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实时预测煤矿地下水水位及其分布范围对于煤矿的安全生产至关重要。以韩城象山矿区的历史水文数据为研究依据,采用RBF神经网络技术,对预测该地区的地下水水位进行了研究。针对RBF神经网络的不足,对RBF算法的中心点数量,计算欧氏距离时各分量的权重进行了优化,导出了ARBF算法。数据对比表明,ARBF神经网络算法达到了更为理想的效果。
Real-time forecast coal mine groundwater level and its distribution range for the safety of coal production is essential. Based on the historical hydrological data of Xiangshan mining area in Hancheng, RBF neural network is used to study the prediction of groundwater level in this area. Aiming at the deficiency of RBF neural network, the weights of each component of RBF algorithm and Euclidean distance are calculated. The ARBF algorithm is derived. Data comparison shows that the ARBF neural network algorithm achieves a more satisfactory result.