Subsidence risk zoning in Varamin County based on effective criteria using TOPSIS and VIKOR techniques
Subject Areas : EnvironmentAli Taheri 1 , Moslem Dehnavi Eelagh 2
1 - Master's student of Geospatial Information Systems, School of Surveying Engineering and Geospatial Information College of Engineering, University of Tehran
2 - PhD student of Geospatial Information Systems, School of Surveying Engineering and Geospatial Information, College of Engineering, University of Tehran, Tehran, Iran
Keywords: TOPSIS, VIKOR, Subsidence, DINSAR, Sentinel-1,
Abstract :
Background and objective: Subsidence is a crisis that modern societies are currently facing. It has the potential to inflict irreparable damage to the lives and properties of residents, as well as disrupt urban infrastructure, including water, oil, and gas transmission lines. While horizontal displacement is also possible, its extent is typically minor. Subsidence results in the formation of cracks and fissures in the ground, alterations in underground water quality, changes to the Earth's surface topography, and other related issues.Materials and methods: In this study, using the multi-criteria decision-making approach, the seven criteria have been taken into account to produce subsidence risk map. At first, expert opinion on this issue have been used to investigate the effect of different criteria on subsidence. Then the weight of each criterion was obtained using the geometric mean method. Then to combine the layers, VIKOR and TOPSIS fusion techniques were used. To evaluate the implemented method, Sentinel 1 radar images were used to prepare a subsidence map, and a comparison between the two maps has been made.Results and conclusion: The analysis indicated that land use, underground water, and rainfall had the most significant influence on subsidence, with weights of 0.4292, 0.2699, and 0.1473, respectively. In contrast, slope and elevation had the least impact, with weights of 0.0220 and 0.0375, respectively. A subsidence map was successfully produced using Sentinel-1 images and Differential Interferometric Synthetic-Aperture Radar (DInSAR) techniques, and this map was compared to those obtained through VIKOR and TOPSIS methods, demonstrating a favorable level of compatibility.
Abdollahi, S., Pourghasemi, H. R., Ghanbarian, G. A., & Safaeian, R. (2019). Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions. Bulletin of Engineering Geology and the Environment, 78(6), 4017-4034. https://doi.org/10.1007/s10064-018-1403-6
Abedini, M., Aghayary, l., & Asghari Saraskanrood, S. (2023). Evaluating and Zoning Subsidence Risk using MABAC and ANP Adaptive Algorithm (Case Study: Ardabil Plain). Journal of Geography and Environmental Hazards, 11(4), 43-68. https://doi.org/10.22067/geoeh.2022.74202.1143
Anderssohn, J., Wetzel, H.-U., Walter, T. R., Motagh, M., Djamour, Y., & Kaufmann, H. (2008). Land subsidence pattern controlled by old alpine basement faults in the Kashmar Valley, northeast Iran: results from InSAR and levelling. Geophysical Journal International, 174(1), 287-294. https://doi.org/10.1111/j.1365-246X.2008.03805.x
Arabameri, A., Pal, S. C., Rezaie, F., Chakrabortty, R., Chowdhuri, I., Blaschke, T., & Ngo, P. T. T. (2021). Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation. Journal of Environmental Management, 284, 112067. https://doi.org/10.1016/j.jenvman.2021.112067
Calderhead, A. I., Therrien, R., Rivera, A., Martel, R., & Garfias, J. (2011). Simulating pumping-induced regional land subsidence with the use of InSAR and field data in the Toluca Valley, Mexico. Advances in Water Resources, 34(1), 83-97. https://doi.org/10.1016/j.advwatres.2010.09.017
Dehghani, M., Zoej, M. J. V., Hooper, A., Hanssen, R. F., Entezam, I., & Saatchi, S. (2013). Hybrid conventional and persistent scatterer SAR interferometry for land subsidence monitoring in the Tehran Basin, Iran. ISPRS journal of photogrammetry and remote sensing, 79, 157-170. https://doi.org/10.1016/j.isprsjprs.2013.02.012
Ebrahimy, H., Feizizadeh, B., Salmani, S., & Azadi, H. (2020). A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods. Environmental Earth Sciences, 79, 1-12. https://doi.org/10.1007/s12665-020-08953-0
Galloway, D. L., Jones, D. R., & Ingebritsen, S. E. (1999). Land subsidence in the United States (Vol. 1182). US Geological Survey.
Galve, J., Gutiérrez, F., Lucha, P., Guerrero, J., Bonachea, J., Remondo, J., & Cendrero, A. (2009). Probabilistic sinkhole modelling for hazard assessment. Earth Surface Processes and Landforms, 34(3), 437-452. https://doi.org/10.1002/esp.1753
Herrera-García, G., Ezquerro, P., Tomás, R., Béjar-Pizarro, M., López-Vinielles, J., Rossi, M., Mateos, R. M., Carreón-Freyre, D., Lambert, J., & Teatini, P. (2021). Mapping the global threat of land subsidence. Science, 371(6524), 34-36. https://doi.org/10.1126/science.abb8549
Hwang, C.-L., Yoon, K., Hwang, C.-L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey, 58-191. https://doi.org/10.1007/978-3-642-48318-9-3
Kidanu, S. T., Anderson, N. L., & Rogers, J. D. (2018). Using GIS-based spatial analysis to determine factors influencing the formation of sinkholes in Greene County, Missouri. Environmental & Engineering Geoscience, 24(3), 251-261. https://doi.org/10.2113/EEG-2014
Lee, S., Park, I., & Choi, J.-K. (2012). Spatial Prediction of Ground Subsidence Susceptibility Using an Artificial Neural Network. Environmental Management, 49(2), 347-358. https://doi.org/10.1007/s00267-011-9766-5
Mehrnoor, S., Robati, M., Kheirkhah Zarkesh, M. M., Farsad, F., & Baikpour, S. (2023). Land Subsidence Hazard Zoning in Hashtgerd Plain based on Integrated Multi-Criteria Decision-Making Approach: WOI-BWM. Journal of Geography and Environmental Hazards, 11(4), 127-148. https://doi.org/10.22067/geoeh.2022.75445.1188
Mohammady, M., Pourghasemi, H. R., & Amiri, M. (2019). Land subsidence susceptibility assessment using random forest machine learning algorithm. Environmental Earth Sciences, 78, 1-12. https://doi.org/10.1007/s12665-019-8518-3
Mohammady, M., Pourghasemi, H. R., Amiri, M., & Tiefenbacher, J. P. (2021). Spatial modeling of susceptibility to subsidence using machine learning techniques. Stochastic Environmental Research and Risk Assessment, 1-12. https://doi.org/10.1007/s00477-020-01967-x
Motagh, M., Djamour, Y., Walter, T. R., Wetzel, H.-U., Zschau, J., & Arabi, S. (2007). Land subsidence in Mashhad Valley, northeast Iran: results from InSAR, levelling and GPS. Geophysical Journal International, 168(2), 518-526. https://doi.org/10.1111/j.1365-246X.2006.03246.x
Nadiri, A. A., Moazamnia, M., Sadeghfam, S., & Barzegar, R. (2021). Mapping Risk to Land Subsidence: Developing a Two-Level Modeling Strategy by Combining Multi-Criteria Decision-Making and Artificial Intelligence Techniques. Water, 13(19), 2622. https://doi.org/10.3390/w13192622
Najafi, Z., Pourghasemi, H. R., Ghanbarian, G., & Fallah Shamsi, S. R. (2020). Land-subsidence susceptibility zonation using remote sensing, GIS, and probability models in a Google Earth Engine platform. Environmental Earth Sciences, 79, 1-16. https://doi.org/10.1007/s12665-020-09238-2
Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of civil engineering, Belgrade, 2(1), 5-21.
Pacheco, J., Arzate, J., Rojas, E., Arroyo, M., Yutsis, V., & Ochoa, G. (2006). Delimitation of ground failure zones due to land subsidence using gravity data and finite element modeling in the Querétaro valley, México. Engineering Geology, 84(3-4), 143-160. https://doi.org/10.1016/j.enggeo.2005.12.003
Tien Bui, D., Shahabi, H., Shirzadi, A., Chapi, K., Pradhan, B., Chen, W., Khosravi, K., Panahi, M., Bin Ahmad, B., & Saro, L. (2018). Land subsidence susceptibility mapping in South Korea using machine learning algorithms. Sensors, 18(8), 2464. https://doi.org/10.3390/s18082464
Tomás, R., Romero, R., Mulas, J., Marturià, J. J., Mallorquí, J. J., López-Sánchez, J. M., Herrera, G., Gutiérrez, F., González, P. J., & Fernández, J. (2014). Radar interferometry techniques for the study of ground subsidence phenomena: a review of practical issues through cases in Spain. Environmental Earth Sciences, 71, 163-181. https://doi.org/10.1007/s12665-013-2422-z
Toomanian, A., Kakroodi, A., & Etemadi, M. A. Spatial Modeling of Land Subsidence Using GIS-Based Machine Learning Algorithms. Available at SSRN 4024231. https://dx.doi.org/10.2139/ssrn.4024231
Water, M. G. (2000). Land Subsidence in the United States.