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

Engineering Applications of Computational Fluid Mechanics

Haitham Abdulmohsin Afan¹  Ahmedbahaaaldin Ibrahem Ahmed Osman²  Yusuf Essam²  Kwok-wing Chau³  Ozgur Kisi⁴  Mohsen Sherif⁵  Ahmed Sefelnasr⁵  Ahmed El-Shafie⁵  Ali Najah Ahmed⁶  Yuk Feng Huang⁷ 

[1] Al-Maarif University College;College of Engineering, Universiti Tenaga Nasional (UNITEN);Hong Kong Polytechnic University;Ilia State University;United Arab Emirates University;Universiti Tenaga Nasional (UNITEN);Universiti Tunku Abdul Rahman;

关键词: groundwater level prediction;    deep learning model;    ensemble deep learning model;    malaysia;   

DOI  :  10.1080/19942060.2021.1974093

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown