A novel combined model for prediction of daily precipitation data using instantaneous frequency feature and...

Meteorological events constantly affect human life, especially the occurrence of excessive precipitation in a short time causes important events such as floods. However, in case of insufficient precipitation for a long time, drought occurs. In recent years, significant changes in precipitation regimes have been observed and these changes cause socio-economic and ecological problems. Therefore, it is of great importance to correctly predict and analyze the precipitation data. In this study, a reliable and accurate precipitation forecasting model is proposed. For this aim, three deep neural network models, long short-time memory networks (LSTM), gated recurrent unit (GRU), and bidirectional long short time memory networks (biLSTM), were applied for one ahead forecasting of daily precipitation data and compared the performances of these models. Moreover, to increase the far ahead forecasting performance of the biLSTM model, the instantaneous frequency (IF) feature was applied as the input parameter for the first time in the literature. Therefore, a novel model ensemble of IF and biLSTM was employed for the aim of one-six ahead forecasting of daily precipitation data. The performance of the proposed IF-biLSTM model was evaluated using mean absolute error (MAE), mean square error (MSE), correlation coefficient (R), and determination coefficient (R²) performance parameter and spider charts were used to assess the model performances. According to the numerical results, the biLSTM model outperformed compared with the LSTM and GRU models. After the good score achieved with biLSTM model, IF feature applied to biLSTM and IF-biLSTM model has the best forecasting performance for daily precipitation data with R² value 0.9983, 0.9827, 0.9092, 0.8508, 0.7827, and 0.7646, respectively, for one-six ahead forecasting of daily precipitation data. It has been observed that the IF-biLSTM model has higher forecasting performance than the biLSTM model, especially in far ahead forecasting studies, and the IF feature improves the estimation performance.