Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study

Accurate streamflow estimation is crucial for proper water management for irrigation, hydropower, drinking and industrial purposes. The main aim of this study to adopt new data preprocessing techniques (e.g., EMD, EEMD and EWT) to capture the data noise and to enhance the prediction accuracy of machine learning methods for streamflow estimation which is a challenging task in high-altitude basins due to the influence of many external climatic and geographical parameters. The prediction accuracy of support vector regression (SVR), twin support vector machine (T), extreme learning machine (ELM), asymmetric Huber loss function-based ELM (AHELM) and epsilon-insensitive Huber loss function-based ELM (epsilon-AHELM) methods are investigated in monthly streamflow prediction. Among the standalone methods, the epsilon-AHELM performs superior to the SVR, TSVR, ELM, and AHELM in streamflow prediction; improvements in root mean square error are 6.9%, 4.9%, 6% and 4.2%, respectively. The study outcomes reveal that the preprocessing methods considerably improve the prediction accuracy of the implemented standalone models. Among the data preprocessing techniques, it is found that the EWT outperforms the EMD and EEMD techniques by reducing the prediction errors in the best epsilon-AHELM, EMD-epsilon-AHELM and EEMD-epsilon-AHELM models by 68-61.3%, 64.7-63.4% and 59.4-58.6%, respectively. The overall results of the study recommend the use of EWT-epsilon-AHELM in streamflow estimation.

Automated summary

Accurate streamflow estimation is crucial for water management. This study aims to improve prediction accuracy by adopting new data preprocessing techniques and machine learning methods for streamflow estimation in high-altitude basins. The results show that the epsilon-AHELM method outperforms other methods, and the EWT technique is more effective in reducing prediction errors compared to EMD and EEMD techniques. Overall, it is recommended to use EWT-epsilon-AHELM for streamflow estimation.