Abstract Poor anthropogenic activities give rise to declining water table in aquifers, which in turn induces land subsidence, an issue of concern to planning systems. Modelling subsidence depends on data and this paper considers impacts over plains with sparse data by building on the ALPRIFT framework, the acronym of 7 data layers, similar to DRASTIC. It uses a scoring system of prescribed rates in terms of physical variations and prescribed weights in terms relative importance of each data layer. The conceptual innovation on ALPRIFT by the paper includes its transformation from Vulnerability Indexing (VI) into Risk Indexing (RI) by discerning the data layers into 5 ALRIF and two PT data layers. These cater for Passive Vulnerability Indices (PVI) and Active Vulnerability Indices (AVI) respectively. Their additions are equivalent to ALPRIFT VI but their products lead to innovative RI capabilities. The paper presents the proof-of-concept for RI and learning its inherent weight values by using an innovative hybrid scheme of fuzzy logic and catastrophe theory. The case study of Marand plain provides a challenging case, where the data are sparse but the analysis of the results provides an insight into the study areas. Subsidence RI is readily applicable to any similar problems.

中文翻译：

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使用新的模糊灾难方案将基于 ALPRIFT 的沉降脆弱性指数转化为风险指数

摘要 不良的人为活动导致含水层地下水位下降，进而导致土地沉降，这是规划系统关注的一个问题。建模沉降取决于数据，本文通过建立在 ALPRIFT 框架（7 个数据层的首字母缩写词，类似于 DRASTIC）的基础上，考虑了对具有稀疏数据的平原的影响。它使用物理变化方面的规定比率评分系统和每个数据层的相对重要性方面的规定权重。该论文对ALPRIFT的概念创新包括通过将数据层识别为5个ALRIF和两个PT数据层，将其从漏洞索引（VI）转换为风险索引（RI）。这些分别迎合被动漏洞指数 (PVI) 和主动漏洞指数 (AVI)。他们的新增功能相当于 ALPRIFT VI，但他们的产品带来了创新的 RI 功能。本文介绍了 RI 的概念验证，并通过使用模糊逻辑和突变理论的创新混合方案学习其固有权重值。Marand 平原的案例研究提供了一个具有挑战性的案例，其中数据稀疏，但对结果的分析提供了对研究区域的深入了解。沉降 RI 很容易适用于任何类似的问题。