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Research Article

An integrated approach based landslide susceptibility mapping: case of Muzaffarabad region, Pakistan

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Article: 2210255 | Received 22 Feb 2023, Accepted 28 Apr 2023, Published online: 09 May 2023

References

  • Abdo HG. 2022. Assessment of landslide susceptibility zonation using frequency ratio and statistical index: a case study of Al-Fawar basin, Tartous, Syria. Int J Environ Sci Technol. 19(4):2599–2618.
  • Abdo HG, Almohamad H, Al Dughairi AA, Ali SA, Parvin F, Elbeltagi A, Costache R, Mohammed S, Al-Mutiry M, Alsafadi K. 2022. Spatial implementation of frequency ratio, statistical index and index of entropy models for landslide susceptibility mapping in Al-Balouta river basin, Tartous Governorate, Syria. Geosci Lett. 9(1):1–24.
  • Achour Y, Pourghasemi HR. 2020. How do machine learning techniques help in increasing accuracy of landslide susceptibility maps? Geosci Front. 11(3):871–883.
  • Ahmad MN, Shao Z, Aslam RW, Ahmad I, Liao M, Li X, Song Y. 2022. Landslide hazard, susceptibility and risk assessment (HSRA) based on remote sensing and GIS data models: a case study of Muzaffarabad Pakistan. Stochastic Environ Res Risk Assess. 36:4041–4056.
  • Ali S, Biermanns P, Haider R, Reicherter K. 2019. Landslide susceptibility mapping by using a geographic information system (GIS) along the China–Pakistan Economic Corridor (Karakoram Highway, Pakistan. Nat Hazards Earth Syst Sci. 19(5):999–1022.
  • Arnoldus H. 1980. An approximation of the rainfall factor in the Universal Soil Loss Equation. In: De Boodt M, Gabriels D, editors. Assessment of Erosion. New York: John Wiley and Sons; 127–132.
  • Aslam B, Maqsoom A, Khalil U, Ghorbanzadeh O, Blaschke T, Farooq D, Tufail RF, Suhail SA, Ghamisi P. 2022c. Evaluation of different landslide susceptibility models for a local scale in the Chitral District, Northern Pakistan. Sensors. 22(9):3107.
  • Aslam B, Zafar A, Khalil U. 2021. Development of integrated deep learning and machine learning algorithm for the assessment of landslide hazard potential. Soft Comput. 25(21):13493–13512.
  • Aslam B, Zafar A, Khalil U. 2022a. Comparison of multiple conventional and unconventional machine learning models for landslide susceptibility mapping of Northern part of Pakistan. Environ Dev Sustain. 1–28.
  • Aslam B, Zafar A, Khalil U. 2022b. Comparative analysis of multiple conventional neural networks for landslide susceptibility mapping. Nat Hazards. 115(1):673–707.
  • Avouac J-P, Ayoub F, Leprince S, Konca O, Helmberger DV. 2006. The 2005, Mw 7.6 Kashmir earthquake: sub-pixel correlation of ASTER images and seismic waveforms analysis. Earth Planet Sci Lett. 249(3–4):514–528.
  • Bai Z, Liu Q, Liu Y. 2021. Landslide susceptibility mapping using GIS-based machine learning algorithms for the Northeast Chongqing Area, China. Arab J Geosci. 14(24):1–16.
  • Baig MS. 2006. Active faulting and earthquake deformation in Hazara-Kashmir syntaxis, Azad Kashmir, northwest Himalaya, Pakistan. In: Extended Abstracts, International Conference on 8 October 2005 Earthquake in Pakistan: Its Implications and Hazard Mitigation, Islamabad, Pakistan, 18–19 January 2006, Citeseer, p. 27–28.
  • 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.
  • Batool M, Ahmad SR, Asif M. 2021. An assessment of landslide hazards in Muzaffarabad-Azad Jammu & Kashmir using geospatial techniques. Pak Geogr Rev. 76:164–173.
  • Bradley AP. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7):1145–1159.
  • Bragagnolo L, da Silva RV, Grzybowski JMV. 2020. Landslide susceptibility mapping with r. landslide: a free open-source GIS-integrated tool based on Artificial Neural Networks. Environ Modell Softw. 123:104565.
  • Bui DT, Tuan TA, Klempe H, Pradhan B, Revhaug I. 2016. Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree. Landslides. 13(2):361–378.
  • Capitani M, Ribolini A, Bini M. 2013. The slope aspect: a predisposing factor for landsliding? CR Geosci. 345(11–12):427–438.
  • Chen W, Chen X, Peng J, Panahi M, Lee S. 2021b. Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer. Geosci Front. 12(1):93–107.
  • Chen L, Guo H, Gong P, Yang Y, Zuo Z, Gu M. 2021a. Landslide susceptibility assessment using weights-of-evidence model and cluster analysis along the highways in the Hubei section of the Three Gorges Reservoir Area. Comput Geosci. 156:104899.
  • 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.
  • Chen W, Xie X, Peng J, Wang J, Duan Z, Hong H. 2017b. GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomatics Nat Hazards Risk. 8(2):950–973.
  • Chen W, Xie X, Wang J, Pradhan B, Hong H, Bui DT, Duan Z, Ma J. 2017c. 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.
  • 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.
  • Choi J, Oh H-J, Lee H-J, Lee C, Lee S. 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Eng Geol. 124:12–23.
  • Cortes C, Vapnik V. 1995. Support-vector networks. Mach Learn. 20(3):273–297.
  • Dou J, Yamagishi H, Pourghasemi HR, Yunus AP, Song X, Xu Y, Zhu Z. 2015. An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards. 78(3):1749–1776.
  • Dragićević S, Lai T, Balram S. 2015. GIS-based multicriteria evaluation with multiscale analysis to characterize urban landslide susceptibility in data-scarce environments. Habitat Int. 45:114–125.
  • Du Y, Zhang Y, Ling F, Wang Q, Li W, Li X. 2016. Water bodies’ mapping from Sentinel-2 imagery with modified normalized difference water index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sensing. 8(4):354.
  • Đurić D, Mladenović A, Pešić-Georgiadis M, Marjanović M, Abolmasov B. 2017. Using multiresolution and multitemporal satellite data for post-disaster landslide inventory in the Republic of Serbia. Landslides. 14(4):1467–1482.
  • Eberhart R, Kennedy J. 1995. A new optimizer using particle swarm theory. In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, p. 39–43.
  • Eshtay M, Faris H, Obeid N. 2018. Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems. Expert Syst Appl. 104:134–152.
  • Flentje P, Chowdhury R. 2018. Resilience and sustainability in the management of landslides. In Proceedings of the Institution of Civil Engineers-Engineering Sustainability, vol ES1.
  • Froude MJ, Petley DN. 2018. Global fatal landslide occurrence from 2004 to 2016. Nat Hazards Earth Syst Sci. 18(8):2161–2181.
  • Gautam P, Kubota T, Sapkota LM, Shinohara Y. 2021. Landslide susceptibility mapping with GIS in high mountain area of Nepal: a comparison of four methods. Environ Earth Sci. 80(9):1–18.
  • Ghorbanzadeh O, Shahabi H, Crivellari A, Homayouni S, Blaschke T, Ghamisi P. 2022. Landslide detection using deep learning and object-based image analysis. Landslides. 19(4):929–939.
  • Gleason CJ, Im J. 2012. Forest biomass estimation from airborne LiDAR data using machine learning approaches. Remote Sens Environ. 125:80–91.
  • Günther F, Fritsch S. 2010. Neuralnet: training of neural networks. R J. 2(1):30.
  • He Q, Xu Z, Li S, Li R, Zhang S, Wang N, Pham BT, Chen W. 2019. Novel entropy and rotation forest-based credal decision tree classifier for landslide susceptibility modeling. Entropy. 21(2):106.
  • Hong H, Liu J, Bui DT, Pradhan B, Acharya TD, Pham BT, Zhu A-X, Chen W, Ahmad BB. 2018. Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). Catena. 163:399–413.
  • Hong H, Naghibi SA, Pourghasemi HR, Pradhan B. 2016. GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci. 9(2):112.
  • Huang F, Yin K, Huang J, Gui L, Wang P. 2017. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Eng Geol. 223:11–22.
  • Hussain MA, Chen Z, Wang R, Shah SU, Shoaib M, Ali N, Xu D, Ma C. 2022. Landslide susceptibility mapping using machine learning algorithm. Civ Eng J. 8(2):209–224.
  • Hussain A, Yeats RS, MonaLisa. 2009. Geological setting of the 8 October 2005 Kashmir earthquake. J Seismol. 13(3):315–325.,
  • Islam ARMT, Talukdar S, Mahato S, Kundu S, Eibek KU, Pham QB, Kuriqi A, Linh NTT. 2021. Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front. 12(3):101075.
  • Jaafari A, Janizadeh S, Abdo HG, Mafi-Gholami D, Adeli B. 2022. Understanding land degradation induced by gully erosion from the perspective of different geoenvironmental factors. J Environ Manage. 315:115181.
  • Jakob M. 2022. Landslides in a changing climate. In Landslide hazards, risks, and disasters. Elsevier, p. 505–579.
  • Kamp U, Growley BJ, Khattak GA, Owen LA. 2008. GIS-based landslide susceptibility mapping for the 2005 Kashmir earthquake region. Geomorphology. 101(4):631–642.
  • 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. Geomatics Nat Hazards Risk. 8(2):348–366.
  • 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.
  • Kazmi AH, Jan MQ. 1997. Geology and tectonics of Pakistan. Karachi: Graphic Publishers,
  • Khan AN, Collins AE, Qazi, F, Atta-Ur-Rahman. 2011. Causes and extent of environmental impacts of landslide hazard in the Himalayan region: a case study of Murree, Pakistan. Nat Hazards. 57(2):413–434.,
  • Khan H, Shafique M, Khan MA, Bacha MA, Shah SU, Calligaris C. 2019. Landslide susceptibility assessment using Frequency Ratio, a case study of northern Pakistan. Egypt J Remote Sens Space Sci. 22(1):11–24.
  • Khattak GA, Owen LA, Kamp U, Harp EL. 2010. Evolution of earthquake-triggered landslides in the Kashmir Himalaya, northern Pakistan. Geomorphology. 115(1–2):102–108.
  • Li W, Fang Z, Wang Y. 2022. Stacking ensemble of deep learning methods for landslide susceptibility mapping in the Three Gorges Reservoir area, China. Stoch Environ Res Risk Assess. 36(8):2207–2228.
  • Li BV, Jenkins CN, Xu W. 2022. Strategic protection of landslide vulnerable mountains for biodiversity conservation under land-cover and climate change impacts. Proc Natl Acad Sci USA. 119(2):e2113416118.
  • Liu R, Li L, Pirasteh S, Lai Z, Yang X, Shahabi H. 2021. The performance quality of LR, SVM, and RF for earthquake-induced landslides susceptibility mapping incorporating remote sensing imagery. Arab J Geosci. 14(4):1–15.
  • Mallick J, Alqadhi S, Talukdar S, AlSubih M, Ahmed M, Khan RA, Kahla NB, Abutayeh SM. 2021. Risk assessment of resources exposed to rainfall induced landslide with the development of GIS and RS based ensemble metaheuristic machine learning algorithms. Sustainability. 13(2):457.
  • Mandal B, Mandal S. 2018. Analytical hierarchy process (AHP) based landslide susceptibility mapping of Lish river basin of eastern Darjeeling Himalaya, India. Adv Space Res. 62(11):3114–3132.
  • Maqsoom A, Aslam B, Hassan U, Kazmi ZA, Sodangi M, Tufail RF, Farooq D. 2020. Geospatial assessment of soil erosion intensity and sediment yield using the revised universal soil loss equation (RUSLE) model. IJGI. 9(6):356.
  • Maqsoom A, Aslam B, Khalil U, Kazmi ZA, Azam S, Mehmood T, Nawaz A. 2022. Landslide susceptibility mapping along the China Pakistan Economic Corridor (CPEC) route using multi-criteria decision-making method. Model Earth Syst Environ. 8(2):1519–1533.
  • Maqsoom A, Aslam B, Yousafzai A, Ullah F, Ullah S, Imran M. 2022. Extracting built-up areas from spectro-textural information using machine learning. Soft Comput. 26(16):7789–7808.
  • Mondini AC, Guzzetti F, Chang K-T, Monserrat O, Martha TR, Manconi A. 2021. Landslide failures detection and mapping using Synthetic Aperture Radar: past, present and future. Earth Sci Rev. 216:103574.
  • Mountrakis G, Im J, Ogole C. 2011. Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens. 66(3):247–259.
  • Naceur HA, Abdo HG, Igmoullan B, Namous M, Almohamad H, Al Dughairi AA, Al-Mutiry M. 2022. Performance assessment of the landslide susceptibility modelling using the support vector machine, radial basis function network, and weight of evidence models in the N'fis river basin, Morocco. Geosci Lett. 9(1):1–20.
  • O’brien RM. 2007. A caution regarding rules of thumb for variance inflation factors. Qual Quant. 41(5):673–690.
  • Owen LA, Kamp U, Khattak GA, Harp EL, Keefer DK, Bauer MA. 2008. Landslides triggered by the 8 October 2005 Kashmir earthquake. Geomorphology. 94(1–2):1–9.
  • Pandey VK, Pourghasemi HR, Sharma MC. 2020. Landslide susceptibility mapping using maximum entropy and support vector machine models along the Highway Corridor, Garhwal Himalaya. Geocarto Int. 35(2):168–187.
  • Park S, Hamm S-Y, Kim J. 2019. Performance evaluation of the GIS-based data-mining techniques decision tree, random forest, and rotation forest for landslide susceptibility modeling. Sustainability. 11(20):5659.
  • Pham BT, Bui DT, Prakash I, Dholakia M. 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.
  • Pham BT, Pradhan B, Bui DT, Prakash I, Dholakia M. 2016. A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Modell Softw. 84:240–250.
  • Pham BT, Vu VD, Costache R, Phong TV, Ngo TQ, Tran T-H, Nguyen HD, Amiri M, Tan MT, Trinh PT, et al. 2022. Landslide susceptibility mapping using state-of-the-art machine learning ensembles. Geocarto Int. 37(18):5175–5200.
  • Piralilou ST, Shahabi H, Pazur R. 2021. Automatic landslide detection using bi-temporal sentinel 2 imagery. Gi_Forum. 1:39–45.
  • Pourghasemi HR, Pradhan B, Gokceoglu C. 2012. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat Hazards. 63(2):965–996.
  • Pradhan B. 2010. Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. J Indian Soc Remote Sens. 38(2):301–320.
  • Pradhan AMS, Kim Y-T. 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.
  • Pradhan B, Lee S, Buchroithner MF. 2009. Use of geospatial data and fuzzy algebraic operators to landslide-hazard mapping. Appl Geomat. 1(1–2):3–15.
  • Qi T, Zhao Y, Meng X, Shi W, Qing F, Chen G, Zhang Y, Yue D, Guo F. 2021. Distribution modeling and factor correlation analysis of landslides in the large fault zone of the western Qinling Mountains: a machine learning algorithm. Remote Sens. 13(24):4990.
  • Rahim I, Ali SM, Aslam M. 2018. GIS Based landslide susceptibility mapping with application of analytical hierarchy process in District Ghizer, Gilgit Baltistan Pakistan. GEP. 06(02):34–49.
  • Rahman G, Bacha AS, Ul Moazzam MF, Rahman AU, Mahmood S, Almohamad H, Al Dughairi AA, Al-Mutiry M, Alrasheedi M, Abdo HG. 2022. Assessment of landslide susceptibility, exposure, vulnerability, and risk in Shahpur valley, eastern Hindu Kush. Front Earth Sci. 10:953627.
  • Rai DC, Murty C. 2006. Effects of the 2005 Muzaffarabad (Kashmir) earthquake on built environment. Curr Sci. 90(8):1066–1070.
  • Riaz MT, Basharat M, Hameed N, Shafique M, Luo J. 2018. A data-driven approach to landslide-susceptibility mapping in mountainous terrain: case study from the Northwest Himalayas, Pakistan. Nat Hazards Rev. 19(4):05018007.
  • Rossetto T, Peiris N. 2009. Observations of damage due to the Kashmir earthquake of October 8, 2005 and study of current seismic provisions for buildings in Pakistan. Bull Earthq Eng. 7(3):681–699.
  • Saba SB, van der Meijde M, van der Werff H. 2010. Spatiotemporal landslide detection for the 2005 Kashmir earthquake region. Geomorphology. 124(1–2):17–25.
  • Saha AK, Gupta RP, Sarkar I, Arora MK, Csaplovics E. 2005. An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas. Landslides. 2(1):61–69.
  • Saha S, Sarkar R, Roy J, Hembram TK, Acharya S, Thapa G, Drukpa D. 2021. Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms. Sci Rep. 11(1):1–23.
  • Seyedashraf O, Mehrabi M, Akhtari AA. 2018. Novel approach for dam break flow modeling using computational intelligence. J Hydrol. 559:1028–1038.
  • Shirzadi A, Solaimani K, Roshan MH, Kavian A, Chapi K, Shahabi H, Keesstra S, Ahmad BB, Bui DT. 2019. Uncertainties of prediction accuracy in shallow landslide modeling: sample size and raster resolution. Catena. 178:172–188.
  • Shu H, Guo Z, Qi S, Song D, Pourghasemi HR, Ma J. 2021. Integrating landslide typology with weighted frequency ratio model for landslide susceptibility mapping: a case study from Lanzhou city of northwestern China. Remote Sens. 13(18):3623.
  • Talukdar S, Ghose B, Salam R, Mahato S, Pham QB, Linh NTT, Costache R, Avand M, Shahfahad. 2020. Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms. Stoch Environ Res Risk Assess. 34(12):2277–2300.,
  • Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Alizadeh M, Chen W, Mohammadi A, Ahmad B, Panahi M, Hong H, et al. 2018. Landslide detection and susceptibility mapping by airsar data using support vector machine and index of entropy models in cameron highlands, malaysia. Remote Sensing. 10(10):1527.
  • Tsangaratos P, Ilia I. 2016. Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: the influence of models complexity and training dataset size. Catena. 145:164–179.
  • Umar Z, Pradhan B, Ahmad A, Jebur MN, Tehrany MS. 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.
  • Van Den Eeckhaut M, Vanwalleghem T, Poesen J, Govers G, Verstraeten G, 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.
  • Vapnik V. 1999. The nature of statistical learning theory. New York: Springer Science & Business Media.
  • Wang S-C. 2003. Artificial neural network. In Interdisciplinary computing in java programming. New York: Springer, p. 81–100.
  • Wang Y, Fang Z, Hong H. 2019. Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China. Sci Total Environ. 666:975–993.
  • Xi W, Li G, Moayedi H, Nguyen H. 2019. A particle-based optimization of artificial neural network for earthquake-induced landslide assessment in Ludian county, China. Geomatics Nat Hazards Risk. 10(1):1750–1771.
  • Yang Z, Qiao J, Uchimura T, Wang L, Lei X, Huang D. 2017. Unsaturated hydro-mechanical behaviour of rainfall-induced mass remobilization in post-earthquake landslides. Eng Geol. 222:102–110.
  • Yousefi S, Jaafari A, Valjarević A, Gomez C, Keesstra S. 2022a. Vulnerability assessment of road networks to landslide hazards in a dry-mountainous region. Environ Earth Sci. 81(22):521.
  • Yousefi S, Mirzaee S, Almohamad H, Al Dughairi AA, Gomez C, Siamian N, Alrasheedi M, Abdo HG. 2022b. Image classification and land cover mapping using sentinel-2 imagery: optimization of SVM parameters. Land. 11(7):993.
  • Yu C, Chen J. 2020. Landslide susceptibility mapping using the slope unit for southeastern Helong City, Jilin Province, China: a comparison of ANN and SVM. Symmetry. 12(6):1047.
  • Yuan X, Liu C, Nie R, Yang Z, Li W, Dai X, Cheng J, Zhang J, Ma L, Fu X, et al. 2022. A comparative analysis of certainty factor-based machine learning methods for collapse and landslide susceptibility mapping in Wenchuan County, China. Remote Sens. 14(14):3259.
  • Zhao S, Zhao Z. 2021. A comparative study of landslide susceptibility mapping using SVM and PSO-SVM models based on Grid and Slope Units. Math Prob Eng. 2021:1–15.
  • Zhu A-X, Miao Y, Yang L, Bai S, Liu J, Hong H. 2018. Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping. Catena. 171:222–233.
  • Zhu Z, Wang H, Pang B, Dou J, Peng D. 2019. Comparison of conventional deterministic and entropy-based methods for predicting sediment concentration in debris flow. Water. 11(3):439.