Ghassemi, F. and Brennan, D. (2000) Resource Profile Subproject: An Evaluation of the Sustainability of the Farming Systems in the Brackish Water Region of the Mekong Delta. ACIAR Project, Canberra.

has been cited by the following article:

• TITLE: Groundwater Level Prediction Using Artificial Neural Networks: A Case Study in Tra Noc Industrial Zone, Can Tho City, Vietnam

  AUTHORS: Tran Van Ty, Le Van Phat, Huynh Van Hiep

  KEYWORDS: Groundwater Resources (GWR), Groundwater Levels (GWLs), Artificial Neural Network (ANN), Prediction, Tra Noc Industrial Zone

  JOURNAL NAME: Journal of Water Resource and Protection, Vol.10 No.9, September 11, 2018

  ABSTRACT: The objective of this study is to predict groundwater levels (GWLs) under different impact factors using Artificial Neural Network (ANN) for a case study in Tra Noc Industrial Zone, Can Tho City, Vietnam. This can be achieved by evaluating the current state of groundwater resources (GWR) exploitation, use and dynamics; setting-up, calibrating and validating the ANN; and then predicting GWLs at different lead times. The results show that GWLs in the study area have been found to reduce rapidly from 2000 to 2015, especially in the Middle-upper Pleistocene (qp2-3) and upper Pleistocene (qp3) due to the over-withdrawals from the enterprises for production purposes. Concerning this problem, an Official Letter of the People’s Committee of Can Tho City was issued and taken into enforcement in 2012 resulting in the reduction of exploitation. The calibrated ANN structures have successfully demonstrated that the GWLs can be predicted considering different impact factors. The predicted results will help to raise awareness and to draw an attention of the local/central government for a clear GWR management policy for the Mekong delta, especially the industrial zones in the urban areas such as Can Tho city.