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

• Main Authors: Ehteram M., Ahmed A.N., Sheikh Khozani Z., El-Shafie A.
• Other Authors: 57113510800
• Format: Article
• Published: Springer Science and Business Media B.V. 2024
• Subjects: Deep learning model; Machine Learning model; Rainfall pattern; Water resource management; Malaysia; Terengganu; Terengganu Basin; West Malaysia; Convolution; Convolutional neural networks; Deep learning; Disasters; Drought; Floods; Forecasting; Gaussian distribution; Gaussian noise (electronic); Information management; Learning systems; Rain; Resource allocation; Surface waters; Uncertainty analysis; Water management; Convolutional neural network; Daily rainfall; Learning models; Machine learning models; Monthly rainfalls; Network support; Rainfall patterns; Support vectors machine; Water resources management; artificial neural network; climate prediction; Gaussian method; machine learning; natural disaster; precipitation assessment; rainfall; support vector machine; water resource; Support vector machines

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 regres...