Application of Back-Propagation Artificial Neural Network Models for Prediction of Groundwater Levels: Case study in Western Jilin Province, China
Evaluation and forecast of groundwater levels through specific model helps in forecasting of groundwater resources. Among the different robust tools available, the Back-Propagation Artificial Neural Network (BPANN) model is commonly used to empirically forecast hydrological variables. Here, we discuss the modeling process and accuracy of this method based on the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE) and coefficient of efficiency (R2). The arid and semi-arid areas of western Jilin province (China) were chosen as study area owing to the decline of groundwater levels during the past decade mainly due to overexploitation. The simulations results indicated that BPANN is accurate in reproducing (fitting) and forecasting the groundwater levels time series based on the R2 are 0.97 and 0.74, respectively. The RMSE, MAE for BPANN model in the predicting stage are 0.08, 0.066, respectively. It is evident that the BPANN is able to predict the groundwater levels reasonable well.
Groundwater levels Back-Propagation Artificial Neural Network (BPANN) Fitting and forecasting Western Jilin Province
Zhongping YANG Wenxi LU Yuqiao LONG Ping LI
College of Environment and Resources, Jilin University Changchun 130026, China
国际会议
上海
英文
2845-2848
2008-05-16（万方平台首次上网日期，不代表论文的发表时间）