Rainfall Prediction Using Machine Learning

Rainfall Prediction

Rainfall, a fundamental component of Earth's climate system, plays a crucial role in shaping ecosystems, sustaining agriculture, and influencing various aspects of human life (Hiroi & Kawaguchi, 2016). Understanding and predicting rainfall patterns are central to effective water resource management, agricultural planning, and disaster preparedness. Traditional methods of weather forecasting have evolved, and contemporary approaches increasingly incorporate advanced technologies, including machine learning, to enhance prediction accuracy. Machine learning algorithms analyze historical meteorological data, discern intricate patterns, and make predictions that aid in proactive decision-making. As climate change introduces greater variability in weather patterns, the demand for more accurate and timely rainfall predictions becomes imperative. This study explores the intersection of meteorology and machine learning in the context of rainfall prediction, aiming to contribute to the development of more robust and efficient models. By delving into historical data and leveraging computational advancements, this research seeks to improve our ability to anticipate and adapt to changing rainfall patterns, ultimately fostering resilience and preparedness in a world characterized by dynamic climatic conditions.