Rainfall modeling using two different neural networks improved by metaheuristic algorithms - Sammen S.S.; Kisi O.; Ehteram M.; El-Shafie A.; Al-Ansari N.; Ghorbani M.A.; Bhat S.A.; Ahmed A.N.; Shahid S. (Springer, 2024)
Rainfall is crucial for the development and management of water resources. Six hybrid soft computing models, includingmultilayer perceptron (MLP)Henry gas solubility optimization (HGSO), MLPbat algorithm (MLPBA), MLPparticle swarm optimization (MLPPSO), radial basis neural network function (RBFNN)HGSO, RBFNNPSO, and RBFGNNBA, were used in this study to forecast monthly rainfall at two stations in Malaysia (Sara and Banding). Different statistical measures (mean absolute error (MAE) and NashSutcliffe efficiency (NSE) and percentage of BIAS (PBIAS)) and a Taylor diagram were used to assess the models performance. The results indicated that the MLPHGSO performed better than the other models in forecasting rainfall at both stations. In addition, transition matrices were computed for each station and year based on the conditional probability of rainfall or absence of rainfall on a given month. The values of MAE for testing processes for the MLPHGSO, MLPPSO, MLPBA, RBFNNHGSO, RBFNNBA, and RBFNNPSO at the first station were 0.712, 0.755, 0.765, 0.717, 0.865, and 0.891, while the corresponding NSE and PBIAS valueswere 0.900.23, 0.830.29, 0.850.25, 0.870.27, 0.810.31, and 0.800.35, respectively. For the second station, the values of MAE were found 0.711, 0.743, 0.742, 0.719, 0.863 and 0.890 for the MLPHGSO, MLPPSO, MLPBA, RBFNNHGSO, RBFNNBA, and RBFNNPSO during testing processes and the corresponding NSEPBIAS values were 0.920.22, 0.850.28, 0.890.26, 0.910.25, 0.830.31, 0.820.32, respectively. Based on the outputs of the MLPHGSO, the highest rainfall was recorded in 2012 with a probability of 0.72, while the lowest rainfall was recorded in 2006 with a probability of 0.52 at the Sara Station. In addition, the results indicated that the MLPHGSO performed better than the other models within the Banding Station. According to the findings, the hybrid MLPHGSO was selected as an effective rainfall prediction model. 2023, The Author(s).