Land subsidence risk assessment using GIS fuzzy logic spatial modeling in Varamin aquifer, Iran

The extent of the subsidence and the consequents damage to most of the residential and populated areas of Iran have made this phenomenon one of the most important natural hazards after the earthquake. Accordingly, in this research to assess the land subsidence risk, a GIS fuzzy logic spatial modelin...

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Bibliographic Details
Published in:GeoJournal 2021-06, Vol.86 (3), p.1203-1223
Main Authors: Mohebbi Tafreshi, Ghazaleh, Nakhaei, Mohammad, Lak, Razyeh
Format: Article
Language:English
Subjects:
Abundance
Aggregation
Annual rainfall
Aquifers
Bedrock
Benchmarks
Drawdown
Earthquake damage
Earthquakes
Environmental Management
Fuzzy logic
Geographical information systems
Geography
Geological hazards
Groundwater
Hazard assessment
Human Geography
Land subsidence
Land use
Literature reviews
Lithology
Mathematical models
Modelling
Natural disasters
Parameters
Rain
Rainfall
Risk assessment
Seismic activity
Social Sciences
Soil erosion
Specific yield
Statistical analysis
Subsidence
Transmissivity
Verification
Zonation
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Summary:The extent of the subsidence and the consequents damage to most of the residential and populated areas of Iran have made this phenomenon one of the most important natural hazards after the earthquake. Accordingly, in this research to assess the land subsidence risk, a GIS fuzzy logic spatial modeling was applied. In this regard, four stages were performed. In stage 1, 14 factors affecting subsidence including aquifer thickness, bedrock depth, Debi zonation of pumping wells, transmissivity, specific yield, Groundwater drawdown in 20 year, soil type, slope, altitude based on DEM, erosion, annual rainfall, distance of fault, lithological units, and land use, were prepared based on the literature review. In stage 2, the parameters were standardized with the fuzzy membership functions. Moreover, in stage 3, for aggregation parameters, several fuzzy overlay operation models were used. Finally, to verification of the models, the statistical benchmarks based on observed land subsidence were used. Accordingly, the “GAMMA 0.8” fuzzy overlay model with the most abundance of rank 1, and the “SUM” fuzzy overlay model with the most abundance of rank 16 among the other models are introduced as the most consistent and the worst consistent models with observed land subsidence data, respectively.
ISSN:0343-2521
1572-9893
DOI:10.1007/s10708-019-10129-8