Convolutional Neural Network -Support Vector Machine Model-Gaussian Process Regression: A New Machine Model for Predicting Monthly and Daily Rainfall

Mohammad Ehteram (), Ali Najah Ahmed (), Zohreh Sheikh Khozani () and Ahmed El-Shafie ()
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Mohammad Ehteram: Semnan University
Ali Najah Ahmed: Universiti Tenaga Nasional (UNITEN)
Zohreh Sheikh Khozani: Bauhaus Universität Weimar
Ahmed El-Shafie: University of Malaya (UM)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2023, vol. 37, issue 9, No 15, 3655 pages

Abstract: Abstract Rainfall prediction is an important issue in water resource management. Predicting rainfall helps researchers to monitor droughts, surface water and floods. The current study introduces a new deep learning model named convolutional neural network (CONN)- support vector machine (SVM)- Gaussian regression process (GPR) to predict daily and monthly rainfall data in Terengganu River Basin, Malaysia. The CONN-SVM-GRP model can extract the most important features automatically. The main advantage of the new model is to reflect the uncertainty values in the modelling process. The lagged rainfall values were used as the input variables to the models. The proposed CONN-SVM-GRP model successfully decreased the Mean Absolute Error (MAE) of other models by 5.9%-23% at the daily scale and 20%-61% at the monthly scale. The CONN-SVM-GRP model also provided the lowest uncertainty among other models, making it a reliable tool for predicting data points and intervals. Hence, it can be concluded that CONN-SVM-GRP model contributes to the sustainable management of water resources, even when satellite data is unavailable, by using lagged values to predict rainfall. Additionally, the model extracts important features without using preprocessing methods, further improving its efficiency. Overall, the CONN-SVM-GRP model can help researchers predict rainfall, which is essential for monitoring water resources and mitigating the impacts of droughts, floods, and other natural disasters.

Keywords: Deep learning model; Rainfall pattern; Machine Learning model; Water resource management (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11269-023-03519-8

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