Document Type : Review Paper
Authors
Department of Civil Engineering, Chandigarh University, Mohali, Punjab, India
Abstract
Natural hazards are naturally occurring phenomena that might lead to a negative impact on the environment and also on the life of living beings. These hazards are caused due to adverse conditions of weather and climate events, and also due to certain human activities that are harmful to the environment. Natural hazards include tsunamis, earthquakes, volcanic activity, landslides, etc. Among these natural hazards, landslides are among the most common natural hazards resulting in loss of life and property each year, leading to socio-economic impact; thus to avoid such losses, a comprehensive study of landslides is required. Landslides generally occur in hill region with steep slopes, heavy precipitation, loose shear strength of soil or due to many human activities like afforestation or construction activities. To resolve the problem of landslides in a hilly region, much research is conducted annually, providing a predicted landslide susceptibility zonation (LSZ) mapping of the area of research. The predicted landslide susceptibility maps are verified based on the past landslide data, an area under the curve (AUC), and other methods to provide an accurate map for landslide susceptibility in any area. In this study,93 research articles are reviewed for analysis of LSZ, and various observations are made based on the recent trends followed by various researchers over the world over the past ten years. The study can be useful for many researchers to practice their research on landslide susceptibility zonation.
Keywords
[1]. Varnes, D.J. (1984). Commission on the Landslides of the IAEG, UNESCO. Landslide Hazard Zonation: A Review of Principles and Practice, 3, 61.
[2]. Brusden, D., Mudslides; In: Brusden, D, Prior, D. editors (1984). Slope Instability. Chichester: Wiley, 363–418.
[3]. Hutchinson, J.N. (1988). Morphological and geotechnical parameters of landslides in relation to geology and hydrogeology. In: Ch. Bonnard (Ed.): Landslides. Proceedings 5th International Conference on Landslides. Lausanne, 1, 3-35
[4]. Courture, R. (2011). Landslide Terminology-National Technical Guidelines and Best Practices on Landslides. Geological Survey of Canada, 131-138.
[5]. Mersha, T. and Meten, M. (2020). GIS-based landslide susceptibility mapping and assessment using bivariate statistical methods in Simada area, northwestern Ethiopia. Geoenvironmental disasters, 7(1): 1-22.
[6]. Mohan, D., Sarswat, A., Ok, Y.S., and Pittman Jr, C.U. (2014). Organic and inorganic contaminants removal from water with biochar, a renewable, low cost and sustainable adsorbent–a critical review. Bioresource technology, 160, 191-202.
[7]. Kahlon, S., Chandel, V.B., and Brar, K.K. (2014). Landslides in Himalayan mountains: a study of Himachal Pradesh, India. Int J IT Eng Appl Sci Res, 3(9): 28-34.
[8]. Crozier, M.J. (2010). Deciphering the effect of climate change on landslide activity: A review. Geomorphology. 124 (3-4): 260-267.
[9]. Arnous, M.O. (2011). Integrated remote sensing and GIS techniques for landslide hazard zonation: a case study Wadi Watier area, South Sinai, Egypt. Journal of Coastal Conservation. 15 (4): 477-497.
[10]. Banshtu, R.S., Versain, L.D., and Pandey, D.D. (2020). Risk assessment using quantitative approach: central Himalaya, Kullu, Himachal Pradesh, India. Arabian Journal of Geosciences, 13(5): 1-11.
[11]. Feng, Z.Y., Lu, Y.R., and Shen, Z.R. (2021). A numerical simulation of seismic signals of coseismic landslides. Engineering Geology, 289, 106191.
[12]. Temesgen, B., Mohammed, M.U., and Korme, T. (2001). Natural hazard assessment using GIS and remote sensing methods, with particular reference to the landslides in the Wondogenet area, Ethiopia. Physics and Chemistry of the Earth, Part C: Solar, Terrestrial & Planetary Science, 26(9): 665-675.
[13]. Dai, F. C.and Lee, C.F. (2002). Landslide characteristics and slope instability modeling using GIS, Lantau Island, Hong Kong. Geomorphology, 42(3-4): 213-228.
[14]. Ohlmacher, G.C.and Davis, J.C. (2003). Using multiple logistic regression and GIS technology to predict landslide hazard in northeast Kansas, USA. Engineering geology, 69(3-4): 331-343.
[15]. Lee, S., Ryu, J.H., Won, J.S., and Park, H.J. (2004). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Engineering Geology, 71(3-4): 289-302.
[16]. Ayalew, L., Yamagishi, H.,and Ugawa, N. (2004) Landslide susceptibility mapping using GIS-based weighted linear combination, the case in Tsugawa area of Agano River, Niigata Prefecture, Japan. Landslides, 1, 73-81.
[17]. Sarkar, S.and Kanungo, D.P. (2004). An integrated approach for landslide susceptibility mapping using remote sensing and GIS. Photogrammetric Engineering & Remote Sensing, 70(5): 617-625.
[18]. Ayalew, L., Yamagishi, H., Marui, H., and Kanno, T. (2005). Landslides in Sado Island of Japan: Part II. GIS-based susceptibility mapping with comparisons of results from two methods and verifications. Engineering geology, 81(4): 432-445.
[19]. Ayalew, L.and 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.
[20]. Ermini, L., Catani, F., and Casagli, N. (2005). Artificial neural networks applied to landslide susceptibility assessment. Geomorphology, 66(1-4): 327-343.
[21]. Yesilnacar, E. and Topal, T.A.M.E.R. (2005). Landslide susceptibility mapping: a comparison of logistic regression and neural networks methods in a medium scale study, Hendek region (Turkey). Engineering Geology, 79(3-4): 251-266.
[22]. Gomez, H. and Kavzoglu, T. (2005). Assessment of shallow landslide susceptibility using artificial neural networks in Jabonosa River Basin, Venezuela. Engineering Geology, 78(1-2): 11-27.
[23]. Kanungo, D.P., Arora, M.K., Sarkar, S., and Gupta, R.P. (2006). A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Engineering geology, 85(3-4): 347-366.
[24]. Van Den Eeckhaut, M., Vanwalleghem, T., Poesen, J., Govers, G., Verstraeten, G., and Vandekerckhove, L. (2006). Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology, 76(3-4): 392-410.
[25]. Neaupane, K.M. and Piantanakulchai, M. (2006). Analytic network process model for landslide hazard zonation. Engineering geology, 85(3-4): 281-294.
[26]. Biswajeet, P. and Saro, L. (2007). Utilization of optical remote sensing data and GIS tools for regional landslide hazard analysis using an artificial neural network model. Earth Science Frontiers, 14(6): 143-151.
[27]. Neuhäuser, B. and Terhorst, B. (2007). Landslide susceptibility assessment using “weights-of-evidence” applied to a study area at the Jurassic escarpment (SW-Germany). Geomorphology, 86(1-2): 12-24.
[28]. Thiery, Y., Malet, J. P., Sterlacchini, S., Puissant, A., and Maquaire, O. (2007). Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology, 92(1-2): 38-59.
[29]. Akgun, A., Dag, S., and Bulut, F. (2008). Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environmental Geology, 54(6): 1127-1143.
[30]. Kamp, U., Growley, B.J., Khattak, G.A., and Owen, L.A. (2008). GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology, 101(4): 631-642.
[31]. Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena, 72(1): 1-12.
[32]. Melchiorre, C., Matteucci, M., Azzoni, A., and Zanchi, A. (2008). Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology, 94(3-4): 379-400.
[33]. García-Rodríguez, M.J., Malpica, J.A., Benito, B., and Díaz, M. (2008). Susceptibility assessment of earthquake-triggered landslides in El Salvador using logistic regression. Geomorphology, 95(3-4): 172-191.
[34]. Nefeslioglu, H.A., Gokceoglu, C., and Sonmez, H. (2008). An assessment on the use of logistic regression and artificial neural networks with different sampling strategies for the preparation of landslide susceptibility maps. Engineering Geology, 97(3-4): 171-191.
[35]. Wang, W.D., Xie, C.M., and Du, X.G. (2009). Landslides susceptibility mapping in Guizhou province based on fuzzy theory. Mining Science and Technology (China): 19(3): 399-404.
[36]. 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). Computers & Geosciences, 35(6): 1125-1138.
[37]. Kawabata, D. and Bandibas, J. (2009). Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN). Geomorphology, 113(1-2): 97-109.
[38]. Saito, H., Nakayama, D., and 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.
[39]. Kouli, M., Loupasakis, C., Soupios, P., and Vallianatos, F. (2010). Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece. Natural hazards, 52(3): 599-621.
[40]. Bai, S. B., Wang, J., Lü, G. N., Zhou, P.G., Hou, S.S., and Xu, S.N. (2010). GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology, 115(1-2): 23-31.
[41]. Das, I., Sahoo, S., Van Westen, C., Stein, A., and Hack, R. (2010). Landslide susceptibility assessment using logistic regression and its comparison with a rock mass classification system, along a road section in the northern Himalayas (India). Geomorphology, 114(4): 627-637.
[42]. Nandi, A. and Shakoor, A. (2010). A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Engineering Geology, 110(1-2): 11-20.
[43]. Pradhan, B., Lee, S., and Buchroithner, M.F. (2010). A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Computers, Environment and Urban Systems, 34(3): 216-235.
[44]. Regmi, N.R., Giardino, J.R., and Vitek, J.D. (2010). Modeling susceptibility to landslides using the weight of evidence approach: Western Colorado, USA. Geomorphology, 115(1-2): 172-187.
[45]. Yalcin, A., Reis, S., Aydinoglu, A.C., and 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.
[46]. Ghosh, S., Carranza, E.J.M., Van Westen, C.J., Jetten, V.G., and Bhattacharya, D.N. (2011). Selecting and weighting spatial predictors for empirical modeling of landslide susceptibility in the Darjeeling Himalayas (India). Geomorphology, 131(1-2): 35-56.
[47]. Khezri, S. (2011). Landslide susceptibility in the Zab Basin, northwest of Iran. Procedia-Social and Behavioral Sciences, 19, 726-731.
[48]. Oh, H.J. and Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & geosciences, 37(9): 1264-1276.
[49]. Ilanloo, M. (2011). A comparative study of fuzzy logic approach for landslide susceptibility mapping using GIS: An experience of Karaj dam basin in Iran. Procedia-Social and Behavioral Sciences, 19, 668-676.
[50]. Choi, J., Oh, H. J., Lee, H.J., Lee, C., and Lee, S. (2012). Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering geology, 124, 12-23.
[51]. Bui, D.T., Pradhan, B., Lofman, O., Revhaug, I., and Dick, O.B. (2012). Landslide susceptibility mapping at HoaBinh province (Vietnam) using an adaptive neuro-fuzzy inference system and GIS. Computers & Geosciences, 45, 199-211.
[52]. Mohammady, M., Pourghasemi, H.R., and Pradhan, B. (2012). Landslide susceptibility mapping at Golestan Province, Iran: a comparison between frequency ratio, Dempster–Shafer, and weights-of-evidence models. Journal of Asian Earth Sciences, 61, 221-236.
[53]. Xu, C., Dai, F., Xu, X., and Lee, Y.H. (2012). GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology, 145, 70-80.
[54]. Das, I., Stein, A., Kerle, N., and Dadhwal, V.K. (2012). Landslide susceptibility mapping along road corridors in the Indian Himalayas using Bayesian logistic regression models. Geomorphology, 179, 116-125.
[55]. 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. Computers & Geosciences, 51, 350-365.
[56]. Kayastha, P., Dhital, M.R., and De Smedt, F. (2013). Application of the analytical hierarchy process (AHP) for landslide susceptibility mapping: A case study from the Tinau watershed, west Nepal. Computers & Geosciences, 52, 398-408.
[57]. Ozdemir, A. and Altural, T. (2013). A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan Mountains, SW Turkey. Journal of Asian Earth Sciences, 64, 180-197.
[58]. Pareek, N., Pal, S., Sharma, M.L., and Arora, M.K. (2013). Study of effect of seismic displacements on landslide susceptibility zonation (LSZ) in Garhwal Himalayan region of India using GIS and remote sensing techniques. Computers & Geosciences, 61, 50-63.
[59]. Wang, L.J., Sawada, K., and Moriguchi, S. (2013). Landslide susceptibility analysis with logistic regression model based on FCM sampling strategy. Computers & Geosciences, 57, 81-92.
[60]. Chen, W., Li, W., Hou, E., Zhao, Z., Deng, N., Bai, H., and Wang, D. (2014). Landslide susceptibility mapping based on GIS and information value model for the Chencang District of Baoji, China. Arabian Journal of Geosciences, 7(11): 4499-4511.
[61]. Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., and Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124-135.
[62]. Niu, R., Wu, X., Yao, D., Peng, L., Ai, L., and Peng, J. (2014). Susceptibility assessment of landslides triggered by the Lushan earthquake, April 20, 2013, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(9): 3979-3992.
[63]. Conforti, M., Pascale, S., Robustelli, G., and Sdao, F. (2014). Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy). Catena, 113, 236-250.
[64]. Ahmed, B. (2015). Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6): 1077-1095.
[65]. Guo, C., Montgomery, D. R., Zhang, Y., Wang, K., and Yang, Z. (2015). Quantitative assessment of landslide susceptibility along the Xianshuihe fault zone, Tibetan Plateau, China. Geomorphology, 248, 93-110.
[66]. Wang, L. J., Guo, M., Sawada, K., Lin, J., and Zhang, J. (2015). Landslide susceptibility mapping in Mizunami City, Japan: A comparison between logistic regression, bivariate statistical analysis and multivariate adaptive regression spline models. Catena, 135, 271-282.
[67]. Conoscenti, C., Ciaccio, M., Caraballo-Arias, N.A., Gómez-Gutiérrez, Á., Rotigliano, E., and Agnesi, V. (2015). Assessment of susceptibility to earth-flow landslide using logistic regression and multivariate adaptive regression splines: a case of the Belice River basin (western Sicily, Italy). Geomorphology, 242, 49-64.
[68]. Anbalagan, R., Kumar, R., Lakshmanan, K., Parida, S., and Neethu, S. (2015). Landslide hazard zonation mapping using frequency ratio and fuzzy logic approach, a case study of Lachung Valley, Sikkim. Geoenvironmental Disasters, 2(1): 1-17.
[69]. Dehnavi, A., Aghdam, I.N., Pradhan, B., and Varzandeh, M.H.M. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148.
[70]. Leonardi, G., Palamara, R., and Cirianni, F. (2016). Landslide susceptibility mapping using a fuzzy approach. Procedia engineering, 161, 380-387.
[71]. Kumar, R. and Anbalagan, R. (2016). Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region, Uttarakhand. Journal of the Geological Society of India, 87(3): 271-286.
[72]. Erener, A., Mutlu, A., and Düzgün, H.S. (2016). A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA): logistic regression (LR) and association rule mining (ARM). Engineering geology, 203, 45-55.
[73]. Zhang, G., Cai, Y., Zheng, Z., Zhen, J., Liu, Y., and Huang, K. (2016). Integration of the statistical index method and the analytic hierarchy process technique for the assessment of landslide susceptibility in Huizhou, China. Catena, 142, 233-244.
[74]. Patriche, C.V., Pirnau, R., Grozavu, A., and Rosca, B. (2016). A comparative analysis of binary logistic regression and analytical hierarchy process for landslide susceptibility assessment in the Dobrov River Basin, Romania. Pedosphere, 26(3): 335-350.
[75]. Chimidi, G., Raghuvanshi, T.K., and Suryabhagavan, K.V. (2017). Landslide hazard evaluation and zonation in and around Gimbi town, western Ethiopia—a GIS-based statistical approach. Applied Geomatics, 9(4): 219-236.
[76]. Kumar, D., Thakur, M., Dubey, C.S., and Shukla, D.P. (2017). Landslide susceptibility mapping & prediction using support vector machine for Mandakini River Basin, Garhwal Himalaya, India. Geomorphology, 295, 115-125.
[77]. Nicu, I.C. (2017). Frequency ratio and GIS-based evaluation of landslide susceptibility applied to cultural heritage assessment. Journal of Cultural Heritage, 28, 172-176.
[78]. Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D.T., and Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147-160.
[79]. Singh, K. and Kumar, V. (2017). Landslide hazard mapping along national highway-154A in Himachal Pradesh, India using information value and frequency ratio. Arabian Journal of Geosciences, 10(24): 1-18.
[80]. Chawla, A., Chawla, S., Pasupuleti, S., Rao, A.C.S., Sarkar, K., and Dwivedi, R. (2018). Landslide susceptibility mapping in darjeeling Himalayas, India. Advances in Civil Engineering, 2018.
[81]. Kumar, D. and Rawat, A. (2018). Study and prediction of landslide in Uttarkashi, Uttarakhand, India using GIS and ANN. American Journal of Neural Networks and Applications, 3(6): 63.
[82]. Thai Pham, B., Prakash, I., Dou, J., Singh, S.K., Trinh, P.T., Trung Tran, H., and Shirzadi, A. (2018). A novel hybrid approach of landslide susceptibility modeling using rotation forest ensemble and different base classifiers. Geocarto Int, 14, 1-38.
[83]. Aditian, A., Kubota, T., and Shinohara, Y. (2018). Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia. Geomorphology, 318, 101-111.
[84]. Mandal, B. and Mandal, S. (2018). Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Advances in Space Research, 62(11): 3114-3132.
[85]. Abija, F.A., Nwosu, J.I., Ifedotun, A.I., and Osadebe, C.C. (2019). Landslide susceptibility assessment of Calabar, Nigeria using Geotechnical. Remote Sensing and Multi-Criteria Decision Analysis: Im-plications for urban planning and development, 774-788.
[86]. Bera, A., Mukhopadhyay, B.P., and Das, D. (2019). Landslide hazard zonation mapping using multi-criteria analysis with the help of GIS techniques: a case study from Eastern Himalayas, Namchi, South Sikkim. Natural Hazards, 96(2): 935-959.
[87]. Pham, B. T., Prakash, I., Singh, S.K., Shirzadi, A., Shahabi, H., and Bui, D.T. (2019). Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches. Catena, 175, 203-218.
[88]. Demir, G. (2019). GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey). Catena, 183, 104211.
[89]. Shahri, A.A., Spross, J., Johansson, F., and Larsson, S. (2019). Landslide susceptibility hazard map in southwest Sweden using artificial neural network. Catena, 183, 104225.
[90]. Koley, B., Nath, A., Bhattacharya, S., Saraswati, S., and Ray, B.C. (2020). GIS-based Landslide Hazard Zonation Mapping by Weighted Overlay Method on the Road Corridor of North Sikkim Himalayas, India.
[91]. Chowdhuri, I., Pal, S.C., Arabameri, A., Ngo, P.T.T., Chakrabortty, R., Malik, S., and Roy, P. (2020). Ensemble approach to develop landslide susceptibility map in landslide dominated Sikkim Himalayan region, India. Environmental Earth Sciences, 79(20): 1-28.
[92]. Sharma, A., Sur, U., Singh, P., Rai, P.K., and Srivastava, P.K. (2020). Probabilistic landslide hazard assessment using statistical information value (SIV) and GIS techniques: A case study of Himachal Pradesh, India. Techniques for Disaster Risk Management and Mitigation, 197-208.
[93]. Banshtu, R.S., Versain, L.D., and Pandey, D.D. (2020). Risk assessment using quantitative approach: central Himalaya, Kullu, Himachal Pradesh, India. Arab J Geosci 13: 1–11.
[94]. Abu El-Magd, S.A., Ali, S. A., and Pham, Q.B. (2021). Spatial modeling and susceptibility zonation of landslides using random forest, naïve bayes and K-nearest neighbor in a complicated terrain. Earth Science Informatics, 14(3): 1227-1243.
[95]. Getachew, N. and Meten, M. (2021). Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenvironmental Disasters, 8(1): 1-22.
[96]. Tran, T.H., Dam, N.D., Jalal, F.E., Al-Ansari, N., Ho, L.S., Phong, T.V., and Pham, B.T. (2021). GIS-based soft computing models for landslide susceptibility mapping: A case study of pithoragarh district, uttarakhand state, India. Mathematical problems in Engineering, 2021, 1-19.
[97]. Ngo, T.Q., Dam, N.D., Al-Ansari, N., Amiri, M., Phong, T.V., Prakash, I., and Pham, B.T. (2021). Landslide susceptibility mapping using single machine learning models: a case study from Pithoragarh District, India. Advances in civil engineering, 2021, 1-19.
[98]. Abdo, H. G. (2022). Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria. International Journal of Environmental Science and Technology, 19(4): 2599-2618.
[99]. Mekonnen, A.A., Raghuvanshi, T.K., Suryabhagavan, K.V., and Kassawmar, T. (2022). GIS-based landslide susceptibility zonation and risk assessment in complex landscape: A case of Beshilo watershed, northern Ethiopia. Environmental Challenges, 8, 100586.
[100]. Dam, N.D., Amiri, M., Al-Ansari, N., Prakash, I., Le, H. V., Nguyen, H.B.T., and Pham, B.T. (2022). Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India. Advances in Civil Engineering, 2022.
[101]. Khaliq, A.H., Basharat, M., Riaz, M.T., Riaz, M.T., Wani, S., Al-Ansari, N., and Linh, N.T.T. (2022). Spatiotemporal landslide susceptibility mapping using machine learning models: A case study from district Hattian Bala, NW Himalaya, Pakistan. Ain Shams Engineering Journal, 101907.
[102]. Alsabhan, A.H., Singh, K., Sharma, A., Alam, S., Pandey, D.D., Rahman, S.A.S., and Munshi, F.M. (2022). Landslide susceptibility assessment in the Himalayan range based along Kasauli–Parwanoo road corridor using weight of evidence, information value, and frequency ratio. Journal of King Saud University-Science, 34(2): 101759.
[103]. Saha, A. and Saha, S. (2022). Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region. Artificial Intelligence in Geosciences, 3, 14-27.
[104]. Kumar, A., Sharma, R.K., andBansal, V.K. (2019). GIS-based comparative study of information value and frequency ratio method for landslide hazard zonation in a part of mid-Himalaya in Himachal Pradesh. Innovative Infrastructure Solutions, 4(1): 1-17.
[105]. Gudiyangada Nachappa, T., Kienberger, S., Meena, S.R., Hölbling, D., and Blaschke, T. (2020). Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 11(1): 572-600.
[106]. Mind’je, R., Li, L., Nsengiyumva, J.B., Mupenzi, C., Nyesheja, E.M., Kayumba, P.M., and Hakorimana, E. (2020). Landslide susceptibility and influencing factors analysis in Rwanda. Environment, Development and Sustainability, 22(8): 7985-8012.
[107]. Mehrabi, M. (2022). Landslide susceptibility zonation using statistical and machine learning approaches in Northern Lecco, Italy. Natural Hazards, 111(1): 901-937.
[108]. Thanh, D.Q., Nguyen, D.H., Prakash, I., Jaafari, A., Nguyen, V.T., Van Phong, T., and Pham, B.T. (2020). GIS based frequency ratio method for landslide susceptibility mapping at Da Lat City, Lam Dong province, Vietnam. Vietnam J Earth Sci, 42(1): 55-66.
[109]. Pham, Q.B., Achour, Y., Ali, S. A., Parvin, F., Vojtek, M., Vojteková, J., and Anh, D.T. (2021). A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping. Geomatics, Natural Hazards and Risk, 12(1): 1741-1777.
[110]. Babitha, B.G., Danumah, J.H., Pradeep, G.S., Costache, R., Patel, N., Prasad, M.K., and Kuriakose, S.L. (2022). A framework employing the AHP and FR methods to assess the landslide susceptibility of the Western Ghats region in Kollam district. Safety in Extreme Environments, 4(2): 171-191.
[111]. Bourenane, H., Meziani, A.A., and Benamar, D.A. (2021). Application of GIS-based statistical modeling for landslide susceptibility mapping in the city of Azazga, Northern Algeria. Bulletin of Engineering Geology and the Environment, 80(10): 7333-7359.
[112]. Chen, Z., Song, D., Juliev, M., and Pourghasemi, H.R. (2021). Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model. Environmental Earth Sciences, 80(8): 1-19.
[113]. Wubalem, A. (2021). Landslide susceptibility mapping using statistical methods in Uatzau catchment area, northwestern Ethiopia. Geoenvironmental Disasters, 8(1): 1-21.
[114]. Bopche, L. and Rege, P.P. (2022). Landslide susceptibility mapping: an integrated approach using geographic information value, remote sensing, and weight of evidence method. Geotechnical and Geological Engineering, 1-13.
[115]. Akinci, H., and Yavuz Ozalp, A. (2021). Landslide susceptibility mapping and hazard assessment in Artvin (Turkey) using frequency ratio and modified information value model. Acta Geophysica, 69(3): 725-745.
[116]. Tang, R. X., Yan, E. C., Wen, T., Yin, X. M., and Tang, W. (2021). Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping. Sustainability, 13(7): 3803.
[117]. Wu, C., Zhao, D., Liu, C., Jiao, Y., Liu, Z., Liu, J., and Feng, Z. (2020). Landslide susceptibility assessment of Longchuan County based on GIS and information value model. Northwestern Geology, 53(2): 308-320.
[118]. Zhao, B., Ge, Y., and Chen, H. (2021). Landslide susceptibility assessment for a transmission line in Gansu Province, China by using a hybrid approach of fractal theory, information value, and random forest models. Environmental Earth Sciences, 80(12): 1-23.
[119]. Chen, T., Zhu, L., Niu, R.Q., Trinder, C.J., Peng, L., and Lei, T. (2020). Mapping landslide susceptibility at the Three Gorges Reservoir, China, using gradient boosting decision tree, random forest and information value models. Journal of Mountain Science, 17(3): 670-685.
[120]. Singh, P., Sharma, A., Sur, U., and Rai, P.K. (2021). Comparative landslide susceptibility assessment using statistical information value and index of entropy model in Bhanupali-Beri region, Himachal Pradesh, India. Environment, Development and Sustainability, 23(4): 5233-5250.
[121]. Farooq, S. and Akram, M.S. (2021). Landslide susceptibility mapping using information value method in Jhelum Valley of the Himalayas. Arabian Journal of Geosciences, 14(10): 1-16.
[122]. Wubalem, A. and Meten, M. (2020). Landslide susceptibility mapping using information value and logistic regression models in Goncha Siso Eneses area, northwestern Ethiopia. SN Applied Sciences, 2(5): 1-19.
[123]. Bonham-Carter, G.F. and Bonham-Carter, G. (1994). Geographic information systems for geoscientists: modelling with GIS (No. 13). Elsevier.
[124]. Cao, Y., Wei, X., Fan, W., Nan, Y., Xiong, W., and Zhang, S. (2021). Landslide susceptibility assessment using the Weight of Evidence method: A case study in Xunyang area, China. PLoS one, 16(1): e0245668.
[125]. Goyes-Peñafiel, P. and Hernandez-Rojas, A. (2021). Landslide susceptibility index based on the integration of logistic regression and weights of evidence: A case study in Popayan, Colombia. Engineering Geology, 280, 105958.
[126]. Saha, A. and Saha, S. (2020). Comparing the efficiency of weight of evidence, support vector machine and their ensemble approaches in landslide susceptibility modelling: A study on Kurseong region of Darjeeling Himalaya, India. Remote Sensing Applications: Society and Environment, 19, 100323.
[127]. Getachew, N. and Meten, M. (2021). Weights of evidence modeling for landslide susceptibility mapping of Kabi-Gebro locality, Gundomeskel area, Central Ethiopia. Geoenvironmental Disasters, 8(1): 1-22.
[128]. Nwazelibe, V.E., Unigwe, C.O., and Egbueri, J. C. (2022). Integration and comparison of algorithmic weight of evidence and logistic regression in landslide susceptibility mapping of the orumba north erosion-prone region, nigeria. Modeling Earth Systems and Environment, 1-20.
[129]. Batar, A.K. and Watanabe, T. (2021). Landslide susceptibility mapping and assessment using geospatial platforms and weights of evidence (WoE) method in the Indian Himalayan region: Recent developments, gaps, and future directions. ISPRS International Journal of Geo-Information, 10(3): 114.
[130]. Es-smairi, A., El Moutchou, B., and Touhami, A.E.O. (2021). Landslide susceptibility assessment using analytic hierarchy process and weight of evidence methods in parts of the Rif chain (northernmost Morocco). Arabian Journal of Geosciences, 14(14): 1-18.
[131]. Denis, K. and Liudmila, B. (2020). Landslide susceptibility analysis for the Kerch Peninsula using weights of evidence approach and GIS. Russian Journal of Earth Sciences, 20(1): 5.
[132]. Kumar, A., Sharma, R.K., and Bansal, V.K. (2022). Spatial Prediction of Landslide Hazard Using GIS-Multicriteria Decision Analysis in Kullu District of Himachal Pradesh, India. Journal of Mining and Environment.
[133]. Kumar, A., Sharma, R.K., and Bansal, V.K. (2018, November). GIS-Based landslide hazard mapping along NH-3 in mountainous terrain of Himachal Pradesh, India using weighted overlay analysis. In International Conference on Sustainable Waste Management through Design, pp. 59-67.
[134]. Kumar, A., Sharma, R.K., and Bansal, V.K. (2018). Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh, India. Environmental Earth Sciences, 77(20): 1-19.
[135]. Maqsoom, A., Aslam, B., Khalil, U., Kazmi, Z.A., Azam, S., Mehmood, T., andNawaz, A. (2021). Landslide susceptibility mapping along the China Pakistan Economic Corridor (CPEC) route using multi-criteria decision-making method. Modeling Earth Systems and Environment, 1-15.
[136]. Devara, M., Tiwari, A., and Dwivedi, R. (2021). Landslide susceptibility mapping using MT-InSAR and AHP enabled GIS-based multi-criteria decision analysis. Geomatics, Natural Hazards and Risk, 12(1): 675-693.
[137]. Salehpour Jam, A., Mosaffaie, J., Sarfaraz, F., Shadfar, S., and Akhtari, R. (2021). GIS-based landslide susceptibility mapping using hybrid MCDM models. Natural Hazards, 108(1): 1025-1046.
[138]. Roccati, A., Paliaga, G., Luino, F., Faccini, F., and Turconi, L. (2021). GIS-based landslide susceptibility mapping for land use planning and risk assessment. Land, 10(2): 162.
[139]. Chanu, M.L. and Bakimchandra, O. (2022). Landslide susceptibility assessment using AHP model and multi resolution DEMs along a highway in Manipur, India. Environmental Earth Sciences, 81(5): 1-11.
[140]. Hodasová, K. and Bednarik, M. (2021). Effect of using various weighting methods in a process of landslide susceptibility assessment. Natural Hazards, 105(1): 481-499.
[141]. Senouci, R., Taibi, N.E., Teodoro, A.C., Duarte, L., Mansour, H., and Yahia Meddah, R. (2021). GIS-based expert knowledge for landslide susceptibility mapping (LSM): case of mostaganem coast district, west of Algeria. Sustainability, 13(2): 630.
[142]. Bahrami, Y., Hassani, H., and Maghsoudi, A. (2021). Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal, 86(4): 1797-1816.
[143]. Saaty, T.L. (2000). The Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, Pennsylvia. University of Pittsburgh, Vol 1.
[144]. Saaty, T. (1980, November). The analytic hierarchy process (AHP) for decision making. In Kobe, Japan , pp. 1-69.
[145]. Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3): 234-281.
[146]. Lucchese, L.V., de Oliveira, G.G., and Pedrollo, O.C. (2021). Investigation of the influence of nonoccurrence sampling on landslide susceptibility assessment using Artificial Neural Networks. Catena, 198, 105067.
[147]. Mehrabi, M. and Moayedi, H. (2021). Landslide susceptibility mapping using artificial neural network tuned by metaheuristic algorithms. Environmental Earth Sciences, 80(24): 1-20.
[148]. Daviran, M., Shamekhi, M., Ghezelbash, R., and Maghsoudi, A. (2022). Landslide susceptibility prediction using artificial neural networks, SVMs and random forest: hyper-parameters tuning by genetic optimization algorithm. International Journal of Environmental Science and Technology, 1-18.
[149]. Tekin, S. and Çan, T. (2022). Slide type landslide susceptibility assessment of the Büyük Menderes watershed using artificial neural network method. Environmental Science and Pollution Research, 1-15.
[150]. Selamat, S.N., Majid, N.A., Taha, M.R., and Osman, A. (2022). Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia. Land, 11(6): 833.
[151]. Orhan, O., Bilgilioglu, S.S., Kaya, Z., Ozcan, A.K., and Bilgilioglu, H. (2022). Assessing and mapping landslide susceptibility using different machine learning methods. Geocarto International, 37(10): 2795-2820.
[152]. Ado, M., Amitab, K., Maji, A. K., Jasińska, E., Gono, R., Leonowicz, Z., and Jasiński, M. (2022). Landslide susceptibility mapping using machine learning: A literature survey. Remote Sensing, 14(13): 3029.
[153]. Youssef, A. M. and Pourghasemi, H. R. (2021). Landslide susceptibility mapping using machine learning algorithms and comparison of their performance at Abha Basin, Asir Region, Saudi Arabia. Geoscience Frontiers, 12(2): 639-655.
[154]. Al-Najjar, H. A., Pradhan, B., Kalantar, B., Sameen, M. I., Santosh, M., and Alamri, A. (2021). Landslide susceptibility modeling: An integrated novel method based on machine learning feature transformation. Remote Sensing, 13(16): 3281.
[155]. Liang, T.P., Turban, E., and Aronson, J.E. (2005). Decision support systems and intelligent systems. Yogyakarta: Penerbit Andi.
[156]. Phong, T.V., Phan, T.T., Prakash, I., Singh, S.K., Shirzadi, A., Chapi, K., and Pham, B.T. (2021). Landslide susceptibility modeling using different artificial intelligence methods: A case study at Muong Lay district, Vietnam. Geocarto International, 36(15): 1685-1708.
[157]. Alqadhi, S., Mallick, J., Talukdar, S., Bindajam, A.A., Van Hong, N., and Saha, T.K. (2022). Selecting optimal conditioning parameters for landslide susceptibility: An experimental research on Aqabat Al-Sulbat, Saudi Arabia. Environmental Science and Pollution Research, 29(3): 3743-3762.
[158]. Jones, S., Kasthurba, A.K., Bhagyanathan, A., and Binoy, B.V. (2021). Landslide susceptibility investigation for Idukki district of Kerala using regression analysis and machine learning. Arabian Journal of Geosciences, 14(10): 1-17.
[159]. Akinci, H., and Zeybek, M. (2021). Comparing classical statistic and machine learning models in landslide susceptibility mapping in Ardanuc (Artvin): Turkey. Natural Hazards, 108(2): 1515-1543.
[160]. Abraham, M. T., Satyam, N., Lokesh, R., Pradhan, B., and Alamri, A. (2021). Factors affecting landslide susceptibility mapping: Assessing the influence of different machine learning approaches, sampling strategies and data splitting. Land, 10(9): 989.
[161]. Liu, R., Li, L., Pirasteh, S., Lai, Z., Yang, X., and Shahabi, H. (2021). The performance quality of LR, SVM, and RF for earthquake-induced landslides susceptibility mapping incorporating remote sensing imagery. Arabian Journal of Geosciences, 14(4): 1-15.
[162]. Chowdhuri, I., Pal, S.C., Chakrabortty, R., Malik, S., Das, B., and Roy, P. (2021). Torrential rainfall-induced landslide susceptibility assessment using machine learning and statistical methods of eastern Himalaya. Natural Hazards, 107(1): 697-722.
[163]. Kincal, C. and Kayhan, H. (2022). A combined method for preparation of landslide susceptibility map in Izmir (Türkiye). Applied Sciences, 12(18): 9029.
[164]. Zhao, S. and Zhao, Z. (2021). A comparative study of landslide susceptibility mapping using SVM and PSO-SVM models based on Grid and Slope Units. Mathematical problems in Engineering, 2021.
[165]. Huang, F., Yan, J., Fan, X., Yao, C., Huang, J., Chen, W., and Hong, H. (2022). Uncertainty pattern in landslide susceptibility prediction modelling: Effects of different landslide boundaries and spatial shape expressions. Geoscience Frontiers, 13(2): 101317.
[166]. Fang, Z., Wang, Y., Peng, L., and Hong, H. (2021). A comparative study of heterogeneous ensemble-learning techniques for landslide susceptibility mapping. International Journal of Geographical Information Science, 35(2): 321-347.
[167]. Zhang, T., Fu, Q., Wang, H., Liu, F., Wang, H., and Han, L. (2022). Bagging-based machine learning algorithms for landslide susceptibility modeling. Natural hazards, 110(2): 823-846
[168]. Al-Najjar, H. A. and Pradhan, B. (2021). Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks. Geoscience Frontiers, 12(2): 625-637.
[169]. Xie, W., Li, X., Jian, W., Yang, Y., Liu, H., Robledo, L.F., and Nie, W. (2021). A novel hybrid method for landslide susceptibility mapping-based geodetector and machine learning cluster: A case of Xiaojin county, China. ISPRS International Journal of Geo-Information, 10(2): 93.
[170]. Pham, B.T., Tien Bui, D., and Prakash, I. (2018). Bagging based support vector machines for spatial prediction of landslides. Environmental Earth Sciences, 77(4): 1-17.
[171]. Vapnik, V., 2013. The nature of statistical learning theory: Springer science & business media.
[172]. Pourghasemi, H.R., Jirandeh, A.G., Pradhan, B., Xu, C. and Gokceoglu, C. (2013). Landslide susceptibility mapping using support vector machine and gis at the golestan province, iran. Journal of Earth System Science, 122 (2): 349-369.
[173]. Micheletti, N., Foresti, L., Kanevski, M., Pedrazzini, A. and Jaboyedoff, M. (2011). Landslide susceptibility mapping using adaptive support vector machines and feature selection. Geophysical Research Abstracts, EGU, 13.
[174]. Kumar, A., Sharma, R.K., and Mehta, B.S. (2020). Slope stability analysis and mitigation measures for selected landslide sites along NH-205 in Himachal Pradesh, India. Journal of Earth System Science, 129(1): 1-14.
[175]. Sharma, R.K., Kaur, A., and Kumar, A. (2018). Slope stability analysis by bishop analysis using MATLAB program based on particle swarm optimization technique. In International Conference on Sustainable Waste Management through Design, pp. 285-293.
)