Evaluating and Zoning Subsidence Risk using MABAC and ANP Adaptive Algorithm (Case Study: Ardabil Plain)

One of the dangers facing Iran’s plains is subsidence. Land subsidence due to various reasons, such as over-harvesting of underground water sources and climate changes, has caused many problems in agricultural lands, roads, and power and energy transmission lines. Therefore, it is important to deal with the causes and influencing factors to control the risks. Ardabil plain is also one of the areas prone to subsidence due to climate changes and the decrease of underground water in recent years. The aim of the present research is the zoning subsidence risk in this plain. First, the factors influencing subsidence in the Ardabil plain (slope, land use, lithology, distance from the fault, distance from the waterway, drop in water level, distance from the city and the village) were identified. Then the layers of data were applied in the geographic information system. In the next stage, the weighting of the investigated factors was done using ANP method and in the Super Decision software, and the final analysis and modeling were done using the MABAC method as one of the multi-criteria decision making methods. Finally, the resulting map was classified into five categories from very low risk to very high risk. The results showed that such factors as water level drop, distance from the river and lithology have the highest weighting factor. Moreover, 244.29 and 370.59 square kilometers, respectively, of the area of this plain, are in very dangerous and dangerous classes. Finally, due to the high potential of Ardabil plain in terms of subsidence, protective and management measures should be taken into consideration by relevant authorities and institutions.