Advanced Rule-Based System for Rainfall Occurrence Forecasting by Integrating Machine Learning Techniques
Publication: Journal of Water Resources Planning and Management
Volume 149, Issue 1
Abstract
Though the magnitude of future rainfall is important in most water resources applications, many applications require its occurrence/nonoccurrence rather than its magnitude such as in agricultural systems management, drought management systems, regulated deficit irrigation for various crops, short-term municipal water demand modeling and management, and reservoir operation. The occurrence of rainfall is a classification problem that also affects day-to-day human activities and management. However, most of the work on rainfall forecasting is for rainfall magnitude, and very few studies on rainfall occurrence forecasting have been carried out in the past. Also, few artificial intelligence and machine learning techniques have been utilized in rainfall magnitude forecasting but not any work registered so far for forecasting rainfall occurrence using these methods. The proposed novel approach in this paper integrates two machine learning methods, artificial neural network (ANN) and decision tree (DT), which are capable of making rainfall occurrence forecasting comprehensible and accurate. For this purpose, the rules have been extracted by generating a DT using the input-output data obtained from an ANN rainfall occurrence forecasting model. Daily climatic data are employed to illustrate the methodology developed in this study. The obtained results show that during training, ANN models learned a fixed set of rules for rainfall occurrence forecasting. The obtained rules are simple and can be used as a tool for rainfall occurrence forecasting.
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Data Availability Statement
All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The data used in this work were obtained freely from the ISWS, Arcola, Illinois, USA, and is duly acknowledged. The computational facility provided by the Department of Civil Engineering, IIT Kanpur, and NIT Raipur to carry out this research is also duly acknowledged.
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Journal of Water Resources Planning and Management
Volume 149 • Issue 1 • January 2023
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© 2022 American Society of Civil Engineers.
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Received: Dec 30, 2021
Accepted: Aug 27, 2022
Published online: Oct 21, 2022
Published in print: Jan 1, 2023
Discussion open until: Mar 21, 2023
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