Proof-of-concept (PoC) is presented for a new framework to serve as a proactive capability to mapping subsidence vulnerability of Shabestar plain of approximately 500km2 overlaying an important aquifer supporting a region renowned for diversity of agricultural products. This aquifer is one of 12 in East and West Azerbaijan provinces, Northwest Iran, which surround the distressed Lake Urmia, with its water table declined approximately 4m in between 2004 and 2014. The decline of water table in aquifers undermines their soil texture and structure by exposure to pressures under their weight and thereby induce or trigger land subsidence. Inspired by the DRASTIC framework to map intrinsic aquifer vulnerability to anthropogenic pollution, the paper introduces the ALPRIFT framework for subsidence, which comprises the seven data layers of Aquifer media (A), Land use (L), Pumping of groundwater, Recharge (R), aquifer thickness Impact (I), Fault distance (F) and decline of water Table (T). The paper prescribes rates to account for local variations and weights for the relative importance of the data layers. The proof-of-concept for ALPRIFT is supported by the correlation of Subsidence Vulnerability Indices (SVIs) with measured subsidence values, which renders a value of 0.5 but improves significantly to 0.86 when using fuzzy logic. Similar improvements are suggested by the ROC/AUC performance metric.
Keywords: ALPRIFT; Fuzzy logic; Sentinel-1 InSAR; Shabestar; Subsidence.
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