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Reliability of groundwater vulnerability maps obtained through statistical methods

机译：通过统计方法获得的地下水脆弱性图的可靠性

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Statistical methods are widely used in environmental studies to evaluate natural hazards. Within groundwater vulnerability in particular, statistical methods are used to support decisions about environmental planning and management. The production of vulnerability maps obtained by statistical methods can greatly help decision making. One of the key points in all of these studies is the validation of the model outputs, which is performed through the application of various techniques to analyze the quality and reliability of the final results and to evaluate the model having the best performance. In this study, a groundwater vulnerability assessment to nitrate contamination was performed for the shallow aquifer located in the Province of Milan (Italy). The Weights of Evidence modeling technique was used to generate six model outputs, each one with a different number of input predictive factors. Considering that a vulnerability map is meaningful and useful only if it represents the study area through a limited number of classes with different degrees of vulnerability, the spatial agreement of different reclassified maps has been evaluated through the kappa statistics and a series of validation procedures has been proposed and applied to evaluate the reliability of the reclassified maps. Results show that performance is not directly related to the number of input predictor factors and that is possible to identify, among apparently similar maps, those best representing groundwater vulnerability in the study area. Thus, vulnerability maps generated using statistical modeling techniques have to be carefully handled before they are disseminated. Indeed, the results may appear to be excellent and final maps may perform quite well when, in fact, the depicted spatial distribution of vulnerability is greatly different from the actual one. For this reason, it is necessary to carefully evaluate the obtained results using multiple statistical techniques that are capable of providing quantitative insight into the analysis of the results. This evaluation should be done at least to reduce the questionability of the results and so to limit the number of potential choices.
机译：统计方法广泛用于环境研究，以评估自然灾害。特别是在地下水脆弱性方面，使用统计方法来支持有关环境规划和管理的决策。通过统计方法获得的漏洞图可以极大地帮助决策。所有这些研究的关键点之一是模型输出的验证，这是通过应用各种技术来分析最终结果的质量和可靠性并评估具有最佳性能的模型来进行的。在这项研究中，对位于米兰省（意大利）的浅层含水层进行了地下水对硝酸盐污染的脆弱性评估。证据权重建模技术用于生成六个模型输出，每个输出具有不同数量的输入预测因子。考虑到脆弱性地图只有通过有限数量的具有不同程度的脆弱性的类别来代表研究区域时才有意义并且有用，因此已通过kappa统计数据评估了不同重新分类的地图的空间一致性，并且已经进行了一系列验证程序提出并应用于评估重分类地图的可靠性。结果表明，性能与输入预测因子的数量不直接相关，并且可以在表面相似的地图中识别出最能代表研究区域地下水脆弱性的地图。因此，在传播之前，必须仔细处理使用统计建模技术生成的漏洞图。确实，当所描述的脆弱性空间分布与实际分布有很大差异时，结果似乎是极好的，最终地图可能会表现良好。因此，有必要使用多种统计技术仔细评估获得的结果，这些统计技术能够提供对结果分析的定量洞察力。至少应进行这种评估，以减少结果的可疑性，从而限制潜在选择的数量。

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