Use of Back-propagation Artificial Neural Networks for Groundwater Level Simulation

Article type: Research Article

Authors: Affandi, Azhar K. | Watanabe, Kunio | Tirtomihardjo, Haryadi

Affiliations: Geosphere Research Institute of Saitama University Shimo Okubo 255, Sakura-ku, Saitama 338-8570, Japan | Center of Environmental Geology, Geological Agency Department of Energy and Mineral Resources Jalan Diponegoro No. 5, Bandung 40122, Indonesia

Note: [] Corresponding author. E-mail: azhar_affandi@yahoo.com

Abstract: This article presents simulation of groundwater level fluctuation based on an artificial neural network modelling. The prediction used multi-layer back-propagation neural networks (BPANN). The case of study area was Jakarta, Indonesia, that has high population density and several purposes of groundwater resource usage. Input variables were using delay five-daily groundwater level fluctuation (GLF) of observation well interest to predict current GLF. The applicability of BPANN for GLF prediction was verified in three sets of input variables. The result showed that application of BPANN to simulate GLF gives satisfied prediction results.

Keywords: Groundwater level fluctuation, forecast, artificial neural network, back-propagation, Jakarta, Indonesia

Journal: Asian Journal of Water, Environment and Pollution, vol. 5, no. 1, pp. 57-65, 2008