| description abstract | This study introduces a novel hybrid model for predicting daily river inflow, combining the Hampel filter (HF) for outlier correction, local mean decomposition (LMD) for initial signal decomposition, and ensemble empirical mode decomposition (EEMD) for further decomposition into intrinsic mode functions (IMFs) and residue. The innovative aspect of this model lies in its dual decomposition strategy (LMD-EEMD) followed by prediction using the K-nearest neighbor (KNN) algorithm, resulting in the HF-LMD-EEMD-KNN (HLEK) approach. This combination aims to enhance the accuracy and reliability of inflow predictions. The model’s performance was evaluated using river inflow data from four rivers in the Indus River Basin, with key metrics including root relative squared error (RRSE). In the training phase, the HLEK model achieved MAE values of 7.072, 5.859, 2.308, and 3.709 for the Indus, Kabul, Jhelum, and Chenab rivers, respectively, significantly outperforming traditional models. The study concludes that the HLEK hybrid model significantly improves prediction accuracy over simpler models, providing a robust tool for forecasting river inflows. This enhanced accuracy is crucial for water resource management and planning in the Indus River Basin and potentially other regions. Modeling of hydrological variables plays a vital role in the management of available water resources in the world. Our study introduces a new hybrid modeling approach named HF-LMD-EEMD-KNN (HLEK) for river inflow prediction using its historical record. The proposed hybrid approach is a combination of outlier correction, decomposition methods, and a machine learning model. We have applied this approach to predict the daily inflow of the four main tributaries of the Indus River Basin in Pakistan. The results show that the proposed hybrid approach is efficient in modeling river inflow with low prediction errors and better accuracy. The effectiveness of the modeling approach is based on the data available in the respective study region. This approach can also be used in modeling other hydrological variables, such as river flow, run-off, outflow, and other. The proposed hybrid method can be helpful in the management of water flow and avoid issues of floods, heat waves, or droughts. | |
