Abstract
Predicting rainfall is one of the difficult and uncertain activities that have a significant influence on human society. Predictions that are correct and timely can help to prevent financial and human loss. Using the Computational approach of Machine learning algorithms, rainfall prediction can be performed by extracting and merging latent knowledge from linear and nonlinear trends in prior weather data. This study discusses a series of studies that used data mining algorithms to construct models that predict whether it will rain tomorrow in major Australian cities based on previous meteorological data for that day. Moreover, AutoML (Automated Machine Learning) technique is applied to find out which model will predict higher accuracy. The results demonstrate a comparison of a variety of evaluation measures for different machine learning techniques, as well as their accuracy in predicting rainfall using weather data.
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Labade, A., Gupta, B., Gupta, R.K., Kumar, A. (2023). Machine Learning-Based Prototype Design for Rainfall Forecasting. In: Ramdane-Cherif, A., Singh, T.P., Tomar, R., Choudhury, T., Um, JS. (eds) Machine Intelligence and Data Science Applications. MIDAS 2022. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-1620-7_13
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