Title :
Groundwater level prediction using ARMA-ANN model
Author :
Shi, Beixiao ; Zhu, Changjun
Author_Institution :
Hebei Univ. of Eng., Handan, China
Abstract :
At present, classic methods are often used to predict groundwater level, but the result is not ideal. Recent studies show that the combinational prediction methods have higher precision than single prediction methods. A combinational prediction model is presented based on ARMA and ANN neural network. And it is applied to comprehensive analysis and prediction of groundwater level. Case study indicates that precision of the model is rather high and its popularization significance is better than the other models, and has some practical value when being used in the dynamic groundwater level analysis.
Keywords :
groundwater; hydrological techniques; neural nets; ANN neural network; ARMA neural network; ARMA-ANN model; BP neural network; combinational prediction methods; groundwater level analysis; groundwater level prediction; Atmosphere; Equations; Neural networks; Prediction methods; Predictive models; Statistical analysis; Stochastic processes; Stochastic resonance; Time series analysis; Yttrium; ARMA; BP neural network; groundwater level;
Conference_Titel :
Test and Measurement, 2009. ICTM '09. International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-4699-5
DOI :
10.1109/ICTM.2009.5413048