The accurate prediction of groundwater level is of great significance for the sustainable development and rational utilization of water resources in the Heihe River Basin.In order to solve problems as difficulty in obtaining parameters,complex modeling process,and short time span of modeling data during numerical modeling,the multivariate LSTM algorithm was used to simulate monthly changes in groundwater level,and a LSTM model,that consists two layers,and each of layer consist 30 neurons,was constructed by using long-term observation data of groundwater level in the middle reaches of the Heihe River from 1986 to 2018 as a dataset.The prediction results of two data input modes,single dynamic type and mixed dynamic type were compared.The results show that:1)The constructed LSTM model can effectively predict the dynamic changes of groundwater level,and it fits well with the observed water level curve,with high prediction accuracy.2)The prediction accuracy of the same dynamic type input is slightly higher than that of mixed dynamic type input,proving that distinguishing different types of groundwater and using data of the same dynamic type as inputs can be helpful to improve the accuracy of deep learning models.This study has achieved rapid and accurate prediction of groundwater level,and provides a scientific basis for the development of groundwater resources in the Zhangye area.
Zhangye Basinwater level predictiondynamic analysis of groundwater leveldeep learningLSTM
万方数据
维普
