Automated predictive analytics tool for rainfall forecasting

Abstract 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 accur...

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Autores principales:	Maulin Raval, Pavithra Sivashanmugam, Vu Pham, Hardik Gohel, Ajeet Kaushik, Yun Wan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/edfb68310cae44049240b88c098ef8a6
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spelling	oai:doaj.org-article:edfb68310cae44049240b88c098ef8a62021-12-02T19:12:31ZAutomated predictive analytics tool for rainfall forecasting10.1038/s41598-021-95735-82045-2322https://doaj.org/article/edfb68310cae44049240b88c098ef8a62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95735-8https://doaj.org/toc/2045-2322Abstract 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.Maulin RavalPavithra SivashanmugamVu PhamHardik GohelAjeet KaushikYun WanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Maulin Raval Pavithra Sivashanmugam Vu Pham Hardik Gohel Ajeet Kaushik Yun Wan Automated predictive analytics tool for rainfall forecasting
description	Abstract 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.
format	article
author	Maulin Raval Pavithra Sivashanmugam Vu Pham Hardik Gohel Ajeet Kaushik Yun Wan
author_facet	Maulin Raval Pavithra Sivashanmugam Vu Pham Hardik Gohel Ajeet Kaushik Yun Wan
author_sort	Maulin Raval
title	Automated predictive analytics tool for rainfall forecasting
title_short	Automated predictive analytics tool for rainfall forecasting
title_full	Automated predictive analytics tool for rainfall forecasting
title_fullStr	Automated predictive analytics tool for rainfall forecasting
title_full_unstemmed	Automated predictive analytics tool for rainfall forecasting
title_sort	automated predictive analytics tool for rainfall forecasting
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/edfb68310cae44049240b88c098ef8a6
work_keys_str_mv	AT maulinraval automatedpredictiveanalyticstoolforrainfallforecasting AT pavithrasivashanmugam automatedpredictiveanalyticstoolforrainfallforecasting AT vupham automatedpredictiveanalyticstoolforrainfallforecasting AT hardikgohel automatedpredictiveanalyticstoolforrainfallforecasting AT ajeetkaushik automatedpredictiveanalyticstoolforrainfallforecasting AT yunwan automatedpredictiveanalyticstoolforrainfallforecasting
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Automated predictive analytics tool for rainfall forecasting

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