Dynamic Characteristics and Trend Prediction of Groundwater Level in Xi’an City, China Based on GM (1, 1) and BP Neural Network Models

Groundwater is exteremely important in arid and semiarid regions, and the core of its effective protection and rational utilization lies in accurate prediction and evaluation of groundwater dynamics, based on which protection, utilization, and planning strategies are formulated. Based on groundwater level monitoring data from 2010 to 2020 in Xi'an City, this study systematically analyzed the inter-annual and intra-annual dynamic changes in groundwater levels, investigated the main factors influencing groundwater dynamics, and conducted a correlation analysis using SPSS on the two primary factors affecting groundwater dynamics: precipitation and extraction volume. Furthermore, the study utilized the GM (1,1) grey prediction model and the BP neural network model to forecast the trend of groundwater level changes. The results indicate that: ① From 2010 to 2016, the groundwater level showed an overall decreasing trend. However, from 2016 to 2020, due to the yearly reduction in extraction volume and continuous optimization and improvement of water supply facilities, the groundwater level exhibited a rising trend. ② Both precipitation and human extraction significantly impact the groundwater level fluctuations in Xi'an. The depth of the groundwater level is a crucial factor determining the degree of influence from precipitation, with river floodplains being the most sensitive, followed by terraces, and loess plateaus showing the weakest response. The correlation between groundwater extraction volume and groundwater depth is stronger, highlighting its dominant role in regulating groundwater level dynamics. ③ Groundwater level predictions suggest that as groundwater extraction continues to decline annually, the overall groundwater in the study area is on a fluctuating upward trend. This study has conducted research on the influencing factors and prediction trends of groundwater dynamics in Xi'an, which has important reference value for groundwater resource management and sustainable development.