Rainfall predictions are critical exercises in agricultural, water resource, disaster and climate management. Conventional rainfall prediction techniques rely primarily on numerical weather prediction models, which may be computationally expensive and sometimes inadequate for predicting complex weather patterns. This research work champions an Artificial Intelligence (AI) and Machine Learning (ML)-based rainfall prediction system. Rainfall prediction variables input will, therefore, be the historical weather variables such as temperature, humidity, atmospheric pressure, wind speed, and rainfall. Machine learning algorithms such as Decision Tree, Random Forest, Support Vector Machine, and LSTM networks are trained and then performance compared to select the best-performing algorithm. Methods of data processing include normalization, feature extraction, and missing data treatments to improve the efficiency of the models. Based on classification or regression performance metrics, the models are evaluated with an accuracy, precision, and recall, mean absolute error (MAE), and a root mean square error (RMSE). Experimental results show that ensemble models and deep learning models provide more prediction accuracy compared to statistical approaches.
Rainfall Prediction, Artificial Intelligence, Machine Learning, Weather Forecasting, Time Series Analysis, LSTM, Random Forest, Climate Data Analytics
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