Evaluating the Accuracy of a Hybrid Neural Model with RBF-Polynomial Kernel for Rainfall Prediction: A Comparative Analysis of Trainlm and Trainrp Functions

Accurate rainfall prediction is crucial for effective water management and disaster mitigation. This study introduces a novel hybrid neural model that employs a fourth-degree polynomial kernel and provides the first empirical comparison of the trainlm and trainrp functions to enhance forecasting accuracy. This study explored the application of a neural network algorithm with RBF-Polynomial (degree 4) kernel for training and testing data in rainfall forecasting. This study focused on monthly rainfall data collected from Mataram City, Indonesia. We developed a hybrid BP-RVM algorithm as the main algorithm that offers a predictive approach to compare the trainlm and trainrp functions. We conducted 20 trials with combinations of learning, momentum, and gamma-RBF at internal values of 0.01-0.9. The training results from trainrp with more than 118 iterations yielded the best performance with learning rate 0.8 and momentum 0.2; MSE value of 2,236.25 and RMSE of 47.29. These results indicate a relatively low error rate for the proposed method. In contrast, the trainlm method, which only requires 18 iterations with a learning rate of 0.6 and momentum of 0.4, produces an MSE of 2,689.25 and RMSE of 51.86, showing its efficiency in reducing the computation time but with a slightly higher error rate than trainrp. Overall, the trainrp method was more accurate in capturing actual rainfall patterns with lower error rates, whereas the trainlm method exhibited good stability but greater sensitivity to parameter variations. This comparative analysis highlights the potential of trainrp to achieve more precise rainfall predictions within the study area.