Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques
This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time seri...
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2023
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my.uniten.dspace-264952023-05-29T17:11:12Z Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques Afan H.A. Ibrahem Ahmed Osman A. Essam Y. Ahmed A.N. Huang Y.F. Kisi O. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A. 56436626600 57221644207 57203146903 57214837520 55807263900 6507051085 7005414714 6505592467 7202674661 16068189400 This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia. � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Final 2023-05-29T09:11:12Z 2023-05-29T09:11:12Z 2021 Article 10.1080/19942060.2021.1974093 2-s2.0-85115650756 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115650756&doi=10.1080%2f19942060.2021.1974093&partnerID=40&md5=3437030c7c5bec0d0fef98ae68acf655 https://irepository.uniten.edu.my/handle/123456789/26495 15 1 1420 1439 All Open Access, Gold, Green Taylor and Francis Ltd. Scopus |
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This study proposes two techniques: Deep Learning (DL) and Ensemble Deep Learning (EDL) to predict groundwater level (GWL) for five wells in Malaysia. Two scenarios were proposed, scenario-1 (S1): GWL from 4 wells was used as inputs to predict the GWL in the fifth well and scenario-2 (S2): time series with lag time up to 20 days for all five wells. The results from S1 prove that the ensemble EDL generally performs superior to the DL in the estimation of GWL of each station using data of remaining four wells except the Paya Indah Wetland in which the DL method provide better estimates compared to EDL. Regarding S2, the EDL also exhibits superior performance in predicting daily GWL in all five stations compared to the DL model. Implementing EDL decreased the RMSE, NAE and RRMSE by 11.6%, 27.3% and 22.3% and increased the R, Spearman rho and Kendall tau by 0.4%, 1.1% and 3.5%, respectively. Moreover, EDL for S2 shows a high level of precision within less time lag, ranging between 2 and 4 compared to DL. Therefore, the EDL model has the potential in managing the sustainability of groundwater in Malaysia. � 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. |
| author2 |
56436626600 |
| author_facet |
56436626600 Afan H.A. Ibrahem Ahmed Osman A. Essam Y. Ahmed A.N. Huang Y.F. Kisi O. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A. |
| format |
Article |
| author |
Afan H.A. Ibrahem Ahmed Osman A. Essam Y. Ahmed A.N. Huang Y.F. Kisi O. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A. |
| spellingShingle |
Afan H.A. Ibrahem Ahmed Osman A. Essam Y. Ahmed A.N. Huang Y.F. Kisi O. Sherif M. Sefelnasr A. Chau K.-W. El-Shafie A. Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| author_sort |
Afan H.A. |
| title |
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| title_short |
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| title_full |
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| title_fullStr |
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| title_full_unstemmed |
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| title_sort |
modeling the fluctuations of groundwater level by employing ensemble deep learning techniques |
| publisher |
Taylor and Francis Ltd. |
| publishDate |
2023 |
| _version_ |
1806426487444733952 |
| score |
13.222552 |