Abstract
The northern region of Pakistan is a top tourist destination that is highly susceptible to landslides. Current mega infrastructure development projects in the region have further boosted the importance of the area at national and international scales, yet detailed studies of landslide susceptibility in upper Indus River Basin (UIRB) is still lacking. The aim of this study is to generate and compare landslide susceptibility maps from machine learning algorithms (MLAs) and a traditional geographic information system (GIS) based approach. Past landslide locations are used for model training and testing with data of eleven controlling factors in MLAs, including random forest (RF), support vector machine (SVM), and Naïve Bayes (NB) classifiers. Among the three MLAs, average accuracy of RF model is found between 89 and 90.5%, for SVM the range is between 88 and 90% and for NB the range is between 86 and 87%. The results show that the traditional GIS based weighted overlay technique overestimated vulnerable areas with most of the study area falling in moderate to high susceptible zones. The machine learning models performed much better than the traditional technique as only areas that were identified as most susceptible were locations where landslides had occurred in the past. Within the three ML techniques, RF model’s performance is marginally better than that of the SVM model, but RF and SVM performed significantly better compared to the NB model. The resultant susceptibility maps highlight the areas where safety measures should be taken before installing mega infrastructure projects.
Similar content being viewed by others
Data availability
All the datasets are available freely online, except geological datasets which are available from the corresponding author on request.
Code availability
All the codes are available from the corresponding author on reasonable request.
References
Aghdam IN, Varzandeh MH, Pradhan B (2016) Landslide susceptibility mapping using an ensemble statistical index (Wi) and adaptive neuro-fuzzy inference system (ANFIS) model at Alborz Mountains (Iran). Environ Earth Sci 75(7):1–20. https://doi.org/10.1007/s12665-015-5233-6
Ahmed MF, Rogers JD (2014) First-approximation landslide inventory maps for northern Pakistan, using ASTER DEM data and geomorphic indicators. Environ Eng Geosci 20(1):67–83. https://doi.org/10.2113/gseegeosci.20.1.67
Aleotti P, Chowdhury R (1999) Landslide hazard assessment: summary review and new perspectives. Bull Eng Geol Environ 58(1):21–44. https://doi.org/10.1007/s100640050066
Alexakis DD, Agapiou A, Tzouvaras M, Themistocleous K, Neocleous K, Michaelides S, Hadjimitsis DG (2014) Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus. Nat Hazards 72(1):119–141. https://doi.org/10.1007/s11069-013-0770-3
Ali S, Biermanns P, Haider R, Reicherter K (2018) Landslide susceptibility mapping by using GIS along the China-Pakistan economic corridor (Karakoram Highway), Pakistan. Nat Hazards Earth Syst Sci. https://doi.org/10.5194/nhess-19-999-2019
Bai SB, Wang J, Lü GN, Zhou PG, Hou SS, Xu SN (2010) GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China. Geomorphology 115:23–31. https://doi.org/10.1016/j.geomorph.2009.09.025
Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River Basin Case Study, Italy. Math Geosci 44(1):47–70. https://doi.org/10.1007/s11004-011-9379-9
Basharat M, Shah HR, Hameed N (2016) Landslide susceptibility mapping using GIS and weighted overlay method: a case study from NW Himalayas, Pakistan. Arab J Geosci 9(4):292. https://doi.org/10.1007/s12517-016-2308-y
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Butt MJ, Umar M, Qamar R (2013) Landslide dam and subsequent dam-break flood estimation using HEC-RAS model in Northern Pakistan. Nat Hazards 65(1):241–254. https://doi.org/10.1007/s11069-012-0361-8
Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazard 13(11):2815–2831. https://doi.org/10.5194/nhess-13-2815-2013
Chau KT, Sze YL, Fung MK, Wong WY, Fong EL, Chan LCP (2004) Landslide hazard analysis for Hong Kong using landslide inventory and GIS. Comput Geosci 30(4):429–443. https://doi.org/10.5194/nhessd-1-583-2013
Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H (2017) 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. https://doi.org/10.1080/19475705.2017.1289250
Chen W, Zhang S, Li R, Shahabi H (2018) Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. Sci Total Environ 644:1006–1018. https://doi.org/10.1016/j.scitotenv.2018.06.389
Ciampalini A, Raspini F, Bianchini S, Frodella W, Bardi F, Lagomarsino D, Di Traglia F, Moretti S, Proietti C, Pagliara P, Onori R (2015) Remote sensing as tool for development of landslide databases: the case of the Messina Province (Italy) geodatabase. Geomorphology 249:103–118. https://doi.org/10.1016/j.geomorph.2015.01.029
Cruden DM (1991) A simple definition of a landslide. Bull Int Assoc Eng Geol 43:27–29. https://doi.org/10.1007/BF02590167
DAAC O (2017) Spatial data access tool (SDAT). ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1388
Di Martire D, Tessitore S, Brancato D, Ciminelli MG, Costabile S, Costantini M, Graziano GV, Minati F, Ramondini M, Calcaterra D (2016) Landslide detection integrated system (LaDIS) based on in-situ and satellite SAR interferometry measurements. CATENA 137:406–421. https://doi.org/10.1016/j.catena.2015.10.002
Diffenbaugh NS, Field CB (2013) Changes in ecologically critical terrestrial climate conditions. Science 341(6145):486–492. https://doi.org/10.1126/science.1237123
Falaschi F, Giacomelli F, Federici PR, Puccinelli A, Avanzi GA, Pochini A, Ribolini A (2009) Logistic regression versus artificial neural networks: landslide susceptibility evaluation in a sample area of the Serchio River valley, Italy. Nat Hazards 50(3):551–569. https://doi.org/10.1007/s11069-009-9356-5
Farooq AM, David RJ (2016) Regional level landslide inventory maps of the Shyok River watershed, Northern Pakistan. Bull Eng Geol Environ 75(2):563–574. https://doi.org/10.1007/s10064-015-0773-2
Gariano SL, Guzzetti F (2016) Landslides in a changing climate. Earth Sci Rev 162:227–252. https://doi.org/10.1016/j.earscirev.2016.08.011
Goetz JN, Brenning A, Petschko H, Leopold P (2015) Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling. Comput Geosci 81:1–11. https://doi.org/10.1016/j.cageo.2015.04.007
Guo Q, Kelly M, Graham CH (2005) Support vector machines for predicting distribution of Sudden Oak Death in California. Ecol Model 182(1):75–90. https://doi.org/10.1016/j.ecolmodel.2004.07.012
He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y, Wang X (2019) Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ 663:1–15. https://doi.org/10.1016/j.scitotenv.2019.01.329
Hewitt K (1998) Catastrophic landslides and their effects on the Upper Indus streams, Karakoram Himalaya, northern Pakistan. Geomorphology 26(1–3):47–80. https://doi.org/10.1016/S0169-555X(98)00051-8
Hong H, Pourghasemi HR, Pourtaghi ZS (2016a) Landslide susceptibility assessment in Lianhua County (China): a comparison between a random forest data mining technique and bivariate and multivariate statistical models. Geomorphology 259:105–118. https://doi.org/10.1016/j.geomorph.2016.02.012
Hong H, Pradhan B, Jebur MN, Bui DT, Xu C, Akgun A (2016b) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci 75(1):1–14. https://doi.org/10.1007/s12665-015-4866-9
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2006) A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng Geol 85(3–4):347–366. https://doi.org/10.1016/j.enggeo.2006.03.004
Kanungo DP, Arora MK, Gupta RP, Sarkar S (2008) Landslide risk assessment using concepts of danger pixels and fuzzy set theory in Darjeeling Himalayas. Landslides 5(4):407–416. https://doi.org/10.1007/s10346-008-0134-3
Kanungo DP, Arora MK, Sarkar S, Gupta RP (2009) Landslide susceptibility zonation (LSZ) mapping–a Review. J South Asia Disaster Stud 2(1):81–105
Kanwal S, Atif S, Shafiq M (2017) GIS based landslide susceptibility mapping of northern areas of Pakistan, a case study of Shigar and Shyok Basins. Geomat Nat Hazards Risk 8(2):348–366. https://doi.org/10.1080/19475705.2016.1220023
Kavzoglu T, Sahin EK, Colkesen I (2014) Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression. Landslides 11(3):425–439. https://doi.org/10.1007/s10346-013-0391-7
Kohavi R (1996) Scaling up the accuracy of naive-bayes classifiers: a decision-tree hybrid. In: Proceedings of the second international conference on knowledge discovery and data mining, pp 114–119
Latif Y, Yaoming M, Yaseen M (2018) Spatial analysis of precipitation time series over the Upper Indus Basin. Theor Appl Climatol 131(1–2):761–775. https://doi.org/10.1007/s00704-016-2007-3
Lee S, Hong SM, Jung HS (2017) A support vector machine for landslide susceptibility mapping in Gangwon Province, Korea. Sustainability 9(1):15–19. https://doi.org/10.3390/su9010048
Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22
Malczewski J (2000) On the use of weighted linear combination method in GIS: Common and best practice approaches. Trans GIS 4(1):5–22. https://doi.org/10.1111/1467-9671.00035
Menggenang P, Samanta S (2017) Modelling and mapping of landslide hazard using remote sensing and GIS techniques. Modeling Earth Syst Environ 3(3):1–10. https://doi.org/10.1007/s40808-017-0361-5
Michael EA, Samanta S (2016) Landslide vulnerability mapping (LVM) using weighted linear combination (WLC) model through remote sensing and GIS techniques. Modeling Earth Syst Environ 2(2):88. https://doi.org/10.1007/s40808-016-0141-7
Naithani AK (1999) The Himalayan landslides. Employ News 23(47):20–26
NASA/METI/AIST/Japan Space Systems (2009) ASTER global digital elevation model [data set]. NASA EOSDIS Land Processes DAAC. https://doi.org/10.5067/ASTER/ASTGTM.002
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(9):1264–1276. https://doi.org/10.1016/j.cageo.2010.10.012
Panagos P, Van Liedekerke M, Jones A, Montanarella L (2012) European Soil Data Centre: response to European policy support and public data requirements. Land Use Policy 29(2):329–338. https://doi.org/10.1016/j.landusepol.2011.07.003
Petley D (2012) Global patterns of loss of life from landslides. Geology 40(10):927–930. https://doi.org/10.1130/G33217.1
Pham BT, Bui D, Prakash I, Dholakia M (2016) Evaluation of predictive ability of support vector machines and naive Bayes trees methods for spatial prediction of landslides in Uttarakhand state (India) using GIS. J Geomat 10(1):71–79
Pourghasemi HR, Jirandeh AG, Pradhan B, Xu C, Gokceoglu C (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan province, Iran. J Earth Syst Sci 122(2):349–369. https://doi.org/10.1007/s12040-013-0282-2
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(2):725–742. https://doi.org/10.1007/s12517-012-0807-z
Sadiq S, Muhammad U, Fuchs M (2021) Investigation of landslides with natural lineaments derived from integrated manual and automatic techniques applied on geospatial data. Nat Hazards. https://doi.org/10.1007/s11069-021-05028-6
Shahabi H, Hashim M (2015) Landslide susceptibility mapping using GIS-based statistical models and remote sensing data in tropical environment. Sci Rep 5:1–15. https://doi.org/10.1038/srep09899
Shirzadi A, Saro L, Joo OH, Chapi K (2012) A GIS-based logistic regression model in rock-fall susceptibility mapping along a mountainous road: Salavat Abad case study, Kurdistan, Iran. Nat Hazards 64(2):1639–1656. https://doi.org/10.1007/s11069-012-0321-3
Taalab K, Cheng T, Zhang Y (2018) Mapping landslide susceptibility and types using Random Forest. Big Earth Data 2(2):159–178. https://doi.org/10.1080/20964471.2018.1472392
Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Nave Bayes models. Math Probl Eng 2012:1–26. https://doi.org/10.1155/2012/974638
Tien Bui D, Ho TC, Pradhan B, Pham BT, Nhu VH, Revhaug I (2016) GIS-based modeling of rainfall-induced landslides using data mining-based functional trees classifier with AdaBoost, Bagging, and MultiBoost ensemble frameworks. Environ Earth Sci 75(14):1–22. https://doi.org/10.1007/s12665-016-5919-4
Vapnik V (1995) Nature of statistical learning theory. John Wiley and Sons Inc, New York
Varnes DJ (1981) The principles and practice of landslide hazard zonation. Bull Int Assoc Eng Geol 23(1):13–14. https://doi.org/10.1007/BF02594720
Varnes DJ (1984) Landslide hazard zonation: a review of principles and practice. UNESCO Press, Paris, p 63
Wang P, Bai X, Wu X, Yu H, Hao Y, Hu BX (2018) GIS-based random forestweight for rainfall-induced landslide susceptibility assessment at a humid region in Southern China. Water 10(8):1019. https://doi.org/10.3390/w10081019
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.2011.01.014
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582. https://doi.org/10.1016/j.geomorph.2008.02.011
Yatagai A, Kamiguchi K, Arakawa O, Hamada A, Yasutomi N, Kitoh A (2012) APHRODITE: constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull Am Meteorol Soc 93(9):1401–1415. https://doi.org/10.1175/BAMS-D-11-00122.1
Young GJ, Hewitt K (1990) Hydrology research in the upper Indus basin, Karakoram Himalaya, Pakistan. Hydrol Mt Areas 90:139–152
Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2016a) Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia. Landslides 13(5):839–856. https://doi.org/10.1007/s10346-015-0614-1
Youssef AM, Pourghasemi HR, El-Haddad BA, Dhahry BK (2016b) Landslide susceptibility maps using different probabilistic and bivariate statistical models and comparison of their performance at Wadi Itwad Basin, Asir Region, Saudi Arabia. Bull Eng Geol Environ 75(1):63–87. https://doi.org/10.1007/s11069-016-2239-7
Zare M, Pourghasemi HR, Vafakhah M, Pradhan B (2013) Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6(8):2873–2888. https://doi.org/10.1007/s12517-012-0610-x
Zêzere JL, Pereira S, Melo R, Oliveira SC, Garcia RA (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267. https://doi.org/10.1016/j.scitotenv.2017.02.188
Zhou C, Yin K, Cao Y, Ahmed B, Li Y, Catani F, Pourghasemi HR (2018) Landslide susceptibility modeling applying machine learning methods: a case study from Longju in the Three Gorges Reservoir area, China. Comput Geosci 112:23–37. https://doi.org/10.1016/j.cageo.2017.11.019
Acknowledgements
Authors acknowledge the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) of water resources, Shell Pakistan, United States Geological Survey (USGS), and Land Process Distributed Active Archive Center (LP DAAC) for providing access to their datasets.
Funding
No funding was received for conducting this study.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this research work.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Imtiaz, I., Umar, M., Latif, M. et al. Landslide susceptibility mapping: improvements in variable weights estimation through machine learning algorithms—a case study of upper Indus River Basin, Pakistan. Environ Earth Sci 81, 112 (2022). https://doi.org/10.1007/s12665-022-10233-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-022-10233-y