Estimation of groundwater level using artificial neural networks: A case study of Hatay-Turkey
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CitationUnes, F., Demirci, M., Ispir, E., (...), Mamak, M., Tasar, B. (2017). Estimation of groundwater level using artificial neural networks: A case study of Hatay-Turkey. 10th International Conference on Environmental Engineering, ICEE 2017, enviro.2017.092. doi: 10.3846/enviro.2017.092
Groundwater, which is a strategic resource in Turkey, is used for drinking-use, agricultural irrigation and industrial purposes. Population increase and total water consumption are constantly increasing. In order to meet the need for water, over-shoots from underground water have caused significant falls in groundwater level. Estimation of water level is important for planning an efficient and sustainable groundwater management. In this study, groundwater level, monthly mean precipitation and temperature observations of Turkish General Directorate of State Hydraulic Works (DSI) in Hatay, Amik Plain, Kumlu district were used between 2000 and 2015 years. The performance evaluation was done by creating Multi Linear Regression (MLR) and Artificial Neural Networks (ANN) models. The ANN model gave better results than the MLR model. © 2017 Fatih Üneş, Mustafa Demirci, Eyup Ispir,Yunus Ziya Kaya, Mustafa Mamak, Bestami Tasar. Published by VGTU Press. This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY-NC 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.