Automated predictive analytics tool for rainfall forecasting

• Published in: Scientific reports Vol. 11; no. 1; p. 17704
• Main Authors: Raval, Maulin, Sivashanmugam, Pavithra, Pham, Vu, Gohel, Hardik, Kaushik, Ajeet, Wan, Yun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 06-09-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166; 639/705; 704/172; Comparative studies; Decision making; Humanities and Social Sciences; Hydrologic data; Learning algorithms; Machine learning; multidisciplinary; Neural networks; Prediction models; Predictive analytics; Rainfall; Science; Science (multidisciplinary); Water resources; Australia
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-021-95735-8

Australia faces a dryness disaster whose impact may be mitigated by rainfall prediction. Being an incredibly challenging task, yet accurate prediction of rainfall plays an enormous role in policy making, decision making and organizing sustainable water resource systems. The ability to accurately predict rainfall patterns empowers civilizations. Though short-term rainfall predictions are provided by meteorological systems, long-term prediction of rainfall is challenging and has a lot of factors that lead to uncertainty. Historically, various researchers have experimented with several machine learning techniques in rainfall prediction with given weather conditions. However, in places like Australia where the climate is variable, finding the best method to model the complex rainfall process is a major challenge. The aim of this paper is to: (a) predict rainfall using machine learning algorithms and comparing the performance of different models. (b) Develop an optimized neural network and develop a prediction model using the neural network (c) to do a comparative study of new and existing prediction techniques using Australian rainfall data. In this paper, rainfall data collected over a span of ten years from 2007 to 2017, with the input from 26 geographically diverse locations have been used to develop the predictive models. The data was divided into training and testing sets for validation purposes. The results show that both traditional and neural network-based machine learning models can predict rainfall with more precision.