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Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall

Author

Listed:
  • Quoc Bao Pham

    (National Cheng-Kung University)

  • S. I. Abba

    (Yusuf Maitama Sule University Kano)

  • Abdullahi Garba Usman

    (Near East University)

  • Nguyen Thi Thuy Linh

    (National Cheng-Kung University
    Thuyloi University)

  • Vivek Gupta

    (Indian Institute of Technology Roorkee)

  • Anurag Malik

    (G.B. Pant University of Agriculture and Technology)

  • Romulus Costache

    (Research Institute of the University of Bucharest
    National Institute of Hydrology and Water Management)

  • Ngoc Duong Vo

    (The University of Danang)

  • Doan Quang Tri

    (Ton Duc Thang University)

Abstract

One of the most challenging tasks in rainfall prediction is designing a reliable computational methodology owing the random and stochastic characteristics of time-series. In this study, the potential of five different data-driven models including Multilayer Perceptron (MLP), Least Square Support Vector Machine (LSSVM), Neuro-fuzzy, Hammerstein-Weiner (HW) and Autoregressive Integrated Moving Average (ARIMA) were employed for multi-station (Hien, Thank My, Hoi Khanh, Ai Nghia and Cai Lau) prediction of daily rainfall in the Vu Gia-Thu Bon River basin in Central Vietnam. Subsequently, hybrid ARIMA-MLP, ARIMA-LSSVM, ARIMA-NF and ARIMA-HW models were also utilized to predict the daily rainfall at these stations. The results were evaluated in terms of widely used performance criteria, viz.: determination coefficient (R2), root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC). Besides, the Taylor diagram is also used to examine and compare the similarity between the observed and predicted rainfall. The quantitative analysis indicated that the HW model increased the prediction accuracy by 5%, 3% and 2% at Hien, Ai Nghia and Cau Lau stations, respectively, compared to the other models. Likewise, the NF model increased the prediction accuracy at Thanh My and Hoi Khanh stations in contrast to the other models in terms of the mean absolute error. Also, the results of hybrid ARIMA-NF and ARIMA-HW models showed the best performance in terms of predictive skills and verified to increase the prediction accuracy in comparison to the single models.

Suggested Citation

  • Quoc Bao Pham & S. I. Abba & Abdullahi Garba Usman & Nguyen Thi Thuy Linh & Vivek Gupta & Anurag Malik & Romulus Costache & Ngoc Duong Vo & Doan Quang Tri, 2019. "Potential of Hybrid Data-Intelligence Algorithms for Multi-Station Modelling of Rainfall," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(15), pages 5067-5087, December.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:15:d:10.1007_s11269-019-02408-3
    DOI: 10.1007/s11269-019-02408-3
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    5. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.

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