Resumen
The accurate and reliable prediction of groundwater depth is the basis of the sustainable utilization of regional groundwater resources. However, the complexity of the prediction has been ignored in previous studies of regional groundwater depth system analysis and prediction, making it difficult to realize the scientific management of groundwater resources. To address this defect, taking complexity diagnosis as the research foundation, this paper proposes a new coupling forecast strategy for evaluating groundwater depth based on empirical mode decomposition (EMD) and a radial basis function neural network (RBFNN). The data used for complexity analysis and modelling are the monthly groundwater depth series monitoring data from 15 long-term monitoring wells from 1997 to 2007, which were collected from the Jiansanjiang Administration of Heilongjiang Agricultural Reclamation in China. The calculation results of the comprehensive complexity index for each groundwater depth series obtained are based on wavelet theory, fractal theory, and the approximate entropy method. The monthly groundwater depth sequence of District 8 of Farm Nongjiang, which has the highest complexity among the five farms in the Jiansanjiang Administration midland, was chosen as the modelling sample series. The groundwater depth series of District 8 of Farm Nongjiang was separated into five intrinsic mode function (IMF) sequences and a remainder sequence by applying the EMD method, which revealed that local groundwater depth has a significant one-year periodic character and an increasing trend. The RBFNN was then used to forecast and stack each EMD separation sequence. The results suggest that the future groundwater depth will remain at approximately 10 m if the past pattern of water use continues, exceeding the ideal depth of groundwater. Thus, local departments should take appropriate countermeasures to conserve groundwater resources effectively.