Abstract
Land subsidence is a morphological phenomenon, which causes negative environmental and economic consequences for human societies. Therefore, identifying the areas prone to subsidence can be one of the necessary measures for reducing the potential risks. This study aims to evaluate the efficiency of the support vector machine (SVM) algorithm and weighted overlay index (WOI) models in zoning the rate of land subsidence hazard in Hashtgerd plain, Iran. First, the 19 criteria include groundwater depletion, groundwater extraction, aquifer thickness, alluvium thickness, aquifer recharge, well density, drainage density, groundwater depth, lithology, bedrock depth, average annual precipitation, average annual temperature, climate type, agricultural use, urban use, industrial use, distance from rivers and streams, distance from roads, distance from faults were considered. Then, the layers were weighed based on the Best–Worst Method (BWM). The results of BWM indicated that the factors of groundwater extraction (0.219), lithology (0.157), and groundwater depletion (0.079) have a greater effect on the potential for subsidence hazard. Moreover, the results of validation by performing ROC curve showed that the accuracy of RBF-SVM, LN-SVM, SIG-SVM, PL-SVM, and WOI were 95.7, 94.3, 94.9, 93.2, and 90%, respectively. Based on the ROC results, all of the models for preparing the subsidence hazard map in Hashtgerd plain exhibit excellent accuracy. Therefore, all of the models used here can predict the areas vulnerable to subsidence properly. In this study, the five land subsidence hazard maps were used as new input factors and integrated using fuzzy gamma-ensemble methods to make better hazard maps. The results of the ensemble model indicated that 19.3% of Hashtgerd plain is in the zone of high to very high sensitivity. The results of this study can help planners in managing and reducing the possible hazards of subsidence.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Not applicable.
Code availability
Not applicable.
References
Abdollahi S, Pourghasemi HR, Ghanbarian GA, 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. Bull Eng Geol Environ 78(6):4017–4034. https://doi.org/10.1007/s10064-018-1403-6
Abidin HZ, Andreas H, Gumilar I, Brinkman JJ (2015) Study on the risk and impacts of land subsidence in Jakarta. Proc Int Assoc Hydrol Sci 372:115–120. https://doi.org/10.5194/piahs-372-115-2015
Aditiya A, Takeuchi W, Aoki Y (2017) Land Subsidence Monitoring by InSAR Time Series Technique Derived From ALOS-2 PALSAR-2 over Surabaya City, Indonesia. In: IOP Conference Series: Earth and Environmental Science 98(1): 012010, https://iopscience.iop.org/article/https://doi.org/10.1088/1755-1315/98/1/012010/meta
Agarwal R, Garg PK (2016) Remote sensing and GIS based groundwater potential & recharge zones mapping using multi-criteria decision making technique. Water Resour Manage 30(1):243–260. https://doi.org/10.1007/s11269-015-1159-8
Arabameri A, Saha S, Roy J, Tiefenbacher JP, Cerda A, Biggs T, Collins AL (2020) A novel ensemble computational intelligence approach for the spatial prediction of land subsidence susceptibility. Sci Total Environ 726:138595. https://doi.org/10.1016/j.scitotenv.2020.138595
Arabameri A, Lee S, Rezaie F, Chandra Pal S, Asadi Nalivan O, Saha A, Moayedi H (2021a) Performance evaluation of GIS-based novel ensemble approaches for land subsidence susceptibility mapping. Front Earth Sci 9:307. https://doi.org/10.3389/feart.2021.663678
Arabameri A, Pal SC, Rezaie F, Chakrabortty R, Chowdhuri I, Blaschke T, Ngo PTT (2021b) Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation. J Environ Manage 284:112067. https://doi.org/10.1016/j.jenvman.2021.112067
Bhavsar H, Ganatra A (2014) Increasing efficiency of support vector machine using the novel kernel function: combination of polynomial and radial basis function. Int J Adv Comp Theory Eng (IJACTE) 3:17–24
Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Comput Method Geosci 13:398
Chen W, Chai H, Zhao Z, Wang Q, Hong H (2016) Landslide susceptibility mapping based on GIS and support vector machine models for the Qianyang County. China Environ Earth Sci 75(6):474
Chen W, Li H, Hou E, Wang S, Wang G, Panahi M, Li T, Peng T, Guo C, Niu C, Xiao L (2018) GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models. Sci Total Environ 634:853–867. https://doi.org/10.1016/j.scitotenv.2018.04.055
Conforti M, Muto F, Rago V, Critelli S (2014) Landslide inventory map of north-eastern Calabria (South Italy). J Maps 10(1):90–102. https://doi.org/10.1080/17445647.2013.852142
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. Environ Earth Sci 79:1–12. https://doi.org/10.1007/s12665-020-08953-0
Elmahdy SI, Mohamed MM, Ali TA, Abdalla JED, Abouleish M (2020) Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain. UAE Geocarto Inter. https://doi.org/10.1080/10106049.2020.1716398
Fabris M, Cenni N, Fiaschi S (2021) Monitoring land subsidence using remote sensing. Remote Sensing 13(9):1771–17676. https://doi.org/10.3390/rs13091771
Fiaschi S, Fabris M, Floris M, Achilli V (2018) Estimation of land subsidence in deltaic areas through differential SAR interferometry: the Po River Delta case study (Northeast Italy). Int J Remote Sens 39(23):8724–8745. https://doi.org/10.1080/01431161.2018.1490977
Foroozesh F, Monavari SM, Salmanmahiny A, Robati M, Rahimi R (2022) Assessment of sustainable urban development based on a hybrid decision-making approach: group fuzzy BWM, AHP, and TOPSIS–GIS. Sustain Cities Soc 76:103402. https://doi.org/10.1016/j.scs.2021.103402
Galloway DL, Bawden GW, Leake SA, Honegger DG (2018) Land subsidence hazards. In: Subsid L (ed) Landslide Hazards to Pipelines. US Geological Survey, Reston VA USA
Galloway DL, Jones DR, Ingebritsen SE (1999) Land subsidence in the United States. Vol. 1182. US Geological Survey. https://doi.org/10.3133/cir1182
Geological Survey of Iran (2007) Geological water report of Hashtgerd plain- Land subsidence plan in Tehran province (Vol. 2). Earth Sciences Development Company
Ghorbanzadeh O, Feizizadeh B, Blaschke T (2018) An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping. Environ Earth Sci 77(16):1–19. https://doi.org/10.1007/s12665-018-7758-y
Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K (2020) A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. J Spat Sci 65(3):401–418. https://doi.org/10.1080/14498596.2018.1505564
Gido NA, Bagherbandi M, Nilfouroushan F (2020) Localized subsidence zones in Gävle City detected by sentinel-1 PSI and leveling data. Remote Sensing 12(16):2629–2649
Guzy A, Malinowska AA (2020) State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal. Water 12(7):2051–2092. https://doi.org/10.3390/w12072051
Hailegebriel S (2007) Irrigation Potential Evaluation and Crop Suitability Analysis Using GIS and Remote Sensing Technique in Beles Sub Basin, Beneshangul Gumez Region. Master’s Thesis, Addis Ababa Universty, Addis Ababa, Ethiopia. Retrieved May 20, 2021, from http://etd.aau.edu.et/handle/123456789/6736
Hakim WL, Achmad AR, Lee CW (2020) Land subsidence susceptibility mapping in jakarta using functional and meta-ensemble machine learning algorithm based on time-series InSAR Data. Remote Sensing 12(21):3627–3653. https://doi.org/10.3390/rs12213627
Hu B, Zhou J, Wang J, Chen Z, Wang D, Xu S (2009) Risk assessment of land subsidence at Tianjin coastal area in China. Environ Earth Sci 59(2):269. https://doi.org/10.1007/s12665-009-0024-6
Huang B, Shu L, Yang YS (2012) Groundwater overexploitation causing land subsidence: hazard risk assessment using field observation and spatial modelling. Water Resour Manage 26(14):4225–4239. https://doi.org/10.1007/s11269-012-0141-y
Ibrahim E, Putranto D D A, Affandi A K (2018) Evaluation of land subsidence impact at lowland areas using analitycal hierarchy process (AHP) Method. In: E3S Web of Conferences (Vol. 68, pp. 04017). EDP Sciences. https://doi.org/10.1051/e3sconf/20186804017
Jeanne P, Farr TG, Rutqvist J, Vasco DW (2019) Role of agricultural activity on land subsidence in the San Joaquin Valley, California. J Hydrol 569:462–469. https://doi.org/10.1016/j.jhydrol.2018.11.077
Jung Y B, Cheon D S, Choi S O (2005) Estimation of subsidence due to mining using artificial neural network–preliminary study. In underground space use. Analysis of the past and lessons for the future, two volume set: Proceedings of the international world tunnel congress and the 31st ITA General Assembly, Istanbul, Turkey, CRC Press. Retrieved May 20, 2021, from https://www.taylorfrancis. com/chapters/edit/10.1201/ NOE0415374521–49/estimation-subsidence-due-mining-using-artificial-neural-network-preliminary-study-jung-cheon-choi
Kim Y, Lee SS (2020) Application of artificial neural networks in assessing mining subsidence risk. Appl Sci 10(4):1302. https://doi.org/10.3390/app10041302
Lee S, Park I (2013) Application of decision tree model for the ground subsidence hazard mapping near abandoned underground coal mines. J Environ Manage 127:166–176. https://doi.org/10.1016/j.jenvman.2013.04.010
Li L, Losser T, Yorke C, Piltner R (2014) Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2. 5 in the contiguous US using parallel programming and kd tree. Int J Environ Res Public Health 11(9):9101–9141. https://doi.org/10.3390/ijerph110909101
Liu L, Yu J, Chen B, Wang Y (2020) Urban subsidence monitoring by SBAS-InSAR technique with multi-platform SAR images: a case study of Beijing Plain China. Eur J Remote Sens 53(1):141–153. https://doi.org/10.1080/22797254.2020.1728582
Machowski R, Rzetala M (2018) Impact of subsidence basins on changes in the catchment water cycle. In: 18th International Multidisciplinary Scientific GeoConferences SGEM, pp 407–414. https://doi.org/10.5593/sgem 2018/3.1/S12.053
Meilianda E, Dohmen-Janssen CM, Maathuis BH, Hulscher SJ, Mulder JP (2010) Short-term morphological responses and developments of Banda Aceh coast, Sumatra Island, Indonesia after the tsunami on 26 December 2004. Mar Geol 275(1–4):96–109. https://doi.org/10.1016/j.margeo.2010.04.012
Meldebekova G, Yu C, Li Z, Song C (2020) Quantifying ground subsidence associated with aquifer overexploitation using space-borne radar interferometry in Kabul Afghanistan. Remote Sensing 12(15):2461–2482. https://doi.org/10.3390/rs12152461
Mhetre P, Bapat MS (2015) Classification of teaching evaluation performance using support vector machine. Int J Latest Res Sci Technol (IJLRST) 4(6):37–39
Mirzadeh SMJ, Jin S, Parizi E, Chaussard E, Burgmann R, Delgado Blasco JM, Mirzadeh SH (2021) Characterization of Irreversible Land Subsidence in the Yazd-Ardakan Plain, Iran from 2003–2020 InSAR Time Series. JGR Solid Earth. https://doi.org/10.1029/2021JB022258
Modoni G, Darini G, Spacagna RL, Saroli M, Russo G, Croce P (2013) Spatial analysis of land subsidence induced by groundwater withdrawal. Eng Geol 167:59–71. https://doi.org/10.1016/j.enggeo.2013.10.014
Moghtased-Azar K, Mirzaei A, Nankali H, Tavakoli F (2012) Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia. Nonlin Process Geophys 19(6):675–683. https://doi.org/10.5194/npg-19-675-2012
Mohammady M, Pourghasemi HR, Amiri M (2019a) Land subsidence susceptibility assessment using random forest machine learning algorithm. Environ Earth Sci 78(16):1–12. https://doi.org/10.1007/s12665-019-8518-3
Mohammady M, Pourghasemi HR, Amiri M (2019b) Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms. Nat Hazards 99(2):951–971. https://doi.org/10.1007/s11069-019-03785-z
Moharrami M, Naboureh A, Gudiyangada Nachappa T, Ghorbanzadeh O, Guan X, Blaschke T (2020) National-scale landslide susceptibility mapping in austria using fuzzy best-worst multi-criteria decision-making. ISPRS Int J Geo Inf 9(6):393. https://doi.org/10.3390/ijgi9060393
Mohebbi Tafreshi G, Nakhaei M, Lak R (2019) Land subsidence risk assessment using GIS fuzzy logic spatial modeling in Varamin aquifer Iran. GeoJournal 86(38):1–21. https://doi.org/10.1007/s10708-019-10129-8
Mokhtari M, Abedian S (2019) Spatial prediction of landslide susceptibility in Taleghan basin Iran. Stoch Environ Res Risk Assess 33(7):1297–1325. https://doi.org/10.1007/s00477-019-01696-w
Motagh M, Walter TR, Sharifi MA, Fielding E, Schenk A, Anderssohn J, Zschau J (2008) Land subsidence in Iran caused by widespread water reservoir overexploitation. Geophys Res Lett 35(16):1–5. https://doi.org/10.1029/2008GL033814
Nadiri AA, Taheri Z, Khatibi R, Barzegari G, Dideban K (2018) Introducing a new framework for mapping subsidence vulnerability indices (SVIs): ALPRIFT. Sci Total Environ 628:1043–1057. https://doi.org/10.1016/j.scitotenv.2018.02.031
Navarro-Hernández MI, Tomás R, Lopez-Sanchez JM, Cárdenas-Tristán A, Mallorquí JJ (2020) Spatial analysis of land subsidence in the san luis potosi valley INDUCED by aquifer overexploitation using the coherent pixels technique (CPT) and Sentinel-1 InSAR observation. Remote Sensing 12(22):3822–3245. https://doi.org/10.3390/rs12223822
Nie L, Zhang M, Jian H (2013) Analysis of surface subsidence mechanism and regularity under the influence of seism and fault. Nat Hazards 66(2):773–780. https://doi.org/10.1007/s11069-012-0515-8
Oh HJ, Lee S (2010) Assessment of ground subsidence using GIS and the weights-of-evidence model. Eng Geol 115(1–2):36–48. https://doi.org/10.1016/j.enggeo.2010.06.015
Orhan O (2021) Monitoring of land subsidence due to excessive groundwater extraction using small baseline subset technique in Konya Turkey. Environ Monit Assess 193(4):1–17. https://doi.org/10.1007/s10661-021-08962-x
Ouabo RE, Sangodoyin AY, Ogundiran MB (2020) Assessment of ordinary Kriging and inverse distance weighting methods for modeling chromium and cadmium soil pollution in E-waste sites in Douala Cameroon. J Health Pollution 10(26):200605. https://doi.org/10.5696/2156-9614-10.26.200605
Park I, Lee J, Saro L (2014) Ensemble of ground subsidence hazard maps using fuzzy logic. Central Eur J Geosci 6(2):207–218. https://doi.org/10.2478/s13533-012-0175-y
Piscopo G (2001) Groundwater vulnerability map explanatory notes—Castlereagh Catchment. NSW Department of Land and Water Conservation, Australia. Retrieved May 20, 2021, from http://www.dlwc.nsw.gov.au/ care/water/ groundwater/reports/pdfs/castlereagh_map_notes.pdf
Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province Iran. J Earth Syst Sci 122(2):349–369. https://doi.org/10.1007/s12040-013-0282-2
Pradhan B (2010) Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Adv Space Res 45(10):1244–1256. https://doi.org/10.1016/j.asr.2010.01.006
Pradhan B, Abokharima MH, Jebur MN, Tehrany MS (2014) Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73(2):1019–1042. https://doi.org/10.1007/s11069-014-1128-1
Ranjgar B, Razavi-Termeh SV, Foroughnia F, Sadeghi-Niaraki A, Perissin D (2021) Land subsidence susceptibility mapping using persistent scatterer SAR interferometry technique and optimized hybrid machine learning algorithms. Remote Sensing 13(7):1326–1350. https://doi.org/10.3390/rs13071326
Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53:49–57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei J (2016) Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega 64:126–130. https://doi.org/10.1016/j.omega.2015.12.001
Rezaei M, Yazdani Noori Z, Dashti Barmaki M (2020) Land subsidence susceptibility mapping using analytical hierarchy process (AHP) and Certain Factor (CF) models at Neyshabur plain Iran. Geocarto International. https://doi.org/10.1080/10106049.2020.1768596
Rudiarto I, Handayani W, Sih Setyono J (2018) A regional perspective on urbanization and climate-related disasters in the northern coastal region of central Java Indonesia. Land 7(1):34–49. https://doi.org/10.3390/land7010034
Saaty TL (2008) Decision making with the analytic hierarchy process. International Journal of Services Sciences 1(1):83–98. https://doi.org/10.1504/IJSSCI.2008.017590
Sadeghfam S, Khatibi R, Dadashi S, Nadiri AA (2020) Transforming subsidence vulnerability indexing based on ALPRIFT into risk indexing using a new fuzzy-catastrophe scheme. Environ Impact Assess Rev 82:106352. https://doi.org/10.1016/j.eiar.2019.106352
Sahu P, Lokhande RD (2015) An investigation of sinkhole subsidence and its preventive measures in underground coal mining. Procedia Earth Planetary Sci 11:63–75. https://doi.org/10.1016/j.proeps.2015.06.009
Sayyaf M, Mahdavi M, Barani OR, Feiznia S, Motamedvaziri B (2014) Simulation of land subsidence using finite element method: Rafsanjan plain case study. Natural Hazards 72(2):309–322. https://doi.org/10.1007/s11069-013-1010-6
Shi X, Jiang S, Xu H, Jiang F, He Z, Wu J (2016) The effects of artificial recharge of groundwater on controlling land subsidence and its influence on groundwater quality and aquifer energy storage in Shanghai China. Environ Earth Sci 75(3):195. https://doi.org/10.1007/s12665-015-5019-x
Shi X, Zhang S, Jiang M, Pei Y, Qu T, Xu J, Yang C (2021) Spatial and temporal subsidence characteristics in Wuhan city (China) during 2015–2019 inferred from Sentinel-1 SAR Interferometry. Nat Hazard 21(8):2285–2297. https://doi.org/10.5194/nhess-21-2285-2021
Shirzaei M, Bürgmann R (2018) Global climate change and local land subsidence exacerbate inundation risk to the San Francisco Bay Area. Science Advances 4(3):eaap9234. https://doi.org/10.1126/sciadv.aap9234
Sneed M, Brandt J T (2007) Detection and measurement of land subsidence using global positioning system surveying and interferometric synthetic aperture radar, Coachella Valley, California, 1996–2005: US Department of the Interior, US Geological Survey. Retrieved May 20, 2021, from https://pubs.usgs.gov/ sir/2007/5251/
Song M, Chen D (2018) An improved knowledge-informed NSGA-II for multi-objective land allocation (MOLA). Geo-Spatial Inf Sci 21(4):273–287. https://doi.org/10.1080/10095020.2018.1489576
Sopata P, Stoch T, Wójcik A, Mrocheń D (2020) Land surface subsidence due to mining-induced tremors in the upper Silesian Coal Basin (Poland)—case study. Remote Sensing 12(23):3923–3940. https://doi.org/10.3390/rs12233923
Sowers G F (1976) Mechanisms of subsidence due to underground openings. Transportation Research Record. ISSUE No. 612. Retrieved May 20, 2021, from http://onlinepubs.trb.org/Onlinepubs/trr/1976/612/612-001.pdf
Sui L, Ma F, Chen N (2020) Mining subsidence prediction by combining support vector machine regression and interferometric synthetic aperture radar data. ISPRS Int J Geo Inf 9(6):390–407. https://doi.org/10.3390/ijgi9060390
Tafreshi GM, Nakhaei M, Lak R (2019) Land subsidence risk assessment using GIS fuzzy logic spatial modeling in Varamin aquifer. Iran Geojournal. https://doi.org/10.1007/s10708-019-10129-8
Taheri Z, Barzghari G, Dideban K (2018) A framework to estimation of potential subsidence of the aquifer using algorithm genetic Iran. Water Resourc Res 14(2):182–194
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and Naive Bayes models. Math Probl Eng. https://doi.org/10.1155/2012/974638
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 18:2464. https://doi.org/10.3390/s18082464
Tzampoglou P, Loupasakis C (2017) Land subsidence susceptibility and hazard mapping: the case of Amyntaio Basin, Greece. In: Fifth International conference on remote sensing and geoinformation of the environment (RSCy2017) (Vol. 10444). https://doi.org/10.1117/12.2277510
Uyan M, Cay T (2010) Geostatistical methods for mapping groundwater nitrate concentrations. 3rd International conference on cartography and GIS. Nessebar, Bulgaria. Retrieved May 20, 2021, from https://www.cartography-gis.com/pdf/20_Mevlut_Uyan_Turkey_paper.pdf.
Vapnik V (2013) The nature of statistical learning theory. Springer science & business media. Citado na 27. Retrieved May 20, 2021, from https://link.springer.com/book/https://doi.org/10.1007/978-1-4757-3264-1
Vázquez-Quintero G, Prieto-Amparán JA, Pinedo-Alvarez A, Valles-Aragón MC, Morales-Nieto CR, Villarreal-Guerrero F (2020) GIS-based multicriteria evaluation of land suitability for grasslands conservation in Chihuahua Mexico. Sustainability 12(1):185–204. https://doi.org/10.3390/su12010185
WRI (2014) Prediction of subsidence due to groundwater resource utilization using combined modeling and interferometric technique in radar satellite imagery. Water Research Institute. Retrieved May 20, 2021, from Iran Ministry of Energy. http://wrr-wri.ir/wp-content/uploads/2017/12/Qom.pdf.
Xu C, Dai F, Xu X, Lee YH (2012) GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80. https://doi.org/10.1016/j.geomorph.2011.12.040
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong China. Geomorphology 101(4):572–582. https://doi.org/10.1016/j.geomorph.2008.02.011
Yu Y, Chen SE, Deng KZ, Wang P, Fan HD (2018) Subsidence mechanism and stability assessment methods for partial extraction mines for sustainable development of mining cities-A review. Sustainability 10(1):113–134. https://doi.org/10.3390/su10010113
Zheng YY, Chen YL, Lin HR, Huang SY, Yeh TCJ, Wen JC (2017) A simple model to describe the relationship among rainfall, groundwater and land subsidence under a heterogeneous aquifer. In AGU Fall Meeting Abstracts (Vol. 2017, pp. H34A-08). Retrieved May 20, 2021, from https://ui.adsabs.harvard. edu/abs/2017 AGUFM.H34A.08Z/abstract
Zhi-xiang T, Pei-xian L, Li-li Y, Ka-zhong D (2009) Study of the method to calculate subsidence coefficient based on SVM. Procedia Earth and Planetary Science 1(1):970–976. https://doi.org/10.1016/j.proeps.2009.09.150
Zhou C, Gong H, Chen B, Gao M, Cao Q, Cao J, Shi M (2020) Land subsidence response to different land use types and water resource utilization in Beijing-Tianjin-Hebei China. Remote Sensing 12(3):457–479. https://doi.org/10.3390/rs12030457
Funding
This research did not receive any specific grant from funding agencies in public, commercial, or not-for-profit sectors.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The authors have no conflict of interest to declare.
Ethical approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mehrnoor, S., Robati, M., Kheirkhah Zarkesh, M.M. et al. Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM). Nat Hazards 115, 1997–2030 (2023). https://doi.org/10.1007/s11069-022-05624-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-022-05624-0