Predicting Precipitation: A Deep Dive into Rainfall Forecasting Methods

Abstract

Accurately predicting rainfall is crucial for managing water resources effectively, planning infrastructure, and ensuring reliable water supply. Researchers have explored various methods from data mining and machine learning to deep learning, statistics, and time series analysis to forecast rainfall. However, despite these advancements, accurately pinpointing rainfall amounts remains a significant challenge. This study delves into different approaches for estimating and forecasting rainfall, comparing their predictions with actual rainfall data. Highlighting the potential of machine learning techniques, this research emphasizes their role in enhancing accuracy. Such improvements not only support the growth of agriculture but also empower farmers to make better-informed decisions. By evaluating a range of methodologies, this paper aims to advance our understanding and application of rainfall prediction methods, crucial for sustainable water management and agricultural planning.