Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria
Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is...
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| Format: | Article |
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MDPI AG
2023-10-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/20/3576 |
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| author | Mahdi Valikhan Anaraki Mohammed Achite Saeed Farzin Nehal Elshaboury Nadhir Al-Ansari Ismail Elkhrachy |
| author_facet | Mahdi Valikhan Anaraki Mohammed Achite Saeed Farzin Nehal Elshaboury Nadhir Al-Ansari Ismail Elkhrachy |
| author_sort | Mahdi Valikhan Anaraki |
| collection | DOAJ |
| description | Rainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling. |
| first_indexed | 2024-03-10T20:48:52Z |
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| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T20:48:52Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-6f4d0c88f41f4f8bb14a17a94fcf68492023-11-19T18:29:41ZengMDPI AGWater2073-44412023-10-011520357610.3390/w15203576Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, AlgeriaMahdi Valikhan Anaraki0Mohammed Achite1Saeed Farzin2Nehal Elshaboury3Nadhir Al-Ansari4Ismail Elkhrachy5Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, IranWater and Environment Laboratory, Faculty of Nature and Life Sciences, Hassiba Benbouali University of Chlef, Chlef 02180, AlgeriaDepartment of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan 35131-19111, IranConstruction and Project Management Research Institute, Housing and Building National Research Centre, Giza 12311, EgyptDepartment of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, SwedenCivil Engineering Department, College of Engineering, Najran University, Najran 66291, Saudi ArabiaRainfall–runoff modeling has been the core of hydrological research studies for decades. To comprehend this phenomenon, many machine learning algorithms have been widely used. Nevertheless, a thorough comparison of machine learning algorithms and the effect of pre-processing on their performance is still lacking in the literature. Therefore, the major objective of this research is to simulate rainfall runoff using nine standalone and hybrid machine learning models. The conventional models include artificial neural networks, least squares support vector machines (LSSVMs), K-nearest neighbor (KNN), M5 model trees, random forests, multiple adaptive regression splines, and multivariate nonlinear regression. In contrast, the hybrid models comprise LSSVM and KNN coupled with a gorilla troop optimizer (GTO). Moreover, the present study introduces a new combination of the feature selection method, principal component analysis (PCA), and empirical mode decomposition (EMD). Mean absolute error (MAE), root mean squared error (RMSE), relative RMSE (RRMSE), person correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and Kling Gupta efficiency (KGE) metrics are used for assessing the performance of the developed models. The proposed models are applied to rainfall and runoff data collected in the Wadi Ouahrane basin, Algeria. According to the results, the KNN–GTO model exhibits the best performance (MAE = 0.1640, RMSE = 0.4741, RRMSE = 0.2979, R = 0.9607, NSE = 0.9088, and KGE = 0.7141). These statistical criteria outperform other developed models by 80%, 70%, 72%, 77%, 112%, and 136%, respectively. The LSSVM model provides the worst results without pre-processing the data. Moreover, the findings indicate that using feature selection, PCA, and EMD significantly improves the accuracy of rainfall–runoff modeling.https://www.mdpi.com/2073-4441/15/20/3576water resources engineeringrainfall–runoff modelingmachine learning techniqueshybrid models |
| spellingShingle | Mahdi Valikhan Anaraki Mohammed Achite Saeed Farzin Nehal Elshaboury Nadhir Al-Ansari Ismail Elkhrachy Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria Water water resources engineering rainfall–runoff modeling machine learning techniques hybrid models |
| title | Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria |
| title_full | Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria |
| title_fullStr | Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria |
| title_full_unstemmed | Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria |
| title_short | Modeling of Monthly Rainfall–Runoff Using Various Machine Learning Techniques in Wadi Ouahrane Basin, Algeria |
| title_sort | modeling of monthly rainfall runoff using various machine learning techniques in wadi ouahrane basin algeria |
| topic | water resources engineering rainfall–runoff modeling machine learning techniques hybrid models |
| url | https://www.mdpi.com/2073-4441/15/20/3576 |
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