Abstract
The primary goal of this study is to devise robust models for analyzing daily streamflow time series across three distinct watersheds in northeastern Algeria, employing artificial intelligence techniques. The approach integrates four predictive models: Multi-Layer Perceptron Neural Network (MLPNN), Extreme Learning Machine (ELM), Random Forest Regression (RFR), and M5 Tree Model (M5Tree). A novel modeling technique introduced herein leverages the Maximum Overlap Discrete Wavelet Transform (MODWT) for preprocessing the input variables. This technique decomposes the inputs into multiple sub-signals, which then serve as new inputs for the machine learning models. The enhanced models, particularly MODWT-MLPNN and MODWT-M5Tree, demonstrated superior numerical performance, achieving correlation coefficients (R) of 0.994 and 0.989 and Nash-Sutcliffe Efficiency (NSE) scores of 0.985 and 0.977, respectively. These results underscore the effectiveness of the decomposition method in surpassing the accuracy of standalone models.
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Conceptualization: Noureddine Daif and Aziz Hebal
Data curation: Noureddine Daif and Aziz Hebal
Formal analysis: Noureddine Daif and Aziz Hebal
Validation: Noureddine Daif and Aziz Hebal
Supervision: Noureddine Daif and Aziz Hebal
Writing original draft: Noureddine Daif and Aziz Hebal
Visualization: Noureddine Daif and Aziz Hebal
Investigation: Noureddine Daif and Aziz Hebal
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Highlights
1. This research utilizes four predictive models: Extreme Learning Machine (ELM), Multilayer Perceptron Neural Network (MLPNN), Random Forest Regressor (RFR), and M5Tree, to predict daily streamflow in northeastern Algeria.
2. A pioneering method is introduced, integrating machine learning models with Maximum Overlap Discrete Wavelet Transform (MODWT) for preprocessing to significantly boost the accuracy of daily streamflow forecasting.
3. The application of MODWT for signal decomposition is explored, and its efficacy is evaluated in comparison to the performance of standalone models.
4. The integration of signal decomposition via MODWT distinctly enhances model efficacy, confirming its vital role in achieving more precise and dependable streamflow predictions.
Appendix
Appendix
Figs. 13, 14, 15, 16, 17, 18, 19, 20, 21
Scatterplot between measured and calculated daily streamflow for the best models in the validation stage for Ain El Asel station (station ID: 031601)
Scatterplot between measured and calculated daily streamflow for the best models in the validation stage for Bouchegouf station (station ID: 140501)
Scatterplots between measured and calculated daily streamflow using hybrid models based MODWT for the validation stage: AIN ASEL station
Scatterplots between measured and calculated daily streamflow using hybrid models based MODWT for the validation stage: BOUCHEGOUF station
Examples of graphs showing the performance of the best developed models during the validation stage at the AIN EL ASSEL Station: a Taylor diagram, b Radar plot and (c) Boxplot, d Violin plot
Examples of graphs showing the performance of the best developed models during the validation stage at the BOUCHEGOUF Station: a Taylor diagram, b Radar plot and (c) Boxplot, d Violin plot
Comparison between measured and forecasted daily streamflow using the MODWT model at the three stations
The decomposition for the AIN ASSEL station of a)rainfall, b)runoff
The decomposition for the AIN ASSEL station of a)rainfall, b)runoff
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Daif, N., Hebal, A. Enhanced daily streamflow forecasting in Northeastern Algeria: integrating hybrid machine learning with advanced wavelet transformation techniques. Model. Earth Syst. Environ. 10, 5351–5379 (2024). https://doi.org/10.1007/s40808-024-02067-3
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DOI: https://doi.org/10.1007/s40808-024-02067-3
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