Identification of villages at risk of subsidence in Ardabil plain using fuzzy-network analysis in GIS
Subject Areas : Applications in water resources managementBahram Imani 1 , Jafar Jafarzadeh 2
1 - Associate Professor, Department of Geography and Rural Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran
2 - Instructor, Department of Geography and Rural and Urban Planning, Faculty of Social Sciences, University of Mohaghegh Ardabili, Iran
Keywords: Subsidence, Ardabil Plain, fuzzy analysis, Aquifer, Geographic Information System (GIS),
Abstract :
Background and Objective In recent years, due to climate change and drought, as well as the lack of supervision in digging agricultural wells, many of the country's aquifers have been harvested improperly, which has led to a negative balance of these aquifers to the extent that, according to the Geological Survey, most of the country's plains have experienced a drop in groundwater levels. Today, the study, planning and planning to reduce the risks of natural hazards is one of the main issues of officials and planners of countries. One of the hazards that are less considered due to gradual performance is the phenomenon of subsidence, which in recent years due to increased use of important aquifers in the plains of the country has become a pervasive problem. In this research, an attempt has been made to investigate the possibility of subsidence and its possible dangers as a threat to human projects as well as rural settlements in the Ardabil plain.tpMaterials and Methods The study area of Ardabil plain is located between the latitude of thirty-eight degrees and thirty minutes north latitude to thirty-eight degrees and thirty minutes and the longitude of east geography forty-eight degrees and fifteen minutes to forty-eight degrees and thirty-five minutes in the northwestern part of Ardabil province. To investigate the groundwater status of the plain, data from 38 observation wells prepared by the Ardabil Regional Water Organization and located at the plain level have been used. First, using 30-year statistics of 65 observation wells and GIS, a water potential drop map for the region was prepared. Then, using fuzzy Dematel model, experts' opinions were collected and modeled. This method is one of the conceptual methods for structuring decision problems. The Dematel technique is based on graph theory, and in this way we can divide the criteria into two groups of cause and effect criteria to gain a better understanding of the cause-and-effect relationship and finally be able to create a network of interrelationships. Finally, after creating the general relationship matrix and according to the defined threshold size, we create the final relationship matrix in which the number zero means no relationship and the number one means the relationship between two criteria. Using the final relations matrix, we conduct a survey of experts on the extent to which factors affect each other with respect to their interdependence. After the data was obtained from the relevant organizations, a database was created for the information in the ArcCatalog software environment, and then maps related to this data were created in ArcGIS software. After the weights of the different layers were obtained using the fuzzy network analysis method, they entered the ArcGIS software and multiplied the weights of each sub-criterion in the map we created for each layer and finally gathered the maps together to get a final map Came. The final map shows the areas of Ardabil plain that are classified in terms of subsidence risk and in five categories in terms of danger status were shown with color spectrum. Then, the area with severe water loss was selected and compared with the scattering map of deep wells. In the last step, using advanced and fuzzy hybrid models and network analysis in the software environment of GIS, each of the layers of sediment sensitivity and water level drop membership is determined and using fuzzy linear overlap, the area sensitivity map to subsidence in five classes of very sensitivity High, high sensitivity, medium sensitivity, low sensitivity and very low sensitivity were prepared. To prepare the final map of subsidence risk status in Ardabil plain, first the obtained weights for each sub-factor were multiplied in the fuzzy maps of that sub-factor and then these weighted maps were aggregated using the Raster Calculator tool. The final fuzzy map of subsidence risk assessment of Ardabil plain is changing with the color spectrum changes from blue, which represents the lowest, to red, which represents the highest. The blue color indicates low risk areas and the red color indicates high risk areas in terms of subsidence risk in Ardabil plain and villages located in this area.Results and Discussion After obtaining the initial plan to assess the subsidence risk status and areas at risk of subsidence in Ardabil plain, the final map to assess the status of Ardabil plain in terms of land subsidence risk has been prepared according to the weights and layers obtained. Since all the base layer maps were reclassified into five layers and the weight corresponding to each layer was given according to the condition of the layers, the final map was classified and weighted into five layers, which according to experts and professors is as follows; 1) Low risk areas, 2) Medium risk areas, 3) High risk, 4) Damaged areas, 5) Critical areas. Finally, using the final map of Ardabil plain subsidence risk assessment, as well as the ranking obtained from the opinions of relevant experts, the final map of Ardabil plain subsidence risk analysis was prepared. Also, the map of deep wells in Ardabil plain and its distribution in rural areas, it can be seen that the highest distribution and concentration of deep wells in the eastern part of the plain is in Wilkij e Markazi and Fooladloo e Shomali villages. This situation shows the scattering position of deep wells showing the proportionality of the scattering of deep wells in areas at risk of subsidence.Conclusion Wilkij e Markazi, Fooladloo e Shomali, and Fooladloo e Sharghi have the highest levels of vulnerability in terms of subsidence risk status. The critical situation of landslide risk is the highest in these three villages. Also, Kalkhoran and Aghbalagh Aqajan Khan villages are moving from a moderate to a vulnerable situation, which requires more care in managing and planning the water resources of these villages. Also, there is a strong relationship between the distribution of deep wells in the Ardabil plain and areas at high risk of subsidence. Also, according to the results obtained the groundwater status sub-criterion with a weight of 0.38 has the greatest impact on the subsidence risk of Ardabil plain. This weight shows the high impact of this sub-criterion by examining other layers related to groundwater status and population dispersion layer.
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Zhou X, Shi Y, Deng X, Deng Y. 2017. D-DEMATEL: A new method to identify critical success factors in emergency management. Safety Science, 91: 93-104. doi:https://doi.org/10.1016/j.ssci.2016.06.014.
_||_Abedini M. 2013. Investigating the causes of subsidence in Ardabil plain and its effects in the city. Natural Geography, 6(19): 71-84. https://www.sid.ir/en/journal/ViewPaper.aspx?id=222459. (In Persian).
Agricultural Statistics of Ardabil City (ASAC). 2019. Ardabil Agricultural Jihad Organization. Administration. Water and soil management. 130 p. (In Persian).
Arvin A, Vahabzadeh G, Mousavi SR, Bakhtyari Kia M. 2019. Geospatial modeling of land subsidence in the south of the Minab watershed using remote sensing and GIS. Journal of RS and GIS for Natural Resources (Journal of RS and GIS for Natural Resources), 10(3): 19-34. https://girs.bushehr.iau.ir/article_668468.html?lang=en. (In Persian).
Asghari Saraskanroud S, Ghale E, Ebady E. 2021. Investigation of land use changes and its relationship with groundwater level (Case study: Ardabil plain). Journal of RS and GIS for Natural Resources, 12(1): 86-106. doi:http://dorl.net/dor/20.1001.1.26767082.1400.12.1.5.6. (In Persian).
Bayer B, Simoni A, Mulas M, Corsini A, Schmidt D. 2018. Deformation responses of slow moving landslides to seasonal rainfall in the Northern Apennines, measured by InSAR. Geomorphology, 308: 293-306. doi:https://doi.org/10.1016/j.geomorph.2018.02.020.
Daneshvar Vousoughi F, Dinpashoh Y, Aalami M. 2011. Effect of drought on groundwater level in the past two decades (Case study: Ardebil Plain). Water and Soil Science, 21(4): 165-179. (In Persian).
Ekbal H, Wright TJ, Walters RJ, Bekaert DPS, Lloyd R, Hooper A. 2018. Constant strain accumulation rate between major earthquakes on the North Anatolian Fault. Nature Communications, 9(1): 1392. doi:https://doi.org/10.1038/s41467-018-03739-2.
Esfandiari F, Ghorbani Filabadi R, Nasiri Khiavi A, Mostafazadeh R. 2019. Assessing the accuracy of algebraic and geostatistical techniques to determine the spatial variations of groundwater quality in Boroojen Plain. Journal of Natural Environmental Hazards, 8(20): 115-130. doi:https://doi.org/10.22111/jneh.2018.22500.1335.
Gharechelou S, Akbari Ghoochani H, Golian S, Ganji K. 2021. Evaluation of land subsidence relationship with groundwater depletion using Sentinel-1 and ALOS-1 radar data (Case study: Mashhad plain). Journal of RS and GIS for Natural Resources, 12(3): 40-61. doi:http://dorl.net/dor/20.1001.1.26767082.1400.12.3.3.8. (In Persian).
Haji Hosseinlou h. 2018. Assessing the decreasein the level of ground water table using geographic information system (GIS) (Case study: Khoy plain aquifer). Journal of Geography and Environmental Hazards, 7(2): 53-74. doi:https://doi.org/10.22067/geo.v7i2.67365. (In Persian).
Imani B, Jafarzadeh J. 2022. Identification of villages at risk of subsidence in Ardabil plain using fuzzy-network analysis in GIS. Journal of RS and GIS for Natural Resources, 13(2): 15-18. doi:http://dorl.net/dor/20.1001.1.26767082.1401.13.2.4.4. (In Persian).
Intrieri E, Raspini F, Fumagalli A, Lu P, Del Conte S, Farina P, Allievi J, Ferretti A, Casagli N. 2018. The Maoxian landslide as seen from space: detecting precursors of failure with Sentinel-1 data. Landslides, 15(1): 123-133. doi:https://doi.org/10.1007/s10346-017-0915-7.
Jafarzadeh J, Rostamzadeh H, Asadi E. 2017. Modeling temporal of groundwater level using basic techniques of time series analysis (Case study: Ardabil plain). Water and Soil Science, 27(4): 185-196. (In Persian).
Jafarzadeh J, Rostamzadeh H, Nikjoo M, Asadi E. 2017. Potential assessment of available water resources of Ardabil plain using fuzzy analytic network process (FANP) in GIS. Geography and Planning, 21(61): 145-164. (In Persian).
Junfei C, Yang Y. 2011. A fuzzy ANP-based approach to evaluate region agricultural drought risk. Procedia Engineering, 23: 822-827. doi:https://doi.org/10.1016/j.proeng.2011.11.2588.
Li YP, Huang GH, Nie SL. 2010. Planning water resources management systems using a fuzzy-boundary interval-stochastic programming method. Advances in Water Resources, 33(9): 1105-1117. doi:https://doi.org/10.1016/j.advwatres.2010.06.015.
Mokhtari D, Ebrahimy H, Salmani S. 2019. Land subsidence susceptibility modeling using random forest approach (Case study: Tasuj plane catchment). Journal of RS and GIS for Natural Resources, 10(3): 93-105. doi:https://girs.bushehr.iau.ir/article_668475.html?lang=en. (In Persian).
Nas B, Berktay A. 2010. Groundwater quality mapping in urban groundwater using GIS. Environmental Monitoring and Assessment, 160(1): 215-227. doi:https://doi.org/10.1007/s10661-008-0689-4.
Rostamzadeh H, Asadi E, Jararzadeh J. 2015. Evaluation of the groundwater table using multi-criteria decision making and spatial analysis, case study: Ardebil plain. Journal of Spatial Analysis Environmental Hazarts, 2(1): 31-42. doi:https://doi.org/10.18869/acadpub.jsaeh.2.1.31. (In Persian).
Saaty TL. 2007. Multi-decisions decision-making: In addition to wheeling and dealing, our national political bodies need a formal approach for prioritization. Mathematical and Computer Modelling, 46(7): 1001-1016. doi:https://doi.org/10.1016/j.mcm.2007.03.023.
Shahid SU, Iqbal J, Hasnain G. 2014. Groundwater quality assessment and its correlation with gastroenteritis using GIS: a case study of Rawal Town, Rawalpindi, Pakistan. Environmental Monitoring and Assessment, 186(11): 7525-7537. doi:https://doi.org/10.1007/s10661-014-3945-9.
Shieh J-I, Wu H-H, Huang K-K. 2010. A DEMATEL method in identifying key success factors of hospital service quality. Knowledge-Based Systems, 23(3): 277-282. doi:https://doi.org/10.1016/j.knosys.2010.01.013.
Shrestha S, Nakamura T, Magome J, Aihara Y, Kondo N, Haramoto E, Malla B, Shindo J, Nishida K. 2018. Groundwater use and diarrhoea in urban Nepal: novel application of a geostatistical interpolation technique linking environmental and epidemiologic survey data. International Health, 10(5): 324-332.
Tzeng G-H, Chiang C-H, Li C-W. 2007. Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert Systems with Applications, 32(4): 1028-1044. doi:https://doi.org/10.1016/j.eswa.2006.02.004.
Wang Y-J. 2020. Combining quality function deployment with simple additive weighting for interval-valued fuzzy multi-criteria decision-making with dependent evaluation criteria. Soft Computing, 24(10): 7757-7767. doi:https://doi.org/10.1007/s00500-019-04394-5.
Wu W-W. 2008. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach. Expert Systems with Applications, 35(3): 828-835. doi:https://doi.org/10.1016/j.eswa.2007.07.025.
Zhou Q, Huang W, Zhang Y. 2011. Identifying critical success factors in emergency management using a fuzzy DEMATEL method. Safety Science, 49(2): 243-252. doi:https://doi.org/10.1016/j.ssci.2010.08.005.
Zhou X, Shi Y, Deng X, Deng Y. 2017. D-DEMATEL: A new method to identify critical success factors in emergency management. Safety Science, 91: 93-104. doi:https://doi.org/10.1016/j.ssci.2016.06.014.