基于SSA优化的GA-BP神经网络煤层底板突水预测模型与应用

随煤层开采深度的不断增加，煤矿生产过程中面临着复杂的突水机理和多变的突水主控因素，且各因素间相互联系的不确定性，使底板突水预测的难度不断增加。为准确预测底板突水危险性，针对底板突水的小样本、非线性问题，首先利用遗传算法(Genetic Algorithm，GA)将网络随机赋值的初始权值和阈值初次优化，再选取搜索能力强、稳定性较好的麻雀搜索算法(Sparrow Search Algorithm，SSA)对权值和阈值进行二次寻优，从而建立SSA-GA-BP神经网络底板突水预测模型。分析整理山东省滨湖煤矿地质及水文地质资料，选取含水层水压、含水层厚度、隔水层厚度、断层密度、断层分维值、渗透系数、单位涌水量、底板破坏深度共8个因素，作为预测底板突水的主控因素，绘制各主控因素3D映射投影曲面图；利用Surfer软件中的克里金插值法提取50个数据点作为模型的输入样本(分为训练集40个，测试集10个)，对模型进行训练学习，训练误差精度达到要求后，对滨湖煤矿3个未开采工作面的12个数据点进行突水危险性预测。为了验证所建模型的准确性，利用BP、GA-BP、SSA-GA-BP这3种模型对测试集进行预测；为避免模型仅与BP网络预测对比的片面性，同时选取以熵权法确定权重的模糊综合评判法对测试集进行预测；将各网络模型及方法的预测结果与实际值进行对比分析。结果表明:基于SSA优化的GA-BP神经网络模型突水预测误差较小，预测结果准确率更高，为矿井水害预测预报提供了科学的评价方法和理论依据。 

Prediction model of water inrush from coal floor based on GA-BP neural network optimized by SSA and its application

With the increase of coal mining depth, coal production process is faced with complex water inrush mechanism and variable water inrush main control factors, and the uncertainties among the factors make the prediction of floor water inrush more difficult. In order to accurately predict the risk of floor water inrush, aiming at the small sample and non-linear problem of floor water inrush, firstly, genetic Algorithm is used to optimize the initial weights and thresholds of network random assignment, and then Sparrow Search Algorithm with strong search ability and good stability is selected to optimize the weights and thresholds for the second time, so as to establish the SSA-GA-BP neural network floor water inrush prediction model. Based on the analysis of geological and hydrological data of Binhu Coal Mine in Shandong Province, 8 factors including water pressure of aquifer, aquifer thickness, aquiclude thickness, fault density, fractal dimension value of fault, permeability coefficient, unit water inflow and floor failure depth are selected as the main control factors to predict floor water inrush, mapping the main controlling factors of 3D surface map projection. The Kriging interpolation method in surfer software is used to extract 50 data points as the input samples of the model(including 40 training sets and 10 test sets). The model is trained and studied. After the training error accuracy meets the requirements, the water inrush risk of 12 data points of 3 unmined working faces in Binhu Coal Mine is predicted. To verify the accuracy of the model, BP, GA-BP and SSA-GA-BP models are used to predict the test set; to avoid the one-sideness of comparing the model only with the prediction of BP network, the Fuzzy Comprehensive Evaluation Method, which determines the weight by Entropy Weight Method, is selected to predict the test set. The prediction results of each network model and method are compared with the actual values for analysis. The results show that the water inrush prediction error of GA-BP neural network model optimized by sparrow search algorithm is smaller, and the prediction accuracy is higher, which provides a scientific theoretical basis for mine water disaster prediction.