models for precipitation prediction can be developed. The tropical monsoon-climate northern region of Bangladesh, including the Rangpur and Sylhet division, was chosen as the case study. Two machine learning algorithms were used: M5P and support vector regression. Moreover, a novel hybrid model based on the two algorithms was developed. The performance of prediction models was assessed by means of evaluation metrics and graphical representations. A sensitivity analysis was also carried out to assess the prediction accuracy as the number of exogenous inputs reduces and lag times increases. Overall, the hybrid model M5P-SVR led to the best predictions among used models in this study, with R 2 values up to 0.87 and 0.92 for the stations of Rangpur and Sylhet, respectively.

Suggested Citation

  • Fabio Di Nunno & Francesco Granata & Quoc Bao Pham & Giovanni de Marinis, 2022. "Precipitation Forecasting in Northern Bangladesh Using a Hybrid Machine Learning Model," Sustainability, MDPI, vol. 14(5), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2663-:d:757921
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    References listed on IDEAS

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    1. Fanping Zhang & Huichao Dai & Deshan Tang, 2014. "A Conjunction Method of Wavelet Transform-Particle Swarm Optimization-Support Vector Machine for Streamflow Forecasting," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-10, May.
    2. Granata, Francesco & Di Nunno, Fabio, 2021. "Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks," Agricultural Water Management, Elsevier, vol. 255(C).
    3. Dieu Tien Bui & Ataollah Shirzadi & Ata Amini & Himan Shahabi & Nadhir Al-Ansari & Shahriar Hamidi & Sushant K. Singh & Binh Thai Pham & Baharin Bin Ahmad & Pezhman Taherei Ghazvinei, 2020. "A Hybrid Intelligence Approach to Enhance the Prediction Accuracy of Local Scour Depth at Complex Bridge Piers," Sustainability, MDPI, vol. 12(3), pages 1-24, February.
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    Cited by:

    1. Chaoqing Huang & Chao He & Qian Wu & MinhThu Nguyen & Song Hong, 2023. "Classification of the Land Cover of a Megacity in ASEAN Using Two Band Combinations and Three Machine Learning Algorithms: A Case Study in Ho Chi Minh City," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    2. Di Nunno, Fabio & Granata, Francesco, 2023. "Future trends of reference evapotranspiration in Sicily based on CORDEX data and Machine Learning algorithms," Agricultural Water Management, Elsevier, vol. 280(C).

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