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Environmental Earth Sciences

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01.08.2020 | Original Article

Assessing subsidence susceptibility to coal mining using frequency ratio, statistical index and Mamdani fuzzy models: evidence from Raniganj coalfield, India

verfasst von: Sufia Rehman, Mehebub Sahana, Shyamal Dutta, Haroon Sajjad, Xuang Song, Kashif Imdad, Jie Dou

Erschienen in: Environmental Earth Sciences | Ausgabe 16/2020

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Abstract

Raniganj, an important coalfield in India, is susceptible to mining subsidence, ejection of toxicants in the environment and huge subsurface destruction. Mining with opencast method poses a great risk to the surrounding areas of this coalfield. Anthropogenic and natural activities have caused ground movement leading to subsidence in Raniganj coalfield. Existing knowledge on subsidence susceptibility to coal mining in Raniganj coalfield is scant. The study makes an attempt to analyze mining subsidence susceptibility in Raniganj coalfield using site-specific parameters. We first prepared subsidence inventory map to identify old subsidence locations and to construct the model. It was later utilized for validating the models. Fifteen site-specific parameters related to subsidence were chosen to analyze coal mining subsidence. Frequency ratio (Fr), statistical index (SI) and Mamdani fuzzy (Mf) models were utilized to assess their effectiveness in preparing susceptibility map. Findings of the study revealed very high susceptibility in the central part, high susceptibility in north-western part and moderate susceptibility in the eastern part of Raniganj coalfield. Low and very low susceptibility was found in those parts having largest area under vegetation and water bodies. The models were validated through ROC curve, seed cell area index (SCAI) and spatially agreed area approach. Mamdani fuzzy model with highest success rate (87%) and prediction accuracy (85%) was found the best fit model for analyzing mining subsidence susceptibility. The framework of the methodology will be instructive for analyzing susceptibility at different geographical locations.

Vorheriger Artikel Extent of zeolite synthesis via alkaline fusion from tailings dam sediments

Nächster Artikel A review of natural geological hazards in Oman

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Adhikari K, Sadhu K, Chakroborty B, Gangopadhyay A (2013) Effect of mining on geochemistry of groundwater in Permo-carboniferous Gondwana coalfields: Raniganj Basin. India J Geol Soc India 82(4):392–402. https://doi.org/10.1007/s12594-013-0166-8CrossRef

Akgun A (2012) A comparison of landslide susceptibility maps produced by logistic regression, multi-criteria decision, and likelihood ratio methods: a case study at İzmir. Turkey Landslides 9(1):93–106. https://doi.org/10.1007/s10346-011-0283-7CrossRef

Atmaca H, Cetisli B, Yavuz HS (2001) The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data. In: Second international conference on electrical and electronics engineering papers ELECO

Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains. Cent Jpn Geomorphol 65(1–2):15–31. https://doi.org/10.1016/j.geomorph.2004.06.010CrossRef

Bijukchhen SM, Kayastha P, Dhital MR (2013) A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–DhadKhola, east Nepal. Arab J Geosci 6(8):2727–2743. https://doi.org/10.1007/s12517-012-0569-7CrossRef

Booth CJ, Spande ED, Pattee CT, Miller JD, Bertsch LP (1998) Positive and negative impacts of longwall mine subsidence on a sandstone aquifer. Environ Geol 34(2–3):223–233. https://doi.org/10.1007/s002540050274CrossRef

BP statistical reviews of world energy (2015) www.bp.com/statisticalreview. Accessed 17 Jan 2019

Bui DT, Pradhan B, Lofman O, Revhaug I, Dick OB (2012) Landslide susceptibility mapping at HoaBinh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211. https://doi.org/10.1016/j.cageo.2011.10.031CrossRef

Camilo DC, Lombardo L, Mai PM, Dou J, Huser R (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environ Modell Softw 97:145–156. https://doi.org/10.1016/j.envsoft.2017.08.003CrossRef

Cao C, Xu P, Wang Y, Chen J, Zheng L, Niu C (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):948. https://doi.org/10.3390/su8090948CrossRef

Carnec C, Delacourt C (2000) Three years of mining subsidence monitored by SAR interferometry, near Gardanne, France. J Appl Geophys 43(1):43–54. https://doi.org/10.1016/S0926-9851(99)00032-4CrossRef

Cawley GC, Talbot NL (2010) On over-fitting in model selection and subsequent selection bias in performance evaluation. J Mach Learn Res 11(Jul):2079–2107

Chai Y, Jia L, Zhang Z (2009) Mamdani model based adaptive neural fuzzy inference system and its application in traffic level of service evaluation. In: 2009 Sixth international conference on fuzzy systems and knowledge discovery, vol 4, IEEE, pp. 555–559

Chang KT, Chiang SH, Hsu ML (2007) Modeling typhoon-and earthquake-induced landslides in a mountainous watershed using logistic regression. Geomorphology 89(3–4):335–347. https://doi.org/10.1016/j.geomorph.2006.12.011CrossRef

Chen W, Pradhan B, Li S, Shahabi H, Rizeei HM, Hou E, Wang S (2019) Novel hybrid integration approach of bagging-based fisher’s linear discriminant function for groundwater potential analysis. Nat Resour Res. https://doi.org/10.1007/s11053-019-09465-wCrossRef

Choi JK, Kim KD, Lee S, Won JS (2010) Application of a fuzzy operator to susceptibility estimations of coal mine subsidence in Taebaek City, Korea. Environ Earth Sci 59(5):1009–1022. https://doi.org/10.1007/s12665-009-0093-6CrossRef

Chugh YP (2018) Concurrent mining and reclamation for underground coal mining subsidence impacts in China. Int J Coal Sci Technol. https://doi.org/10.1007/s40789-018-0189-2CrossRef

Colesanti C, Mouelic SL, Bennani M, Raucoules D, Carnec C, Ferretti A (2005) Detection of mining related ground instabilities using the Permanent Scatterers technique—a case study in the east of France. Int J Remote Sens 26(1):201–207. https://doi.org/10.1080/0143116042000274069CrossRef

Dai J, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput 13(1):211–221. https://doi.org/10.1016/j.asoc.2012.07.029CrossRef

Darmody RG (1995) Modeling agricultural impacts of longwall mine subsidence: a GIS approach. Int J Surf Mining Reclam 9(2):63–68. https://doi.org/10.1080/09208119508964720CrossRef

Das S, Biswas AB (1969) Geology and groundwater conditions of the alluvial tract, East of Durgapur, Burdwan District, West Bengal—a preliminary study. Indian Geohydrol 5(1):71–89 (19 P, 4 FIG, 1 TAB, 3 REF)

Dash M, Liu H (2003) Consistency-based search in feature selection. Artif Intell 151(1–2):155–176. https://doi.org/10.1016/S0004-3702(03)00079-1CrossRef

Denaï MA, Palis F, Zeghbib A (2007) Modeling and control of non-linear systems using soft computing techniques. Appl Soft Comput 7(3):728–738. https://doi.org/10.1016/j.asoc.2005.12.005CrossRef

Do TN, Wu JH (2020) Simulating a mining-triggered rock avalanche using DDA: a case study in Nattai North. Aust Eng Geol 264:105386CrossRef

Dou J, Bui DT, Yunus AP, Jia K, Song X, Revhaug I, Xia H, Zhu Z (2015a) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS ONE 10(7):e0133262. https://doi.org/10.1371/journal.pone.0133262CrossRef

Dou J, Paudel U, Oguchi T, Uchiyama S, Hayakavva YS (2015b) Shallow and deep-seated landslide diferentiation using support vector machines: a case study of the Chuetsu Area, Japan. Terr Atmos Ocean Sci 26(2). https://doi.org/10.3319/TAO.2014.12.02.07(EOSI)

Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y, Zhu Z (2015c) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island. Jpn Nat Hazards 78(3):1749–1776. https://doi.org/10.1007/s11069-015-1799-2CrossRef

Dou J, Yunus AP, Bui DT, Merghadi A, Sahana M, Zhu Z, et al (2019) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662:332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221…CrossRef

Egan JP (1975) Signal detection theory and ROC Analysis Academic Press Series in Cognition and Perception. Academic Press, London

Elmahdy SI, Mohamed MM, Ali TA, Abdalla JE, Abouleish M (2020) Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain, UAE. Geocarto Int 20:1–7

Ercanoğlu M (2003) Production of landslide susceptibility maps using fuzzy log and statistical methods: West Black Sea region (South of Kumlace–North of Yenice), Geological Engineering Dept. Hacettepe University. Ph.D. thesis

Ercanoglu M, Gokceoglu C, Van Asch TW (2004) Landslide susceptibility zoning north of Yenice (NW Turkey) by multivariate statistical techniques. Nat Hazards 32(1):1–23. https://doi.org/10.1023/b:nhaz.0000026786.85589.4aCrossRef

Gaprindashvili G, Guo J, Daorueang P, Xin T, Rahimy P (2014) A new statistic approach towards landslide hazard risk assessment. Int J Geosci 5(01):38CrossRef

Geological Survey of India. GSI (2017) Natural energy resources mission-IIB 2017. Indian coal and lignite resources-2017. https://gsi.gov.in/cs/groups/public/documents/document/b3zp/mtyx/~edisp/dcport1gsigovi161863.pdf. Accessed 17 Jan 2019

Ghasemi E, Ataei M (2013) Application of fuzzy logic for predicting roof fall rate in coal mines. Neural Comput Appl 22(1):311–321. https://doi.org/10.1007/s00521-012-0819-3CrossRef

Ghosh S (2002) The Raniganj Coal Basin: an example of an Indian Gondwana rift. Sediment Geol 147(1–2):155–176. https://doi.org/10.1016/s0037-0738(01)00195-6CrossRef

Gupta AK, Dutta AK, Basu R (2018) Subsidence—a major effect of coal mining in Raniganj Coalfield. IJRDO J Bus Manag 4(8):14–27

Harliana P, Rahim R (2017) Comparative analysis of membership function on Mamdani fuzzy inference system for decision making. J Phys Conf Ser 930(1):012029. https://doi.org/10.1088/1742-6596/930/1/012029CrossRef

Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9(2):112. https://doi.org/10.1007/s12517-015-2094-yCrossRef

Howladar MF (2016) Environmental impacts of subsidence around the Barapukuria Coal Mining area in Bangladesh. Energy Ecol Environ 1(6):370–385. https://doi.org/10.1007/s40974-016-0031-xCrossRef

Iancu I (2012) A Mamdani type fuzzy logic controller, fuzzy logic—controls, concepts, theories and applications, Prof. Elmer Dadios (ed) ISBN: 978-953-51-0396-7, InTech. https://www.intechopen.com/books/fuzzy-logic-controls-concepts-theories-and-applications/a-mamdani-typefuzzy-logic-controller

Jung HC, Kim SW, Jung HS, Min KD, Won JS (2007) Satellite observation of coal mining subsidence by persistent scatterer analysis. Eng Geol 92(1–2):1–13. https://doi.org/10.1016/j.enggeo.2007.02.007CrossRef

Kayastha P, Dhital MR, De Smedt F (2013) Evaluation and comparison of GIS based landslide susceptibility mapping procedures in Kulekhani watershed. Nepal J Geol Soc India 81(2):219–231. https://doi.org/10.1007/s12594-013-0025-7CrossRef

Kim KD, Lee S, Oh HJ, Choi JK, Won JS (2006) Assessment of ground subsidence hazard near an abandoned underground coal mine using GIS. Environ Geol 50(8):1183–1191. https://doi.org/10.1007/s00254-006-0290-5CrossRef

Kleinhans I, Van Rooy JL (2016) Guidelines for sinkhole and subsidence rehabilitation based on generic geological models of a dolomite environment on the East Rand, South Africa. J Afr Earth Sci 117:86–101. https://doi.org/10.1016/j.jafrearsci.2016.01.001CrossRef

Kruse R (2008) Fuzzy neural network. Scholarpedia 3(11):6043. https://doi.org/10.4249/scholarpedia.6043CrossRef

Lee S, Kim K, Oh HJ, Park NW (2006) Ground subsidence hazard analysis in an abandoned underground coal mine area using probabilistic and logistic regression models. In: 2006 IEEE international symposium on geoscience and remote sensing. IEEE, pp. 1549–1552

Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855. https://doi.org/10.1007/s00254-006-0256-7CrossRef

Li X, Wang D, Li C, Liu Z (2019) Numerical simulation of surface subsidence and backfill material movement induced by underground mining. Adv Civ Eng. https://doi.org/10.1155/2019/2724370CrossRef

Luo X, Lin F, Zhu S, Yu M, Zhang Z, Meng L, Peng J (2019) Mine landslide susceptibility assessment using IVM, ANN and SVM models considering the contribution of affecting factors. PLoS ONE 14(4):0215134. https://doi.org/10.1371/journal.pone.0215134CrossRef

Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 1:1–13. https://doi.org/10.1016/S0020-7373(75)80002-2CrossRef

Manna A, Maiti R (2014) Opencast coal mining induced defaced topography of RANIGANJ coalfield in India-remote sensing and GIS based analysis. J Indian Soc Remote Sens 42(4):755–764CrossRef

Marschalko M, Yilmaz I, Bednárik M, Kubečka K, Bouchal T, Závada J (2012) Subsidence map of underground mining influence for urban planning: an example from the Czech Republic. Q J Eng Geol Hydrogeol l45(2): 231–241

Mathew J, Jha VK, Rawat GS (2007) Weights of evidence modelling for landslide hazard zonation mapping in part of Bhagirathi valley, Uttarakhand. Curr Sci 628–638

Mladenić D, Brank J, Grobelnik M, Milic-Frayling N (2004) Feature selection using linear classifier weights: interaction with classification models. In: Proceedings of the 27th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp. 234–241

Mondol S, Chakravarty D, Bandyopadhyay J, Maiti KK (2016) Identification of land subsidence and management Using Cadastral Techniques, Mining Area of Raniganj, Barddhaman District, India. Energy Environ Res 6(2):52–62. https://doi.org/10.5539/eer.v6n2p52CrossRef

Naha K, Srinivasan R, Jayaram S (1991) Sedimentational, structural and migmatitic history of the Archaean Dharwar tectonic province, southern India. Proc Indian Acad Sci Earth Planet Sci 100(4):413

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.2010.06.015CrossRef

Oh HJ, Syifa M, Lee CW, Lee S (2019) Land subsidence susceptibility mapping using Bayesian, functional, and meta-ensemble machine learning models. Appl Sci 9(6):1248. https://doi.org/10.3390/app9061248CrossRef

Park I, Choi J, Lee MJ, Lee S (2012) Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping. Comput Geosci 48:228–238. https://doi.org/10.1016/j.cageo.2012.01.005CrossRef

Park I, Lee J, Saro L (2014) Ensemble of ground subsidence hazard maps using fuzzy logic. Open Geosci 6(2):207–218. https://doi.org/10.2478/s13533-012-0175-yCrossRef

Pellicani R, Spilotro G, Gutiérrez F (2017) Susceptibility mapping of instability related to shallow mining cavities in a built-up environment. Eng Geol 217:81–88. https://doi.org/10.1016/j.enggeo.2016.12.011CrossRef

Pourghasemi HR, Beheshtirad M, Pradhan B (2016) A comparative assessment of prediction capabilities of modified analytical hierarchy process (M-AHP) and Mamdani fuzzy logic models using Netcad-GIS for forest fire susceptibility mapping. Geomat Nat Hazards Risk 7(2):861–885. https://doi.org/10.1080/19475705.2014.984247CrossRef

Pradhan B (2013) A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Comput Geosci 51:350–365. https://doi.org/10.1016/j.cageo.2012.08.023CrossRef

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/s11069-014-1128-1CrossRef

Quanyuan W, Jiewu P, Shanzhong Q, Yiping L, Congcong H, Tingxiang L, Limei H (2009) Impacts of coal mining subsidence on the surface landscape in Longkou city, Shandong Province of China. Environ Earth Sci 59(4):783. https://doi.org/10.1007/s12665-009-0074-9CrossRef

Ramya T, Kannan AC, Balasenthil RS, Bagirathi BA (2014) Fuzzy logic modeling for decision making processes using MATLAB. Adv Mater Res 984:425–430. https://doi.org/10.4028/www.scientific.net/AMR.984-985.425CrossRef

Regmi AD, Dhital MR, Zhang JQ, Su LJ, Chen XQ (2016) Landslide susceptibility assessment of the region affected by the 25 April 2015 Gorkha earthquake of Nepal. J Mount Sci 13(11):1941–1957. https://doi.org/10.1007/s11629-015-3688-2CrossRef

Sadhu K, Adhikari K, Gangopadhyay A (2012) Effect of mine spoil on native soil of Lower Gondwana coal fields: Raniganj coal mines areas, India. Int J Environ Sci 2(3):1675–1687. https://doi.org/10.6088/ijes.00202030052CrossRef

Sahana M, Sajjad H (2017) Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district, India. J Mount Sci 14(11):2150–2167. https://doi.org/10.1007/s11629-017-4404-1CrossRef

Sahu SP, Yadav M, Das AJ, Prakash A, Kumar A (2017) Multivariate statistical approach for assessment of subsidence in Jharia coalfields, India. Arabian J Geosci 10(8):191. https://doi.org/10.1007/s12517-017-2985-1CrossRef

Salmi EF, Nazem M, Karakus M (2017) Numerical analysis of a large landslide induced by coal mining subsidence. Eng Geol 217:141–152CrossRef

Sengupta S (1996) Contributions in environmental geology. In: SK Shome et al (eds) Special publication.: Geological Survey of India, Kolkata 43, 105–107.

Sikdar PK, Chakraborty S, Adhya E, Paul PK (2004) Land use/Land cover changes and groundwater potential zoning in and around Raniganj coal mining area, Bardhaman District, West Bengal-A GIS and Remote Sensing Approach. J Spatial Hydrol 4(2):1–24

Singh RP, Yadav RN (1995a) Subsidence due to coal mining in India Series of Proceedings and Reports. Intern Assoc Hydrol Sci 234:207–214

Singh RP, Yadav RN (1995) Prediction of subsidence due to coal mining in Raniganj coalfield, West Bengal, India. Eng Geol 39(1–2):103–111. https://doi.org/10.1016/0013-7952(94)00062-7CrossRef

Singh TN, Dubey BK (1993) Scope of Bhuggatdih method of mining for thick coal seams. In: Proceedings of the workshop on wide stall mining of coal seams under constraints. Central Mining Fuel Research Institute, Dhanbad, 18p

Smith S (2009) In: New South Wales Parliamentary Library Research Service (eds) Mining and the environment. Briefing Paper No 6/09, NSW Parliamentary Library Research Service, Sydney, State of New South Wales

Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679. https://doi.org/10.1007/s00254-003-0917-8CrossRef

Süzen ML, Kaya BŞ (2012) Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digital Earth 5(4):338–355. https://doi.org/10.1080/17538947.2011.586443CrossRef

Thai Pham B, Prakash I, Dou J, Singh SK, Trinh PT, Trung Tran H et al (2018) A novel hybrid approach of landslide susceptibility modeling using rotation forest ensemble and different base classifiers. Geocarto Int. https://doi.org/10.1080/10106049.2018.1559885CrossRef

Viljoen DA (1979) Importance of coal. J South Afr Inst Min Metall 79(2):493–494

Waltham AC (1989) Ground subsidence, 1st edn. Blackie & Son Ltd., Glasgow

Wang Q, Li W, Wu Y, Pei Y, Xie P (2016) Application of statistical index and index of entropy methods to landslide susceptibility assessment in Gongliu (Xinjiang, China). Environ Earth Sci 75(7):599CrossRef

Wang WD, Xie CM, Du XG (2009) Landslides susceptibility mapping based on geographical information system, GuiZhou, south-west China. Environ Geol 58(1):33–43. https://doi.org/10.1007/s00254-008-1488-5CrossRef

Westen CJV (1997) Statistical landslide hazard analysis. ILWIS 2.1 for Windows application guide. ITC Publication, Enschede, pp 73–84

Wu JH, Ohnishi Y, Nishiyama S (2004) Simulation of the mechanical behavior of inclined jointed rock masses during tunnel construction using discontinuous deformation analysis (DDA). Int J Rock Mech Sci 41(5):731–743CrossRef

Xiao W, Hu Z, Zhang R, Zhao Y (2013) A simulation of mining subsidence and its impacts to land in high ground water area-an integrated approach based on subsidence prediction and GIS. Disaster Adv 6:142–148

Yalcin A, Reis S, Aydinoglu AC, Yomralioglu T (2011) A GIS-based comparative study of frequency ratio, analytical hierarchy process, bivariate statistics and logistics regression methods for landslide susceptibility mapping in Trabzon, NE Turkey. CATENA 85(3):274–287. https://doi.org/10.1016/j.catena.2011.01.014CrossRef

Yilmaz I (2010) Comparison of landslide susceptibility mapping methodologies for Koyulhisar, Turkey: conditional probability, logistic regression, artificial neural networks, and support vector machine. Environ Earth Sci 61(4):821–836. https://doi.org/10.1007/s12665-009-0394-9CrossRef

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. https://doi.org/10.3390/su10010113CrossRef

Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 1:28–44. https://doi.org/10.1109/TSMC.1973.5408575CrossRef

Zahiri H, Palamara DR, Flentje P, Brassington GM, Baafi E (2006) A GIS-based weights-of-evidence model for mapping cliff instabilities associated with mine subsidence. Environ Geol 51(3):377–386. https://doi.org/10.1007/s00254-006-0333-yCrossRef

Titel: Assessing subsidence susceptibility to coal mining using frequency ratio, statistical index and Mamdani fuzzy models: evidence from Raniganj coalfield, India
verfasst von: Sufia Rehman
Mehebub Sahana
Shyamal Dutta
Haroon Sajjad
Xuang Song
Kashif Imdad
Jie Dou
Publikationsdatum: 01.08.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 16/2020
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-020-09119-8

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