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Sinkhole susceptibility mapping in Marion County Florida: Evaluation and comparison between analytical hierarchy process and logistic regression based approaches

机译：佛罗里达州马里恩县的污水池敏感性图：基于层次分析法和逻辑回归的方法的评估和比较

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Sinkholes are the major cause of concern in Florida for their direct role on aquifer vulnerability and potential loss of lives and property. Mapping sinkhole susceptibility is critical to mitigating these consequences by adopting strategic changes to land use practices. We compared the analytical hierarchy process (AHP) based and logistic regression (LR) based approaches to map the areas prone to sinkhole activity in Marion County, Florida by using long-term sinkhole incident report dataset. For this study, the LR based model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.8 compared to 0.73 with the AHP based model. Both models performed better when an independent future sinkhole dataset was used for validation. The LR based approach showed a low presence of sinkholes in the very low susceptibility class and low absence of sinkholes in the very high susceptibility class. However, the AHP based model detected sinkhole presence by allocating more area to the high and very high susceptibility classes. For instance, areas susceptible to very high and high sinkhole incidents covered almost 43.4% of the total area under the AHP based approach, whereas the LR based approach allocated 20.7% of the total area to high and very high susceptibility classes. Of the predisposing factors studied, the LR method revealed that closeness to topographic depression was the most important factor for sinkhole susceptibility. Both models classified Ocala city, a populous city of the study area, as being very vulnerable to sinkhole hazard. Using a common test case scenario, this study discusses the applicability and potential limitations of these sinkhole susceptibility mapping approaches in central Florida.
机译：在佛罗里达州，污水池是引起人们关注的主要原因，因为污水池对含水层的脆弱性以及潜在的生命和财产损失具有直接作用。绘制下沉敏感性图对于通过对土地利用实践进行战略性改变来减轻这些后果至关重要。我们比较了基于分析层次过程（AHP）和基于逻辑回归（LR）的方法，以通过使用长期下沉事件报告数据集来绘制佛罗里达州马里恩县容易发生下沉活动的区域。对于本研究，与基于AHP的模型的0.73相比，基于LR的模型在接收器工作特性（ROC）曲线下的面积为0.8时更为准确。当使用独立的未来污水坑数据集进行验证时，两个模型的性能都更好。基于LR的方法在极低的敏感性等级中显示出较低的沉陷孔，而在极高的敏感性等级中显示中不存在沉孔。但是，基于AHP的模型通过将更多区域分配给高和非常高的磁化率类别来检测到沉孔的存在。例如，在基于AHP的方法中，易发生极高和高度的沉井事故的区域几乎覆盖了总面积的43.4％，而基于LR的方法将总面积的20.7％分配给了高和非常高的磁化率等级。在研究的诱发因素中，LR方法表明，与地形凹陷的接近程度是造成沉陷敏感性的最重要因素。两种模型都将奥卡拉（Ocala）市（研究区域人口稠密的城市）归类为极易遭受坑洼危害的城市。本研究使用一个常见的测试用例场景，讨论了佛罗里达州中部这些下沉敏感性地图绘制方法的适用性和潜在局限性。

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