A Review of Machine Learning Approaches for Rainfall Forecasting Trends, Datasets, and Challenges

Atharv Taksali

Abstract: Predicting rainfall is a must-have capability in climate science, and it has implications in other interconnected fields like agriculture, hydrology, energy, and disaster management. Traditionally, physical or statistical models were used; however, these models have faced limitations. Among these limitations are the high computational requirements, the inflexibility of assumptions, and the difficulty of capturing nonlinear atmospheric processes. Machine Learning (ML) and Deep Learning (DL) have become powerful alternatives. They are not only able to learn directly from the data, but also can model complex patterns in time and space, and provide more reliable forecasts. This survey collects the cutting-edge research works that have been done in ML and DL methods for rainfall prediction. It covers models ranging from the classical methods like Support Vector Regression and Random Forest to the highly complex architectures such as Long Short-Term Memory Networks (LSTM) and Convolutional Neural Networks. Besides that, it stresses the importance of the datasets that are obtained from satellite missions, re-analysis products, and community-driven platforms for better performance of the models. The review pinpointed existing issues, including data quality problems, missing values, limited coverage in geographical areas, high computational costs, and limited interpretability. Moreover, it presented ideas about the potential future paths of these issues. These paths involve hybrid and physics-informed models, improved benchmarking, real-time data integration, and interpretable AI. By describing the current state of research and the gaps therein, this paper sets out the essentials that constitute a viable framework for making advances in rainfall forecasting not only with higher precision, but also greater scalability and operational trust.

Keywords: Climate resilience, Deep Learning, Machine Learning, Rainfall forecasting, Time-series prediction

How to Cite?: Atharv Taksali, "A Review of Machine Learning Approaches for Rainfall Forecasting Trends, Datasets, and Challenges", Volume 14 Issue 12, December 2025, International Journal of Science and Research (IJSR), Pages: 1981-1983, https://www.ijsr.net/getabstract.php?paperid=SR251205113939, DOI: https://dx.doi.org/10.21275/SR251205113939