Abstract

This study attempted to predict ground subsidence occurrence using statistical and machine learning models, specifically the evidential belief function (EBF), index of entropy (IoE), support vector machine (SVM), and random forest (RF) models in the Rafsanjan Plain in southern Iran to investigate 11 possible causative factors: slope percent, aspect, topographic wetness index (TWI), plan and profile curvatures, normalized difference vegetation index (NDVI), land use, lithology, distance to river, groundwater drawdown, and elevation. The Boruta algorithm was applied to determine the importance of the possible causative factors. NDVI, groundwater drawdown, land use, and lithology had the strongest relationships with land subsidence. Finally, we generated land subsidence maps using different machine learning and statistical models. The accuracy of these models was assessed using the AUC value and the true skill statistic (TSS) metrics. The SVM model had the highest prediction accuracy (AUC =0.967, TSS =0.91), followed by RF (AUC =0.936, TSS =0.87), EBF (AUC =0.907, TSS =0.83), and IoE (AUC= 0.88, TSS =0.8).

摘要

这项研究尝试使用统计和机器学习模型来预测地面沉降的发生，特别是在拉夫桑延平原中的证据信念函数（EBF），熵指数（IoE），支持向量机（SVM）和随机森林（RF）模型。伊朗南部地区调查了11种可能的致病因素：坡度百分比，纵横比，地形湿度指数（TWI），平面曲率和剖面曲率，归一化植被指数（NDVI），土地利用，岩性，与河流的距离，地下水抽取量和高程。应用Boruta算法确定可能的致病因素的重要性。NDVI，地下水位下降，土地利用和岩性与地面沉降之间的关系最密切。最后，我们使用不同的机器学习和统计模型生成了地面沉降图。这些模型的准确性是使用AUC值和真实技能统计（TSS）指标进行评估的。SVM模型具有最高的预测准确性（AUC = 0.967，TSS = 0.91），其次是RF（AUC = 0.936，TSS = 0.87），EBF（AUC = 0.907，TSS = 0.83）和IoE（AUC = 0.88，TSS = 0.8）。