Document Type : Research Paper
Authors
1 PhD Student in Water Resources Engineering, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
2 Associate Professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
3 Associate Professor, Department of Pasture and watershed, Faculty Natural Resources, Urmia University, Urmia, Iran
Abstract
Introduction
Salmas Plain represents one of the most critical areas in the country experiencing subsidence. In general, various factors cause land subsidence, but in many areas, the excessive extraction of ground water from aquifers causes land subsidence. The increasing use of ground water, especially in the sites that are accumulated with alluvial deposits, shallow sea or unconsolidated lake, leads to subsidence or collapse of the land. With the excessive extraction of ground water, the water level of the aquifer decreases and the hydrostatic pressure decreases, which makes it possible for the land to subside gradually. Subsidence in plains mostly occurs due to this factor, namely excessive groundwater extraction and compaction of clay and silt layers between aquifers. In this case, even if the water table level rises again, the land cannot return to its original level.
Materials and methods
In this study, the susceptibility of land subsidence in Salmas Plain was investigated using layers of influential factors in subsidence with ArcGIS software and fuzzy logic. In the first stage, statistical information on some factors causing subsidence, including groundwater level decline, well extraction rate, aquifer storage coefficient, transmissivity coefficient, precipitation, DEM map, soil texture, and bedrock depth, was collected and raster maps of each of these factors at the aquifer level were prepared. In the next stage, fuzzy layering was performed using fuzzy membership functions based on the impact of decreasing or increasing each of these factors on land subsidence. Subsequently, the maps were combined using fuzzy operators (Gamma OR, AND, SUM, PRODUCT) to obtain a unified map of aquifer subsidence susceptibility. Finally, to select the best combination of operators, the results were compared and evaluated with field observation data and the ROC curve performance index.
Results and discussion
The results showed that the OR operator had the lowest conformity with observed subsidence in the area with an AUC of 0.693. Gamma operators with an AUC above 70% had the highest overlap or conformity with observed subsidence in the plain. In this study, the Gamma 0.9 operator was selected as the best fuzzy operator with an AUC of 0.805. The results indicate that the eastern part of the aquifer is critical in terms of subsidence. Approximately 25% of the total area of Salmas Plain, equivalent to 93 square kilometers, has subsidence with very high susceptibility.
Conclusion
Based on the results obtained, it can be said that although the AUC value of the fuzzy operator sum is higher, the Gamma operator with a value of 0.9 has the highest conformity with the ground reality on the fuzzy map, even though it has a lower AUC value. It is essential to mention that the minimum operator AND and Product create a region with low susceptibility, while the maximum operator OR and SUM maximize the susceptible area. They cannot achieve satisfactory performance in preparing a subsidence susceptibility map. Here, they have only been used to demonstrate the inefficiency of fuzzy operators in maximizing or minimizing subsidence susceptibility.
Keywords
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