Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

References

• 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(1-2):15–31.
   Google Scholar
• Bhandary NP, Dahal RK, Timilsina M, Yatabe R. 2013. Rainfall event-based landslide susceptibility zonation mapping. Nat Hazards. 69(1):365–388.
   Google Scholar
• Bi YH. 2014. Study on evaluation and control of Nanshan geologic hazards of Wen County (Master's thesis, Lanzhou University).
   Google Scholar
• Binaghi E, Luzi L, Madella P, Pergalani F, Rampini A. 1998. Slope instability zonation: a comparison between certainty factor and fuzzy Dempster-Shafer approaches. Nat Hazards. 17(1):77–97.
   Google Scholar
• Bonham-Carter GF. 1994. Geographic information systems for geoscientists: modeling with GIS. Ottawa: Pergamon Press; p. 398.
   Google Scholar
• Bonham-Carter GF, Agterberg FP, Wright DF. 1988. Integration of geological datasets for gold exploration in Nova Scotia. Photogramm Eng Remote Sens. 54:1585–1592.
   Google Scholar
• Bourenane H, Bouhadad Y, Guettouche MS, Braham M. 2015. GIS-based landslide susceptibility zonation using bivariate statistical and expert approaches in the city of Constantine (Northeast Algeria). Bull Eng Geol Environ. 74(2):337–355.
   Google Scholar
• Bourenane H, Guettouche MS, Bouhadad Y, Braham M. 2016. Landslide hazard mapping in the constantine city, northeast Algeria using frequency ratio, weighting factor, logistic regression, weights of evidence, and analytical hierarchy process methods. Arab J Geosci. 9:1–24.
   Google Scholar
• Bui DT, Lofman O, Revhaug I, Dick O. 2011. Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Nat Hazards. 59(3):1413–1444.
   Google Scholar
• Chen W, Chai H, Sun X, Wang Q, Ding X, Hong H. 2016. A GIS-based comparative study of frequency ratio, statistical index and weights-of-evidence models in landslide susceptibility mapping. Arab J Geosci. 9:1–16.
   Google Scholar
• Chen W, Pourghasemi HR, Panahi M, Kornejady A, Wang J, Xie X, Cao S. 2017a. Spatial prediction of landslide susceptibility using an adaptive neuro-fuzzy inference system combined with frequency ratio, generalized additive model, and support vector machine techniques. Geomorphology. 297:69–85.
   Google Scholar
• Chen W, Shirzadi A, Shahabi H, Ahmad BB, Zhang S, Hong H, Zhang N. 2017b. A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomat Nat Hazards Risk. 8(2):1955–1977.
   Google Scholar
• Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H. 2017c. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Hazards Risk. 8(2):950–973.
   Google Scholar
• Chung CJF, Fabbri AG. 1993. The representation of geoscience information for data integration. Nat Resour Res. 2:122–139.
   Google Scholar
• Dahal RK, Hasegawa S, Nonomura A, Yamanaka M, Masuda T, Nishino K. 2008. GIS-based weights-of-evidence modelling of rainfall-induced landslides in small catchments for landslide susceptibility mapping. Environ Geol. 54(2):311–324.
   Google Scholar
• Devkota KC, Regmi AD, Pourghasemi HR, Yoshida K, Pradhan B, Ryu IC, Dhital MR, Althuwaynee OF. 2013. Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya. Nat Hazards. 65(1):135–165.
   Google Scholar
• Ermini L, Catani F, Casagli N. 2005. Artificial neural networks applied to landslide susceptibility assessment. Geomorphology. 66(1-4):327–343.
   Google Scholar
• Gayen A, Saha S. 2017. Application of weights-of-evidence (WoE) and evidential belief function (EBF) models for the delineation of soil erosion vulnerable zones: a study on Pathro river basin, Jharkhand, India. Model Earth Syst Environ. 3(3):1123–1139.
   Google Scholar
• Heckeman 1986. Probabilistic interpretation of MYCIN’s certainty factors. In Kanal LN, Lemmer JF, editors. Uncertainty in artificial intelligence. New York: Elsevier; p. 298–311.
   Google Scholar
• Hussin HY, Zumpano V, Reichenbach P, Sterlacchini S, Micu M, Westen CV, Bălteanu D. 2016. Different landslide sampling strategies in a grid-based bi-variate statistical susceptibility model. Geomorphology. 253:508–523.
   Google Scholar
• Intarawichian N, Dasananda S. 2011. Frequency ratio model based landslide susceptibility mapping in lower Mae Chaem watershed, Northern Thailand. Environ Earth Sci. 64(8):2271–2285.
   Google Scholar
• Jaafari A, Najafi A, Pourghasemi HR, Rezaeian J, Sattarian A. 2014. GIS-based frequency ratio and index of entropy models for landslide susceptibility assessment in the Caspian forest, northern Iran. Int J Environ Sci Technol. 11(4):909–926.
   Google Scholar
• Kamp U, Owen LA, Growley BJ, Khattak GA. 2010. Back analysis of landslide susceptibility zonation mapping for the 2005 Kashmir earthquake: an assessment of the reliability of susceptibility zoning maps. Nat Hazards. 54(1):1–25.
   Google Scholar
• Kanungo DP, Sarkar S, Sharma S. 2011. Combining neural network with fuzzy, certainty factor and likelihood ratio concepts for spatial prediction of landslides. Nat Hazards. 59(3):1491–1512.
   Google Scholar
• 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.
   Google Scholar
• Kayastha P, Dhital MR, De Smedt F. 2012. Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal. Nat Hazards. 63(2):479–498.
   Google Scholar
• Kayastha P. 2015. Landslide susceptibility mapping and factor effect analysis using frequency ratio in a catchment scale: a case study from Garuwa sub-basin, East Nepal. Arab J Geosci. 8(10):8601–8613.
   Google Scholar
• Lee S, Pradhan B. 2007. Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides. 4(1):33–41.
   Google Scholar
• Liu M, Chen X, Yang S. 2014. Collapse landslide and mudslides hazard zonation. In Landslide Science for a Safer Geoenvironment. Cham: Springer; p. 457–462.
   Google Scholar
• Luzi L, Pergalani F. 1999. Slope instability in static and dynamic conditions for urban planning: the ‘oltre po pavese’ case history (regione lombardia – italy). Natural Hazards. 20(1):57–82.
   Google Scholar
• McQuillan A, Canbulat I, Payne D, Oh J. 2018. New risk assessment methodology for coal mine excavated slopes. Int J Min. Sci Technol. 28(4):583–592.
   Google Scholar
• Mohammady M, Pourghasemi HR, Pradhan B. 2012. Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. J Asian Earth Sci. 61:221–236.
   Google Scholar
• Oh HJ, Pradhan B. 2011. Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Comput Geosci. 37:1264–1276.
   Google Scholar
• Pham BT, Bui DT, Prakash I, Dholakia MB. 2017. Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena. 149:52–63.
   Google Scholar
• Pourghasemi HR, Moradi HR, Aghda SF. 2013a. Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances. Nat Hazards. 69(1):749–779.
   Google Scholar
• Pourghasemi HR, Pradhan B, Gokceoglu C, Mohammadi M, Moradi HR. 2013b. Application of weights-of-evidence and certainty factor models and their comparison in landslide susceptibility mapping at Haraz watershed, Iran. Arab J Geosci. 6(7):2351–2365.
   Google Scholar
• Pradhan AMS, Kim YT. 2014. Relative effect method of landslide susceptibility zonation in weathered granite soil: a case study in Deokjeok-ri Creek, South Korea. Nat Hazards. 72(2):1189–1217.
   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.
   Google Scholar
• Qiao W, Li W, Li T, Chang J, Wang Q. 2017. Effects of coal mining on shallow water resources in semiarid regions: a case study in the Shennan mining area, Shaanxi, China. Mine Water Environ. 36(1):104–113.
   Google Scholar
• Rahmati O, Pourghasemi HR, Zeinivand H. 2016. Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the golastan province, iran. Geocarto Int. 31(1):42–70.
   Google Scholar
• Razavizadeh S, Solaimani K, Massironi M, Kavian A. 2017. Mapping landslide susceptibility with frequency ratio, statistical index, and weights of evidence models: a case study in northern Iran. Environ Earth Sci. 76:499.
   Google Scholar
• Regmi NR, Giardino JR, Vitek JD. 2010. Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology. 115(1-2):172–187.
   Google Scholar
• Saito H, Nakayama D, Matsuyama H. 2009. Comparison of landslide susceptibility based on a decision-tree model and actual landslide occurrence: the Akaishi Mountains, Japan. Geomorphology. 109(3-4):108–121.
   Google Scholar
• Sezer EA, Pradhan B, Gokceoglu C. 2011. Manifestation of an adaptive neuro-fuzzy model on landslide susceptibility mapping: Klang valley, Malaysia. Expert Syst Appl. 38(7):8208–8219.
   Google Scholar
• Shortliffe EH, Buchanan BG. 1975. A model of inexact reasoning in medicine. Math Biosci. 23(3-4):351–379.
   Google Scholar
• Solaimani K, Mousavi SZ, Kavian A. 2013. Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab J Geosci. 6(7):2557–2569.
   Google Scholar
• Sujatha ER, Kumaravel P, Rajamanickam GV. 2014. Assessing landslide susceptibility using Bayesian probability-based weight of evidence model. Bull Eng Geol Environ. 73(1):147–161.
   Google Scholar
• Sujatha ER, Rajamanickam GV, Kumaravel P. 2012. Landslide susceptibility analysis using Probabilistic Certainty Factor Approach: a case study on Tevankarai stream watershed, India. J Earth Syst Sci. 121(5):1337–1350.
   Google Scholar
• Sujatha ER, Sridhar V. 2017. Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills, Tamil Nadu (India. J Earth Syst Sci. 126:116.
   Google Scholar
• Sujatha ER. 2017. An empirical study to identify factors causing landslides using multiple linear regression model (MLR). Disaster Adv. 10:18–26.
   Google Scholar
• Tsangaratos P, Ilia I. 2016. Landslide susceptibility mapping using a modified decision tree classifier in the Xanthi perfection, Greece. Landslides. 13(2):305–320.
   Google Scholar
• Van Westen CJ. 1993. Application of geographic information systems to landslide hazard zonation. ITC, International institute for aerospace survey and earth sciences. Enschede, The Nethelands: ITC Publication.
   Google Scholar
• Van Westen CJ. 2000. The modelling of landslide hazards using GIS. Surv Geophys. 21(2/3):241–255.
   Google Scholar
• Wang LJ, Guo M, Sawada K, Lin J, Zhang J. 2016. A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci J. 20(1):117–136.
   Google Scholar
• Wang J, Chen C. 2017. Three dimensional back analysis for stability of slope dumped on weak basement. J China Univ Min Technol. 46:474–479.
   Google Scholar
• Wang P, Wang H, Xu S, Yu Y, Wang L. 2018b. Dynamic response of loess-wearthered rock contact surface slope. J China Univ Min Technol. 47:893–899.
   Google Scholar
• Wang Q, Li W. 2017. A GIS-based comparative evaluation of analytical hierarchy process and frequency ratio models for landslide susceptibility mapping. Phys Geogr. 38(4):318–337.
   Google Scholar
• Wang Q, Li W, Chen W, Bai H. 2015. GIS-based assessment of landslide susceptibility using certainty factor and index of entropy models for the Qianyang County of Baoji city, China. J Earth Syst Sci. 124(7):1399–1415.
   Google Scholar
• Wang Q, Li W, Li T, Li X, Liu S. 2018a. Goaf water storage and utilization in arid regions of northwest China: A case study of Shennan coal mine district. J Clean Prod. 202:33–44.
   Google Scholar
• Wang Q, Li W, Wu Y, Pei Y, Xing M, Yang D. 2016. A comparative study on the landslide susceptibility mapping using evidential belief function and weights of evidence models. J Earth Syst Sci. 125(3):645–662.
   Google Scholar
• Wang X, Zhang L, Wang S, Lari S. 2014. Regional landslide susceptibility zoning with considering the aggregation of landslide points and the weights of factors. Landslides. 11(3):399–409.
   Google Scholar
• Xie Y. 2013. Characteristic analysis and risk assessment of geological disaster in Wen County. Master's thesis, Lanzhou University.
   Google Scholar
• 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.
   Google Scholar
• Xu W, Yu W, Jing S, Zhang G, Huang J. 2013. Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Nat Hazards. 65(3):1379–1392.
   Google Scholar
• Yan C, Li W, Zhang Z, Shi Y. 2017. Impact analysis of creep movement of Zhengjiawan landslide to bridge across. J Eng Geol. 25:416–423.
   Google Scholar
• 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.
   Google Scholar
• Yesilnacar E, Topal T. 2005. Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Eng Geol. 79(3-4):251–266.
   Google Scholar
• Yilmaz I. 2009. 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.
   Google Scholar
• Yin KL, Yan TZ. 1988. Statistical prediction model for slope instability of metamorphosed rocks. In Proceedings of the 5th international symposium on landslides, Lausanne, Switzerland (Vol. 2, pp. 1269–1272).
   Google Scholar
• Zhang T, Cai Q, Han L, Shu J, Zhou W. 2017. 3D stability analysis method of concave slope based on the Bishop method. Int J Min Sci Technol. 27(2):365–370.
   Google Scholar