Search

Rainfall forecasting plays a crucial role in managing water resources, agricultural planning, and mitigating the risks of disasters. In this study, we propose an approach using multi-output neural network to predict rainfall rates specifically in East Java Province, Indonesia. The neural network model is trained using historical rainfall data obtained from eleven meteorological stations across East Java Province. To optimize the model's performance, we used resilient propagation algorithm during the training process. VAR (Vector Autoregressive) modeling was utilized to determine the significant lags to be used as input variables in the neural network model. This enabled us to capture the relationships and dependencies between different time steps. The model's performance was evaluated using two metrics, Root Mean Squared Error (RMSE) and Akaike's Information Criterion (AIC). The best-performing model was the Feed Forward Neural Network Model, which outperformed the VAR models, with a lower score of RMSE and AIC value. Furthermore, the model consistently delivered accurate predictions across all sampled meteorological stations, indicating its suitability and reliability for rainfall forecasting in East Java Province. The proposed multi-output neural network approach offers an effective solution for rainfall forecasting in East Java Province. Its implementation can significantly contribute for decision-making in water resource management and disaster mitigation. Providing accurate rainfall forecasts can aid in planning and preparing for potential weather-related challenges and minimizing the adverse impacts of extreme weather events in the region. [ABSTRACT FROM AUTHOR]