Abstract
Groundwater is a prominent resource of drinking and domestic water in the world. In this context, a feasible water resources management plan necessitates acceptable predictions of groundwater table depth fluctuations, which can help ensure the sustainable use of a watershed’s aquifers for urban and rural water supply. Due to the difficulties of identifying non-linear model structure and estimating the associated parameters, in this study radial basis function neural network (RBFNN) and GM (1, 1) models are used for the prediction of monthly groundwater level fluctuations in the city of Longyan, Fujian Province (South China). The monthly groundwater level data monitored from January 2003 to December 2011 are used in both models. The error criteria are estimated using the coefficient of determination (R2), mean absolute error (E) and root mean squared error (RMSE). The results show that both the models can forecast the groundwater level with fairly high accuracy, but the RBFN network model can be a promising tool to simulate and forecast groundwater level since it has a relatively smaller RMSE and MAE.
Highlights
Groundwater is a valuable resource for domestic, irrigation and industrial uses
Due to the difficulties of identifying non-linear model structure and estimating the associated parameters, in this study radial basis function neural network (RBFNN) and GM (1, 1) models are used for the prediction of monthly groundwater level fluctuations in the city of Longyan, Fujian Province (South China)
We evaluated the potential of the popular time series models (1, 1) method and the seasonal decomposition method; multiplicative and additive methods have been applied to simulate groundwater water tables in a coastal aquifer at Fujian Province, South China, and the simulated results are compared by evaluating the root mean square error (RMSE) and regression coefficient (R2)
Summary
Groundwater is a valuable resource for domestic, irrigation and industrial uses. In China, a large part of water is supplied by groundwater, thereby increasing its importance. As far as information is concerned, the systems which lack information, such as structure message, operation mechanism and behaviour document, are referred to as grey systems, where ‘‘grey’’ means poor, incomplete, uncertain, etc It has received increasing application in the field of hydrology (Xu et al 2008). From the point of view of the GM (1, 1) model, the dynamics of groundwater level is regarded as a typical grey system problem, where the GM (1, 1) model can better reflect the changing features of groundwater level It especially has the unique function of analysis and modelling for short time series, less statistical data and incomplete information of the system and has been widely applied (Deng 2002). There are many activation functions, the most commonly used is the Gaussian function (Schwenker et al 2001) and its mathematical model of the algorithm can be defined as follows: X
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