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Neural Computing and Applications

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27-12-2022 | Original Article

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

Authors: Rana Muhammad Adnan Ikram, Barenya Bikash Hazarika, Deepak Gupta, Salim Heddam, Ozgur Kisi

Published in: Neural Computing and Applications | Issue 12/2023

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Abstract

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.

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Title: Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study
Authors: Rana Muhammad Adnan Ikram
Barenya Bikash Hazarika
Deepak Gupta
Salim Heddam
Ozgur Kisi
Publication date: 27-12-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 12/2023
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-08163-8

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