Jianhua Ping
School of Water Conservancy and Environment Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China
Qiang Yu
School of Water Conservancy and Environment Engineering, Zhengzhou University, Zhengzhou, 450001, Henan, China
Jianfeng Cao
College of Environment and Resources, Jilin University, Changchun, 130026, Jilin, China
ABSTRACT
Groundwater is the important water supply source of many cities in north China and it plays an important role to the development of economy and society. The groundwater level fluctuation reflects groundwater resources variation. The research of groundwater level prediction is complicated mainly because the complication, nonlinear, multi-scale, mutability and stochastic phenomena are evidence in groundwater system. It is significantly valuable to improve the accuracy and reliability of groundwater level prediction and develop a forecasting model which is capable to describing the natural reality. The strengths and shortcomings of existing deterministic prediction models and stochastic models were summarized. A coupled model with deterministic model and stochastic model was developed to forecasting groundwater level in Hengshui City, North Plain, China. The deterministic model was employed to describe groundwater flow characteristics and delineate performance of groundwater system. The neural network sequence predicting model was utilized to identify the stochastic factors including precipitation, evaporation and first kind of boundary condition and to predict their values in future. The coupled model was applied to forecast groundwater level regime which provided scientific results for controlling groundwater tables.
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How to cite this article
Jianhua Ping, Qiang Yu and Jianfeng Cao, 2013. Application of Coupled Model of Deterministic and Stochastic Model in Predicting
Groundwater Regime in Hengshui City. Information Technology Journal, 12: 3390-3397.
DOI: 10.3923/itj.2013.3390.3397
URL: https://scialert.net/abstract/?doi=itj.2013.3390.3397
DOI: 10.3923/itj.2013.3390.3397
URL: https://scialert.net/abstract/?doi=itj.2013.3390.3397
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