Novel hybrid and weighted ensemble models to predict river discharge series with outliers
In this study, a novel hybrid framework named HVK/HVA-HEM was designed to predict river discharge with outliers. Firstly, the Hampel filter (HF) identifies and corrects outliers in the discharge series. Next, this series was denoised and decomposed using ensemble empirical mode decomposition (EEMD)...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Kuwait Journal of Science |
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| Online Access: | https://www.sciencedirect.com/science/article/pii/S2307410824000130 |
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| author | Maha Shabbir Sohail Chand Farhat Iqbal |
| author_facet | Maha Shabbir Sohail Chand Farhat Iqbal |
| author_sort | Maha Shabbir |
| collection | DOAJ |
| description | In this study, a novel hybrid framework named HVK/HVA-HEM was designed to predict river discharge with outliers. Firstly, the Hampel filter (HF) identifies and corrects outliers in the discharge series. Next, this series was denoised and decomposed using ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) respectively. The HF-VMD components were employed to K-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models, while the HF-EEMD series was applied to the multilayer perceptron (MLP) model to obtain the predictions of the proposed HVK(HF-VMD-KNN), HVA(HF-VMD-ARIMA), and HEM(HF-EEMD-MLP) hybrid models. Lastly, using the mean absolute error (MAE) weights of HVK, HVA and HEM predictions, the HVK-HEM and HVA-HEM models were formulated. The application of the new hybrid framework was displayed using the discharge of four rivers in Pakistan. In terms of the RMSE of Kabul River, the HEM hybrid model had better performance than MLP (175.2053 m3/s), HF-MLP (156.1853 m3/s), EEMD-MLP (133.4049 m3/s) and VMD-MLP (170.1337 m3/s). Similarly, the proposed HVK and HEM hybrid models are more efficient than their respective single, HF, EEMD, and VMD-based models. Overall, the proposed HVA-HEM hybrid model outperformed all competing and proposed models. |
| format | Article |
| id | doaj-art-769a751024d449748ffe243bc3e32109 |
| institution | DOAJ |
| issn | 2307-4116 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Kuwait Journal of Science |
| spelling | doaj-art-769a751024d449748ffe243bc3e321092025-08-20T03:10:23ZengElsevierKuwait Journal of Science2307-41162024-04-01512100188https://doi.org/10.1016/j.kjs.2024.100188Novel hybrid and weighted ensemble models to predict river discharge series with outliersMaha Shabbir0https://orcid.org/0000-0002-1525-2505Sohail Chand1https://orcid.org/0000-0002-4564-143XFarhat Iqbal2https://orcid.org/0000-0002-8579-0966College of Statistical Sciences, University of the Punjab, Lahore, PakistanCollege of Statistical Sciences, University of the Punjab, Lahore, PakistanDepartment of Mathematics, Imam Abdulrahman Bin Faisal University, Saudi ArabiaIn this study, a novel hybrid framework named HVK/HVA-HEM was designed to predict river discharge with outliers. Firstly, the Hampel filter (HF) identifies and corrects outliers in the discharge series. Next, this series was denoised and decomposed using ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) respectively. The HF-VMD components were employed to K-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models, while the HF-EEMD series was applied to the multilayer perceptron (MLP) model to obtain the predictions of the proposed HVK(HF-VMD-KNN), HVA(HF-VMD-ARIMA), and HEM(HF-EEMD-MLP) hybrid models. Lastly, using the mean absolute error (MAE) weights of HVK, HVA and HEM predictions, the HVK-HEM and HVA-HEM models were formulated. The application of the new hybrid framework was displayed using the discharge of four rivers in Pakistan. In terms of the RMSE of Kabul River, the HEM hybrid model had better performance than MLP (175.2053 m3/s), HF-MLP (156.1853 m3/s), EEMD-MLP (133.4049 m3/s) and VMD-MLP (170.1337 m3/s). Similarly, the proposed HVK and HEM hybrid models are more efficient than their respective single, HF, EEMD, and VMD-based models. Overall, the proposed HVA-HEM hybrid model outperformed all competing and proposed models.https://www.sciencedirect.com/science/article/pii/S2307410824000130river dischargeoutliersdecompositiondenoisinghybrid models |
| spellingShingle | Maha Shabbir Sohail Chand Farhat Iqbal Novel hybrid and weighted ensemble models to predict river discharge series with outliers Kuwait Journal of Science river discharge outliers decomposition denoising hybrid models |
| title | Novel hybrid and weighted ensemble models to predict river discharge series with outliers |
| title_full | Novel hybrid and weighted ensemble models to predict river discharge series with outliers |
| title_fullStr | Novel hybrid and weighted ensemble models to predict river discharge series with outliers |
| title_full_unstemmed | Novel hybrid and weighted ensemble models to predict river discharge series with outliers |
| title_short | Novel hybrid and weighted ensemble models to predict river discharge series with outliers |
| title_sort | novel hybrid and weighted ensemble models to predict river discharge series with outliers |
| topic | river discharge outliers decomposition denoising hybrid models |
| url | https://www.sciencedirect.com/science/article/pii/S2307410824000130 |
| work_keys_str_mv | AT mahashabbir novelhybridandweightedensemblemodelstopredictriverdischargeserieswithoutliers AT sohailchand novelhybridandweightedensemblemodelstopredictriverdischargeserieswithoutliers AT farhatiqbal novelhybridandweightedensemblemodelstopredictriverdischargeserieswithoutliers |