关键词: BP人工神经网络, 富水性评价, 含水层, 煤矿防治水

Abstract: In order to evaluate the water abundance of the study area, and to reduce the dependence of the evaluation results on the hydrographic bore. After making the best of geological and hydrogeological data, four main controlling factors was established, which are aquifer thickness, core recovery percentage, thickness ratio of brittle rock and weathering influence index. BP artificial neural network with autonomous learning, nonlinear mapping ability is introduced into the study, the data of 25 groups of main control factors of the treatment by quantification, normalization as a network of learning samples, taking the measured specific well yield as the prediction target, the simulation of the mapping relationship between the main control factor and the specific well yield has been realized by repeated training. Finally, the trained neural network is used to predict the water abundance, and the sensitivity analysis method is introduced to analyze the sensitivity of the prediction results to the dominant factors.