Groundwater depth predictions by GSM,RBF, and ANFIS models: a comparative assessment |
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| Authors: | Nan Zhang Changlai Xiao Bo Liu Xiujuan Liang |
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| Institution: | 1.College of Environment and Resources,Jilin University,Changchun,China;2.Key Laboratory of Groundwater Resources and Environment, Ministry of Education,Jilin University,Changchun,China;3.Shengyang Academy of Environmental Sciences,Shengyang,China |
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| Abstract: | The potential of grey self-memory model (GSM), radial basis function network (RBF), and adaptive neuro fuzzy inference system (ANFIS) models in forecasting groundwater depths over an unconfined aquifer was compared. GSM, RBF, and ANFIS modeling was carried out at five sites in Jilin City, northeastern China, considering the influential lags of monthly groundwater depth as the inputs. The performance of the models was evaluated using criteria standards (R, RMSE, MARE, NS) and graphical indicators. Results indicate that the performance of all models was satisfactory in the region which lack of hydro-meteorological data. Comparison of the goodness-of-fit statistics in the research indicated that ANFIS was the better technique than the other two at all the sites except for J21, and GSM(1,1) was the worst model at all the sites. However, considering the practical advantages of GSM(1,1) technique, it was recommended as an alternative and cost-effective groundwater modeling tool. Meanwhile, it was found that the modeling prediction for the well with the stable and evenly distributed data series has more accurate fitting results, generally. |
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