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

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...