Transforming subsidence vulnerability indexing based on ALPRIFT into risk indexing using a new fuzzy-catastrophe scheme

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.