随着我国各项建设对水资源的需求越来越大,导致由地下水开采而引起的沉降问题成为当前研究的热点课题。本文对某矿区采水地面沉降进行了模型设计,通过对部分实测数据的训练,优选出该模型的网络结构和网络参数,并且用Matlab软件编程实现对其他监测点的计算和预测。通过研究表明本文所建立的BP神经网络模型能较准确反映采水地面沉降的规律,同时也能较准确地预测地下水开采引起的地面沉降。 With the increasing demand for water resources in various constructions in our country, the settlement problem caused by groundwater exploitation has become a hot topic in current research. In this paper, the groundwater subsidence in a mining area is modeled. Through the training of some measured data, the network structure and network parameters of the model are optimized, and the calculation and prediction of other monitoring points are realized by Matlab software. The research shows that the BP neural network model established in this paper can reflect the law of land subsidence more accurately and predict the land subsidence caused by groundwater exploitation more accurately.