基于BP神经网络的煤矿地下水水位预报方法

The Forecasting Method of Coal Mine Ground-water-level Based on BP Neural Network

煤矿地下水是威胁煤矿安全生产的重要因素之一。文章在阐述BP网络原理和分析地下水水位特点和影响因素的基础上,提出了基于BP神经网络的煤矿地下水水位预报方法,并利用历史数据对该网络进行了训练学习,建立了地下水水位特征模型,可预报未来一个时期地下水水位的变化趋势。实验表明该方法效果良好,相对误差小于2%。

The coal mine ground-water is one of the important factors that threaten coal security. Based on describing the theory of BP network and analyzing the influencing factors of ground-water-level, this paper presented a forecasting method of coal mine ground-water-level based on BP neural network, and made some experiments by using a lot of historical data to build up the characteritic model and to forecast the changing trend of ground-water-level. The experimental results showed that the method works well with the relative error less than 2%.