Abstract
Accurate weather prediction is essential for supporting various human activities and mitigating the impacts of changing atmospheric conditions. Recent advances in artificial intelligence have enabled the development of data-driven forecasting models capable of capturing complex relationships among meteorological variables. This study proposes an Artificial Neural Network (ANN)-based weather prediction model using multi-sensor weather data, including temperature, humidity, precipitation, solar irradiance, and wind velocity. The proposed ANN architecture consists of an input layer, two hidden layers, and an output layer. Model performance was evaluated using three training–testing data splits (90/10, 80/20, and 70/30) with 100 and 150 training epochs. Prediction performance was assessed using accuracy and Root Mean Squared Error (RMSE). Experimental results demonstrate that the proposed model achieves the best performance with a 70/30 training–testing split and 150 training epochs, providing the highest prediction accuracy and the lowest RMSE among the evaluated configurations. These findings indicate that a relatively simple ANN architecture can effectively model multi-sensor weather data and provide reliable weather predictions.