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Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS

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Abstract

Land subsidence is one of the frequent geological hazards worldwide. Urban areas and agricultural industries are the entities most affected by the consequences of land subsidence. The main objective of this study was to estimate the land subsidence (sinkhole) hazards at the Kinta Valley of Perak, Malaysia, using geographic information system and remote sensing techniques. To start, land subsidence locations were observed by surveying measurements using GPS and using the tabular data, which were produced as coordinates of each sinkhole incident. Various land subsidence conditioning factors were used such as altitude, slope, aspect, lithology, distance from the fault, distance from the river, normalized difference vegetation index, soil type, stream power index, topographic wetness index, and land use/cover. In this article, a data-driven technique of an evidential belief function (EBF), which is in the category of multivariate statistical analysis, was used to map the land subsidence-prone areas. The frequency ratio (FR) was performed as an efficient bivariate statistical analysis method in order compare it with the acquired results from the EBF analysis. The probability maps were acquired and the results of the analysis validated by the area under the (ROC) curve using the testing land subsidence locations. The results indicated that the FR model could produce a 71.16 % prediction rate, while the EBF showed better prediction accuracy with a rate of 73.63 %. Furthermore, the success rate was measured and accuracies of 75.30 and 79.45 % achieved for FR and EBF, respectively. These results can produce an understanding of the nature of land subsidence as well as promulgate public awareness of such geo-hazards to decrease human and economic losses.

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References

  • Alon N, Spencer JH (2004) The probabilistic method. Wiley, USA

    Google Scholar 

  • Althuwaynee OF, Pradhan B, Lee S (2012) Application of an evidential belief function model in landslide susceptibility mapping. Comput Geosci 44:120–135

    Article  Google Scholar 

  • An P, Moon W, Bonham-Carter G (1994) Uncertainty management in integration of exploration data using the belief function. Nonrenewable Res 3:60–71

    Article  Google Scholar 

  • Aurit MD, Peterson RO, Blanford JI (2013) A GIS analysis of the relationship between Sinkholes, dry-well complaints and groundwater pumping for frost-freeze protection of winter strawberry production in Florida. PLoS ONE 8:e53832. doi:10.1371/journal.pone.0053832

    Article  Google Scholar 

  • Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology 65:15–31

    Article  Google Scholar 

  • Calderhead A, 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. Adv Water Resour 34:83–97

    Article  Google Scholar 

  • Caniani D, Pascale S, Sdao F, Sole A (2008) Neural networks and landslide susceptibility: a case study of the urban area of Potenza. Nat Hazards 45:55–72

    Article  Google Scholar 

  • Carranza EJM, Hale M (2003) Evidential belief functions for data-driven geologically constrained mapping of gold potential, Baguio district, Philippines. Ore Geol Rev 22:117–132

    Article  Google Scholar 

  • Carranza E, Woldai T, Chikambwe E (2005) Application of data-driven evidential belief functions to prospectivity mapping for aquamarine-bearing pegmatites, Lundazi district, Zambia. Nat Resour Res 14:47–63

    Article  Google Scholar 

  • Carranza EJM, Van Ruitenbeek F, Hecker C, van der Meijde M, van der Meer FD (2008) Knowledge-guided data-driven evidential belief modeling of mineral prospectivity in Cabo de Gata, SE Spain. Int J Appl Earth Obs 10:374–387

    Article  Google Scholar 

  • Chang Z, Zhang J, Guo Q, Gong L (2004) Study on land subsidence evolvement tendency by means of. In: Proceedings IEEE International Geoscience and Remote Sensing Symposium, IGRASS, USA, vol 1, pp 20–24

  • Chaussard E, Wdowinski S, Cabral-Cano E, Amelung F (2014) Land subsidence in central Mexico detected by ALOS InSAR time-series. Remote Sens of Environ 140:94–106

    Article  Google Scholar 

  • 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 59:1009–1022

    Article  Google Scholar 

  • Clerici A, Perego S, Tellini C, Vescovi P (2002) A procedure for landslide susceptibility zonation by the conditional analysis method. Geomorphology 48:349–364

    Article  Google Scholar 

  • Demir G, Aytekin M, Akgün A, İkizler SB, Tatar O (2013) A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65:1481–1506

    Article  Google Scholar 

  • Dempster AP (1967a) Upper and lower probabilities induced by a multivalued mapping. Ann Math Stat 38:325–339

    Article  Google Scholar 

  • Dempster AP (1967b) Upper and lower probability inferences based on a sample from a finite univariate population. Biometrika 54:515–528

    Article  Google Scholar 

  • Ding X, Liu G, Li Z, Li Z, Chen Y (2004) Ground subsidence monitoring in Hong Kong with satellite SAR interferometry. Photogramm Eng Rem S 70:1151–1156

    Article  Google Scholar 

  • Galve J, Bonachea J, Remondo J, Gutiérrez F, Guerrero J, Lucha P, Cendrero A, Gutiérrez M, Sánchez J (2008) Development and validation of sinkhole susceptibility models in mantled karst settings. A case study from the Ebro valley evaporite karst (NE Spain). Eng Geol 99:185–197

    Article  Google Scholar 

  • Galve J, Gutiérrez F, Lucha P, Guerrero J, Bonachea J, Remondo J, Cendrero A (2009) Probabilistic sinkhole modelling for hazard assessment. Earth Surf Proc Land 34:437–452

    Article  Google Scholar 

  • Ghafari AS, Alasty A (2004) Design and real-time experimental implementation of gain scheduling PID fuzzy controller for hybrid stepper motor in micro-step operation. In: Proceedings of the IEEE International Conference on Mechatronics, pp 421–426

  • Guzzetti F, Cardinali M, Reichenbach P, Carrara A (2000) Comparing landslide maps: a case study in the upper Tiber River Basin, central Italy. Environ Manag 25:247–263

    Article  Google Scholar 

  • Hermans C, Erickson J, Noordewier T, Sheldon A, Kline M (2007) Collaborative environmental planning in river management: an application of multicriteria decision analysis in the White River Watershed in Vermont. J Environ Manag 84:534–546

    Article  Google Scholar 

  • Hu R, Yue Z, Wang L, Wang S (2004) Review on current status and challenging issues of land subsidence in China. Eng Geol 76:65–77

    Article  Google Scholar 

  • Hu B, Zhou J, Xu S, Chen Z, Wang J, Wang D, Wang L, Guo J, Meng W (2013) Assessment of hazards and economic losses induced by land subsidence in Tianjin Binhai new area from 2011 to 2020 based on scenario analysis. Nat Hazards 66:873–886

    Article  Google Scholar 

  • Jebur MN, Pradhan B, Tehrany MS (2013) Detection of vertical slope movement in highly vegetated tropical area of Gunung pass landslide, Malaysia, using L-band InSAR technique. Geosci J. doi:10.1007/s12303-013-0053-8

    Google Scholar 

  • Julio-Miranda P, Ortíz-Rodríguez A, Palacio-Aponte A, López-Doncel R, Barboza-Gudiño R (2012) Damage assessment associated with land subsidence in the San Luis Potosi-Soledad de Graciano Sanchez metropolitan area, Mexico, elements for risk management. Nat Hazards 64:751–765

    Article  Google Scholar 

  • 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 50:1183–1191

    Article  Google Scholar 

  • Kim KD, Lee S, Oh HJ (2009) Prediction of ground subsidence in Samcheok City, Korea using artificial neural networks and GIS. Environ Geol 58:61–70

    Article  Google Scholar 

  • Lee S, Pradhan B (2006a) Landslide hazard assessment at Cameron Highland Malaysia using frequency ratio and logistic regression models. Geophys Res Abs 8:03241

    Google Scholar 

  • Lee S, Pradhan B (2006b) Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia. J Earth Syst Sci 115:661–672

    Article  Google Scholar 

  • Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4:33–41

    Article  Google Scholar 

  • Lee S, Oh HJ, Kim KD (2010) Statistical spatial modeling of ground subsidence hazard near an abandoned underground coal mine. Disaster Adv 3:11–23

    Google Scholar 

  • Lefsky M, Cohen W, Spies T (2001) An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon. Can J Forest Res 31:78–87

    Article  Google Scholar 

  • Liu Y, Huang HJ (2013) Characterization and mechanism of regional land subsidence in the Yellow River Delta, China. Nat Hazards 68:687–709

    Article  Google Scholar 

  • Mancini F, Stecchi F, Gabbianelli G (2009) GIS-based assessment of risk due to salt mining activities at Tuzla (Bosnia and Herzegovina). Eng Geol 109:170–182

    Article  Google Scholar 

  • Motagh M, Djamour Y, Walter TR, Wetzel HU, Zschau J, Arabi S (2007) Land subsidence in Mashhad Valley, northeast Iran: results from InSAR, levelling and GPS. Geophys J Int 168:518–526

    Article  Google Scholar 

  • Oh HJ, Lee S (2011) Integration of ground subsidence hazard maps of abandoned coal mines in Samcheok, Korea. Int J Coal Geol 86:58–72

    Article  Google Scholar 

  • Park NW (2011) Application of Dempster-Shafer theory of evidence to GIS-based landslide susceptibility analysis. Environ Earth Sci 62:367–376

    Article  Google Scholar 

  • Pourghasemi HR, Mohammady M, Pradhan B (2012a) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. Catena 97:71–84

    Article  Google Scholar 

  • Pourghasemi HR, Pradhan B, Gokceoglu C (2012b) Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards 63:965–996

    Article  Google Scholar 

  • Pourghasemi H, Moradi H, Aghda SF, Gokceoglu C, Pradhan B (2013a) GIS-based landslide susceptibility mapping with probabilistic likelihood ratio and spatial multi-criteria evaluation models (North of Tehran, Iran). Arab J Geosci 68:1–22

    Google Scholar 

  • Pourghasemi H, Pradhan B, Gokceoglu C, Moezzi KD (2013b) A comparative assessment of prediction capabilities of Dempster-Shafer and weights-of-evidence models in landslide susceptibility mapping using GIS. Geomatics Nat Hazards Risk 4:93–118

    Article  Google Scholar 

  • Pradhan B (2010a) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9:1–18

    Google Scholar 

  • Pradhan B (2010b) 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 45:1244–1256

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Pradhan B, Lee S (2007) Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Sci Front 14:143–151

    Article  Google Scholar 

  • Pradhan B, Lee S (2010a) Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models. Environ Earth Sci 60:1037–1054

    Article  Google Scholar 

  • Pradhan B, Lee S (2010b) Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environ Model Softw 25:747–759

    Article  Google Scholar 

  • Pradhan B, Mansor S, Pirasteh S, Buchroithner MF (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32:4075–4087

    Article  Google Scholar 

  • Regmi NR, Giardino JR, Vitek JD (2010) Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology 115:172–187

    Article  Google Scholar 

  • Regmi AD, Yoshida K, Dhital MR, Pradhan B (2013a) Weathering and mineralogical variation in gneissic rocks and their effect in Sangrumba Landslide, East Nepal. Environ Earth Sci 8:1–17. doi:10.1007/s12665-013-2649-8

    Google Scholar 

  • Regmi AD, Yoshida K, Nagata H, Pradhan AMS, Pradhan B, Pourghasemi HR (2013b) The relationship between geology and rock weathering on the rock instability along Mugling-Narayanghat road corridor, Central Nepal Himalaya. Nat Hazards 66:501–532

    Article  Google Scholar 

  • Regmi AD, Devkota KC, Yoshida K, Pradhan B, Pourghasemi HR, Kumamoto T, Akgun A (2014) Application of frequency ratio, statistical index, and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya. Arab J Geosci 7:725–742

    Article  Google Scholar 

  • Safari HO, Pirasteh S, Pradhan B, Gharibvand LK (2010) Use of remote sensing data and GIS tools for seismic hazard assessment for shallow oilfields and its impact on the settlements at Masjed-i-Soleiman Area, Zagros Mountains, Iran. Remote Sens Basel 2:1364–1377

    Article  Google Scholar 

  • Scheet P, Stephens M (2006) A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase. Am J Hum Genet 78:629–644

    Article  Google Scholar 

  • Sterlacchini S, Frigerio S, Giacomelli P, Brambilla M (2007) Landslide risk analysis: a multi-disciplinary methodological approach. Nat Hazard Earth Syst 7:657–675

    Article  Google Scholar 

  • Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45:665–679

    Article  Google Scholar 

  • Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J Hydrol 504:69–79. doi:10.1016/j.jhydrol.2013.09.034

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012a) Landslide susceptibility mapping at Hoa Binh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Comput Geosci 45:199–211

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Lofman O, Revhaug I, Dick OB (2012b) Spatial prediction of landslide hazards in Hoa Binh province (Vietnam): a comparative assessment of the efficacy of evidential belief functions and fuzzy logic models. Catena 96:28–40

    Article  Google Scholar 

  • Tien Bui D, Pradhan B, Revhaug I, Nguyen DB, Pham HV, Bui QN (2013) A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam). Geomatics Nat Hazards Risk 1–30. doi:10.1080/19475705.2013.843206

  • Tralli DM, Blom RG, Zlotnicki V, Donnellan A, Evans DL (2005) Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS J Photogramm 59:185–198

    Article  Google Scholar 

  • Wan S, Lei TC (2009) A knowledge-based decision support system to analyze the debris-flow problems at Chen-Yu-Lan River, Taiwan. Knowl Based Syst 22:580–588

    Article  Google Scholar 

  • Wan S, Lei T, Chou T (2010a) A novel data mining technique of analysis and classification for landslide problems. Nat Hazards 52:211–230

    Article  Google Scholar 

  • Wan S, Lei T, Chou TY (2010b) An enhanced supervised spatial decision support system of image classification: consideration on the ancillary information of paddy rice area. Int J Geogr Inf Sci 24:623–642

    Article  Google Scholar 

  • Wan S, Lei TC, Chou TY (2012) A landslide expert system: image classification through integration of data mining approaches for multi-category analysis. Int J Geogr Inf Sci 26:747–770

    Article  Google Scholar 

  • Wang Y, Liao M, Li D, Lin H (2004) Subsidence monitoring in urban area using multi-temporal InSAR data: a case study in China. In: Proceedings of 11th SPIE International Symposium on Remote Sensing, Spain, vol 323, pp 323–330

  • Wang WD, Xie CM, Du XG (2009) Landslides susceptibility mapping based on geographical information system, GuiZhou, south-west China. Environ Geol 58:33–43

    Article  Google Scholar 

  • Wu X, Jiang XW, Chen YF, Tian H, Xu NX (2009) The influences of mining subsidence on the ecological environment and public infrastructure: a case study at the Haolaigou iron ore mine in Baotou, China. Environ Earth Sci 59:803–810

    Article  Google Scholar 

  • Xu C, Xu X, Dai F, Saraf AK (2012) Comparison of different models for susceptibility mapping of earthquake triggered landslides related with the 2008 Wenchuan earthquake in China. Comput Geosci 46:317–329

    Article  Google Scholar 

  • Yang T, Shen Y, van der Lee S, Solomon SC, Hung SH (2006) Upper mantle structure beneath the Azores hotspot from finite-frequency seismic tomography. Earth Planet Sci Lett 250:11–26

    Article  Google Scholar 

  • Yilmaz I (2009a) A case study from Koyulhisar (Sivas-Turkey) for landslide susceptibility mapping by artificial neural networks. B Eng Geol Environ 68:297–306

    Article  Google Scholar 

  • Yilmaz I (2009b) Landslide susceptibility mapping using frequency ratio, logistic regression, artificial neural networks and their comparison: a case study from Kat landslides (Tokat–Turkey). Comput Geosci 35:1125–1138

    Article  Google Scholar 

  • Yilmaz I, Marschalko M, Bednarik M (2013) An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ. J Earth Syst Sci 122:371–388

    Article  Google Scholar 

  • Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62:611–623

    Article  Google Scholar 

  • Zhou G, Esaki T, Mori J (2003) GIS-based spatial and temporal prediction system development for regional land subsidence hazard mitigation. Environ Geol 44:665–678

    Article  Google Scholar 

  • Ziaie A, Kumarci K, Ghanizadeh KR, Mahmodinejad A (2009) Prediction of earth fissures development in Sirjan. Res J Environ Sci 3:486–496

    Article  Google Scholar 

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Acknowledgments

Thanks to the Department of Minerals and Geosciences, Malaysia, for providing the geology and structural map of the study area.

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Correspondence to Biswajeet Pradhan.

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Pradhan, B., Abokharima, M.H., Jebur, M.N. et al. Land subsidence susceptibility mapping at Kinta Valley (Malaysia) using the evidential belief function model in GIS. Nat Hazards 73, 1019–1042 (2014). https://doi.org/10.1007/s11069-014-1128-1

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