An investigation into time-variant subsidence potentials using inclusive multiple modelling strategies.

Groundwater over-abstraction due to the absence of an effective management plan is often one of the main reasons for land subsidence in aquifer areas. This paper investigates this environmental problem at Salmas plain, Iran, by using the ALPRIFT framework, an acronym of a set of seven general-purpose data layers, introduced recently by the authors. It is capable of mapping Subsidence Vulnerability Indices (SVI) and the paper investigates an innovation to transform it into Time-variant SVI (TSVI) mapping capabilities through a three module strategy: Module 1: maps SVI; Module 2: develops a predictive model for Groundwater Levels (GWL); Module 3: combines both modules to produces TSVI maps. Modules 1 and 2 employ Inclusive Multiple Modelling (IMM) practices, which promote learning from multiple models, as opposed to their ranking and selecting a 'superior' one. IMM is implemented through the same single modelling strategy for both Modules 1 and 2 at two levels: at Level 1, multiple models are constructed by three Fuzzy Logic (FL) models: Sugeno FL (SFL), Mamdani FL (MFL) and Larsen FL (LFL). (ii) At Level 2, FL models at Level 1 are reused by Support Vector Machine (SVM) as the combiner model. The results show that (i) the models at Level 1 are fit-for-purpose; (ii) the models at Level 2 are defensible owing to IMM strategies focussed on enhancing their accuracy and investigating their residuals; and (iii) according to TSVI maps, the north of the plain is vulnerable to hotspot areas and is exposed to subsidence risks due to unplanned over-abstraction of groundwater from the aquifer at Salmas plain.