RAINFALL PREDICTION SYSTEM PREDICTION USING MACHINE LEARNING: A CASE STUDY OF BILLIRI, GOMBE STATE
Abstract
This study presents the development of a machine learning-based rainfall prediction system designed for Billiri, Gombe State, Nigeria. The work aims to improve rainfall forecasting accuracy using historical meteorological data such as temperature, humidity. Different machine learning algorithms including Random Forest, Support Vector Machine, Artificial Neural Network, and Long Short-Term Memory (LSTM) were implemented and compared to determine the most effective predictive model. The system was developed using Php and html and css. The results demonstrated that machine learning techniques can significantly enhance rainfall prediction, providing accurate and timely forecasts beneficial for agricultural planning and disaster management. This project contributes to data-driven environmental decision-making and supports sustainable development efforts in Gombe State and similar regions.
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