Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

• Published in: Engineering applications of computational fluid mechanics Vol. 15; no. 1; pp. 1420 - 1439
• Main Authors: Afan, Haitham Abdulmohsin, Ibrahem Ahmed Osman, Ahmedbahaaaldin, Essam, Yusuf, Ahmed, Ali Najah, Huang, Yuk Feng, Kisi, Ozgur, Sherif, Mohsen, Sefelnasr, Ahmed, Chau, Kwok-wing, El-Shafie, Ahmed
• Format: Journal Article
• Language: English
• Published: Hong Kong Taylor & Francis 01.01.2021; Taylor & Francis Ltd; Taylor & Francis Group
• Subjects: Deep learning; deep learning model; ensemble deep learning model; Groundwater; Groundwater level prediction; Groundwater levels; Lag time; Malaysia; Performance prediction; Time lag; Wells; Malaysia
• ISSN: 1994-2060; 1997-003X
• DOI: 10.1080/19942060.2021.1974093

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.