Journal
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
Volume 34, Issue 7, Pages 1059-1087Publisher
SPRINGER
DOI: 10.1007/s00477-020-01810-3
Keywords
Fuzzy-artificial neural network (F-ANN); Fuzzy-gene expression programming (F-GEP); GIS; Land subsidence; Varamin
Categories
Funding
- Research Institute for Earth Sciences (RIES), Geological Survey of Iran (GSI) [98-P-T-114]
Ask authors/readers for more resources
Land subsidence is a complicated hazard that artificial intelligence models can model it without approximation and simplification. In this study, for the first time in land subsidence studies, we used and compared the accuracy and efficiency of hybrid fuzzy-gene expression programming (F-GEP) and fuzzy-artificial neural network (F-ANN) models in estimating land subsidence susceptibility modeling in Varamin aquifer of Iran. For this purpose, after selecting and gathering information from fifteen geo-environmental and hydrogeological effectual factors including specific yield, erosion, aquifer thickness, distance of fault, bedrock level, digital elevation model (DEM), annual rainfall, clay thickness, transmissivity (T), soil type, Debi zonation of pumping wells, slope based on DEM, groundwater drawdown in 20 years, land use, and lithological units event based on literature review in the GIS environment, they were first standardized with GIS fuzzy membership functions, and then GEP model was used to integrate the layers. For this step, using 70% of the data (2919 pixels) for the train and 30% (1251 pixels) for the test. Finally, using several statistical criteria and radar image data, the models were validated. We repeat the model on ANN, and our results showed that F-GEP model (with R-2 = 0.99 and RMSE = 0.004) is more accurate than F-ANN model (with R-2 = 0.94 and RMSE = 0.056) for land subsidence susceptibility modeling in the study area.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available