Monthly Streamflow Forecasting Using Decomposition-Based Hybridization with Two-step Verification Method Over the Mangla Watershed, Pakistan

In this study, three ensemble and decomposition methods (DMs), i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) were coupled with artificial intelligence and machine lear...

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Bibliographic Details
Published in:Iranian journal of science and technology. Transactions of civil engineering 2023-02, Vol.47 (1), p.565-584
Main Authors: Tayyab, Muhammad, Xiaohua, Dong, Sibtain, Muhammad, Ahmad, Ijaz, Zahra, Aqeela, Azam, Muhammad Imran
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
Civil Engineering
Decomposition
Empirical analysis
Engineering
Extreme values
Hybridization
Hydrologic models
Lead time
Machine learning
Multilayer perceptrons
Performance evaluation
Rainfall-runoff relationships
Regression analysis
Research Paper
Runoff
Stream discharge
Stream flow
Streamflow forecasting
Support vector machines
Value analysis
Verification
Watersheds
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Summary:In this study, three ensemble and decomposition methods (DMs), i.e., empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and improved complete ensemble empirical mode decomposition with additive noise (ICEEMDAN) were coupled with artificial intelligence and machine learning based method AI-ML, i.e., multilayer perceptron (MLP), support vector regression (SVR) to develop six fundamental hybrid models to predict streamflow with one-month lead time. Developed models in this study were categorized into runoff models (RMs) and rainfall–runoff models (RRMs). Results indicated that (i) among standalone models (SMs), support vector regression (SVR) performs better than multilayer perceptron (MLP), (ii) decomposition methods (DMs) can improve the accuracy rate of the standalone models (SMs), and (iii) rainfall–runoff models (RRMs) have shown great accuracy throughout the investigation as compared to runoff models (RMs). To compare model performances, flow hydrographs (FHGs) were generated, and five performance evaluation criteria (PEC) were used to quantify the model precision. Two-step verification methods, i.e., extreme value analysis (EVA) and least value analysis (LVA) approaches, were proposed to verify the performances. Among all developed hybrid models (HMs), i.e., EMD-(MLP, SVR), EEMD-(MLP, SVR), and ICEEMDAN-(MLP, SVR), rainfall–runoff ICEEMDAN-(SVR) model was selected as an optimal model with MAE (59.56), RMSE (91.82), R (0.97) MAPE (8.75), and NSEC (0.97) for Mangla watershed, Pakistan.
ISSN:2228-6160
2364-1843
DOI:10.1007/s40996-022-00947-1