摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑发表了关于人工智能的新研究结果。根据NewsRx记者从伊朗德黑兰发回的新闻报道，研究表明，“自然过程或人类活动引起的地面沉降(LS)会对环境造成不可挽回的损害。本研究采用准永久散射体方法探测2018年至2020年期间有和没有沉降的地区。”新闻记者从K.N.那里获得了研究的一句话。Toosi技术大学：“此外，选取了12个影响沉降的因素进行LS易发区探测，利用信息增益比(IGR)和频率比法确定了影响沉降的各因素和各UB因素的重要性和权重，提出了基于标准自适应网络的模糊推理系统(ANFIS)算法及其与粒子群优化(PSO)算法的集成方法。”利用均方根误差(RMSE)、受试者工作特征曲线下面积(AUROC)和混淆矩阵准则(如敏感性、特异性、精密度、准确度和召回率)对模型的预测性能进行评估,最后利用Shapley加法解释方法探讨特征在建模中的重要性,结果表明:沉降模式为V型,平均321mm/年。地面水准测量和红外合成孔径雷达测量的相关系数为0.93，变形速率S=1.43mm/年。根据IGR分析，介质、地下水位下降和含水层厚度对LS的发生起关键作用。此外，ANFIS-PSO和ANFIS模型对训练数据集的AUROC值分别为0.912和0.807,对测试数据集的AUROC值分别为0.863和0.771.。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New study results on artificial intell igence have been published. According to news reporting originating from Tehran, Iran, by NewsRx correspondents, research stated, “Land subsidence (LS) due to n atural processes or human activity can irreparably damage the environment. This study employed the quasi-permanent scatterer method to detect areas with and wit hout subsidence in the period from 2018 to 2020.” The news correspondents obtained a quote from the research from K.N. Toosi Unive rsity of Technology: “In addition, 12 factors affecting subsidence were selected to detect LS-prone areas. Information gain ratio (IGR) and frequency ratio meth ods were used to determine the importance and weighting of various factors and s ub-factors affecting subsidence. Novel approaches, including the standard adapti ve-networkbased fuzzy inference system (ANFIS) algorithm and its integration wi th the particle swarm optimization (PSO) algorithm, yielded LS maps. The models’ predictive performance was assessed using statistical indexes such as the root mean square error (RMSE), area under the receiver operating characteristic curve (AUROC) and confusion matrix criteria (e.g., sensitivity, specificity, precisio n, accuracy, and recall). Finally, Shapley additive explanations approach was us ed to explore the importance of features in modeling. The findings showed that t he subsidence pattern was V-shaped, averaging 321 mm/year. Ground-leveling and i nterferometric synthetic aperture radar measurements revealed a 0.93 correlation coefficient with a s = 1.43 mm/year deformation rate. Based on IGR analysis, aq uifer media, the decline of the water table, and aquifer thickness played pivota l roles in LS occurrences. In addition, the ANFIS-PSO model classified approxima tely 50.26 % of the study area as high and very high susceptibilit y. The AUROC values of ANFIS-PSO and ANFIS models for the training dataset were 0.912 and 0.807, respectively. For the testing dataset, the ANFIS-PSO model prod uced a higher AUROC value of 0.863, while the ANFIS model had a value of 0.771.”
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