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

• Published in: Neural computing & applications 2023-04, Vol.35 (12), p.9053-9070
• Main Authors: Ikram, Rana Muhammad Adnan, Hazarika, Barenya Bikash, Gupta, Deepak, Heddam, Salim, Kisi, Ozgur
• Format: Article
• Language: English
• Subjects: Accuracy; Artificial Intelligence; Artificial neural networks; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; High altitude; Image Processing and Computer Vision; Machine learning; Mountainous areas; Noise prediction; Original Article; Preprocessing; Probability and Statistics in Computer Science; Stream flow; Support vector machines; Water management
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-08163-8

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 ε-insensitive Huber loss function-based ELM (ε-AHELM) methods are investigated in monthly streamflow prediction. Among the standalone methods, the ε-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 ε-AHELM, EMD-ε-AHELM and EEMD-ε-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-ε-AHELM in streamflow estimation.