With the rise of machine learning and artificial intelligence, back propagation (BP) neural network, support vector machine (SVM), random forest model, and others can be used to predict the distribution of groundwater. By using the existing sample data, learning, training, and forecasting for some unknown areas (unmanned areas, areas where people are not easy to reach, etc.) can save costs and improve the efficiency of machine learning. This paper took an area of 2000 km² in the southeast of Liaoning Province as the study area. This study used convolutional neural network (CNN) for data training and testing based on the results of groundwater assessment by remote sensing with lithology index, relief index, slope index, water density index, vegetation fraction index, soil humidity index, and land temperature index and field survey data and data of wells. With the coupling relationship between the results of groundwater potential assessment–based AHP and groundwater spatial distribution, the prediction model of groundwater distribution is established. Finally, after 1000 times of training, a good prediction model with the training set of 100% accurate and the test set of about 80% accurate was obtained. Subsequently, a ROC curve was done by using the survey data of the study and the results of prediction (one or zero) of the CNN model. The ROC curve showed that the AUC was 0.854, and the standard error was 0.08. Thus, the groundwater situation in the unsurveyed areas can be predicted by this model to guide the development and utilization of groundwater in the future.

中文翻译：

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卷积神经网络在遥感预测地下水潜力中的应用：以辽宁东南为例

随着机器学习和人工智能的兴起，反向传播（BP）神经网络，支持向量机（SVM），随机森林模型等可用于预测地下水的分布。通过使用现有的样本数据，对一些未知区域（无人驾驶区域，人员不易到达的区域等）的学习，培训和预测可以节省成本并提高机器学习的效率。该论文占地2000 km ²以辽宁省东南为研究区。这项研究基于卷积神经网络的遥感遥感评估结果，使用卷积神经网络（CNN）进行数据训练和测试，包括岩性指数，浮雕指数，坡度指数，水密度指数，植被分数指数，土壤湿度指数和地温指数实地调查数据和油井数据。通过基于地下水潜力评估的层次分析法结果与地下水空间分布之间的耦合关系，建立了地下水分布预测模型。最终，经过1000次训练，获得了一个良好的预测模型，其训练集的准确度为100％，测试集的准确度约为80％。后来，通过使用研究的调查数据和CNN模型的预测结果（一或零）绘制出ROC曲线。ROC曲线显示AUC为0.854，标准误为0.08。因此，该模型可以预测未调查地区的地下水状况，指导今后的地下水开发利用。