Abstract
Landslides are the most common and catastrophic activities in the mountainous topography which are responsible for extensive economic and human losses. A regional-scale area susceptible to landslides, located in Hunza and Nagar Districts in the Northern part of Pakistan, was selected for landslide susceptibility mapping (LSM). The objective of the work aims to evaluate the reliability of the models by a comparative analysis of Bi-variate statistical and deep machine learning models (DMLTs). The statistical models such as “weight of evidence (WofE), frequency ratio (FR), information valve (IV), Shannon entropy (SE) and machine learning technique (MLTs), deep machine neural network (DM-NN), and random forest (RF)” were applied for landslide susceptibility mapping (LSM). The major scope of the work was to prepare a reliable landslide susceptibility map by the comparative analysis of six aforementioned statistical and deep machine learning models. It also seeks to analyze the influence of geo-environmental landslide conditional factors contributing to landslides. Information about landslide inventory and 12 pre-defined geo-environmental landslide causative factors were selected for LSM of the study area. The locations of 148 landslides were identified and mapped from detail field survey using Garmin GPS. The results were validated using area under cure (AUC) for prediction accuracy and seed cell area index (SCAI) tests were applied for the classification ability of LSM models. The results revealed that the prediction accuracy of the models for WofE, FR, SE, IV, DM-NN, and RF are 83.70%, 82.26%, 75%, 70.7%, 80.5%, and 80.6% respectively. From the differential value of seed cell area index the (SCAI) represented by (D_value), the results shown for the LSM are (IV=10.82) (WofE=12.99) (FR=8.47) (SE=5.32) (RF=11.39) (DM-NN=12.19). The results revealed that WofE and DM-NN have similar accuracy and far better classification ability than other models. In terms of the prediction accuracy rate, WofE model has the highest prediction accuracy and the best classification ability. As such, the landslide susceptibility map produced from WofE is proposed to be more useful for this study area. The susceptibility map of this area can be useful for land use planning and engineering purposes.
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References
Aditian A, Kubota T, 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. https://doi.org/10.1016/j.geomorph.2018.06.006
Ahmed B, Rahman MS, Sammonds P, Islam R, Uddin K (2020) Application of geospatial technologies in developing a dynamic landslide early warning system in a humanitarian context: the Rohingya refugee crisis in Cox’s Bazar, Bangladesh. Geomat Nat Haz Risk 11(1):446–468
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. https://doi.org/10.5194/nhess-19-999-2019
Arabameri A, Pourghasemi HR (2019) 13—spatial modeling of gully erosion using linear and quadratic discriminant analyses in GIS and R. In H. R. Pourghasemi & C. Gokceoglu (Eds.), Spatial Modeling in GIS and R for Earth and Environmental Sciences (pp. 299–321). Elsevier. https://doi.org/10.1016/B978-0-12-815226-3.00013-2
Bacha AS, Shafique M, van der Werff H (2018) Landslide inventory and susceptibility modelling using geospatial tools, in Hunza-Nagar valley, northern Pakistan. J Mt Sci 15(6):1354–1370. https://doi.org/10.1007/s11629-017-4697-0
Bai S, Wang J, Lü G-N, Zhou P-G, Hou S-S, 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):23–31. https://doi.org/10.1016/j.geomorph.2009.09.025
Brabb EE (1993) Priorities for landslide mapping during the International Decade of Hazard Reduction. In: International conference and field workshop on landslides, pp 7–14
Chen W, Panahi M, Tsangaratos P, Shahabi H, Ilia I, Panahi S, Li S, Jaafari A, Ahmad BB (2019a) Applying population-based evolutionary algorithms and a neuro-fuzzy system for modeling landslide susceptibility. CATENA 172:212–231. https://doi.org/10.1016/j.catena.2018.08.025
Chen W, Zhao X, Shahabi H, Shirzadi A, Khosravi K, Chai H, Zhang S, Zhang L, Ma J, Chen Y, Wang X, Bin Ahmad B, Li R (2019b) Spatial prediction of landslide susceptibility by combining evidential belief function, logistic regression and logistic model tree. Geocarto International 34(11):1177–1201. https://doi.org/10.1080/10106049.2019.1588393
Cossart E, Fort M (2008) Consequences of landslide dams on alpine river valleys: examples and typology from the French Southern Alps. Norsk Geografisk Tidsskrift - Norwegian J Geography 62(2):75–88. https://doi.org/10.1080/00291950802094882
Dunning SA, Mitchell WA, Rosser NJ, Petley DN (2007) The Hattian Bala rock avalanche and associated landslides triggered by the Kashmir Earthquake of 8 October 2005. Eng Geol 93(3–4):130–144
Felicísimo ÁM, Cuartero A, Remondo J, Quirós E (2013) Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: a comparative study. Landslides 10(2):175–189. https://doi.org/10.1007/s10346-012-0320-1
Guzzetti F, Carrara A, Cardinali M, Reichenbach P (1999) Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy. Geomorphology 31(1–4):181–216
He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Chai H, Bian H, Ma J, Chen Y, Wang X, Chapi K, Ahmad BB (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 AT, Mosher DC (2001) Late quaternary stratigraphy and seafloor geology of eastern Juan de Fuca Strait, British Columbia and Washington. Mar Geol 177(3–4):295–316
Holmes J (1993) Ostracod faunal and microchemical evidence for middle Pleistocene sea-level change at Clacton-on-Sea (Essex, UK)
Hong H, Kornejady A, Soltani A, Termeh SVR, Liu J, Zhu A-X, Hesar AY, Ahmad BB, Wang Y (2018) Landslide susceptibility assessment in the Anfu County, China: comparing different statistical and probabilistic models considering the new topo-hydrological factor (HAND). Earth Sci Inf 11(4):605–622. https://doi.org/10.1007/s12145-018-0352-8
Hong H, Pradhan B, Sameen MI, Kalantar B, Zhu A, Chen W (2017) Improving the accuracy of landslide susceptibility model using a novel region-partitioning approach. Landslides 15(4):753–772. https://doi.org/10.1007/s10346-017-0906-8
Hong H, Pradhan B, Xu C, Bui DT (2015) Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines. Catena 133:266–281
Huang F, Cao Z, Guo J, Jiang S-H, Li S, Guo Z (2020) Comparisons of heuristic, general statistical and machine learning models for landslide susceptibility prediction and mapping. CATENA 191:104580. https://doi.org/10.1016/j.catena.2020.104580
Juliev M, Mergili M, Mondal I, Nurtaev B, Pulatov A, Hübl J (2019) Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan. Sci Total Environ 653:801–814
Kalantar B, Pradhan B, Naghibi SA, Motevalli A, Mansor S (2018) Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN). Geomatics, Natural Hazards and Risk 9(1):49–69. https://doi.org/10.1080/19475705.2017.1407368
Kargel JS, Leonard GJ, Shugar DH, Haritashya UK, Bevington A, Fielding EJ, ... Young N (2016) Geomorphic and geologic controls of geohazards induced by Nepal’s 2015 Gorkha earthquake. Science 351(6269):aac8353
Karim KH, Koyi H, Baziany MM, Hessami K (2011) Significance of angular unconformities between cretaceous and tertiary strata in the northwestern segment of the Zagros fold–thrust belt, Kurdistan Region, NE Iraq. Geol Mag 148(5–6):925–939
Kettles IM, Bonham-Carter GF (2002) Modelling dispersal of metals from a copper smelter at Rouyn-Noranda (Québec, Canada) using peatland data. Geochem Explor Environ Anal 2(2):99–110
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
Lee S, Pradhan B (2006) Probabilistic landslide hazards and risk mapping on Penang Island. Malaysia J Earth Syst Sci 115(6):661–672. https://doi.org/10.1007/s12040-006-0004-0
Mahmood I, Qureshi SN, Tariq S, Atique L, Iqbal MF (2015) Analysis of landslides triggered by October 2005, Kashmir Earthquake. PLoS Currents 7
Meng L, Li W, Zhang S, Wu C, Jiang W, Sha C (2016) Effect of different extra carbon sources on nitrogen loss control and the change of bacterial populations in sewage sludge composting. Ecol Eng 94:238–243
Mondal S, Mandal S (2019a) Landslide susceptibility mapping of Darjeeling Himalaya, India using index of entropy (IOE) model. Appl Geomat 11(2):129–146. https://doi.org/10.1007/978-3-319-93897-4_1
Mondal S, Mandal S (2019b) Statistical approaches for landslide susceptibility assessment and prediction. Cham, Springer International Publishing. https://doi.org/10.1007/978-3-319-93897-4_3
Park S, Kim J (2019) Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl Sci 9(5):942
Paryani S, Neshat A, Javadi S, Pradhan B (2020) Comparative performance of new hybrid ANFIS models in landslide susceptibility mapping. Nat Hazards 103(2):1961–1988. https://doi.org/10.1007/s11069-020-04067-9
Pashkov BR, Shvol’man VA (1979) Riftogenic margins of Tethys in the Pamir. Geotektonika 6:42–57
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2017) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods. Theor Appl Climatol 128(1–2):255–273. https://doi.org/10.1007/s00704-015-1702-9
Pourghasemi HR, Mohammady M, Pradhan B (2012) Landslide susceptibility mapping using index of entropy and conditional probability models in GIS: Safarood Basin, Iran. CATENA 97:71–84. https://doi.org/10.1016/j.catena.2012.05.005
Pourghasemi HR, Moradi HR, Fatemi Aghda SM (2013) 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. https://doi.org/10.1007/s11069-013-0728-5
Pradhan B, Mansor S, Pirasteh S, Buchroithner MF (2011) Landslide hazard and risk analyses at a landslide prone catchment area using statistical based geospatial model. Int J Remote Sens 32(14):4075–4087. https://doi.org/10.1080/01431161.2010.484433
Pradhan B, Al-Najjar HAH, Sameen MI, Mezaal MR, Alamri AM (2020) Landslide detection using a saliency feature enhancement technique from LiDAR-derived DEM and orthophotos. IEEE Access 8:121942–121954. https://doi.org/10.1109/ACCESS.2020.3006914
Reichenbach P, Rossi M, Malamud BD, Mihir M, Guzzetti F (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180:60–91
Rosdi MNHM, Mahmood WHW, Razik MA, Kamat SR (2021) Fuzzy analytic hierarchical process implementation on enhancing manufacturing responsiveness. J King Saud Univ Eng Sci
Sahin EK, Colkesen I, Kavzoglu T (2020) A comparative assessment of canonical correlation forest, random forest, rotation forest and logistic regression methods for landslide susceptibility mapping. Geocarto Int 35(4):341–363. https://doi.org/10.1080/10106049.2018.1516248
Sameen MI, Pradhan B, Lee S (2020) Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment. CATENA 186:104249. https://doi.org/10.1016/j.catena.2019.104249
Searle MP, Tirrul R (1991) Structural and thermal evolution of the Karakoram crust. J Geol Soc 148(1):65–82
Shroder Jr JF, Bishop MP (1998) Mass movement in the Himalaya: new insights and research directions. Geomorphology 26(1–3):13–35
Sun D, Shi S, Wen H, Xu J, Zhou X, Wu J (2021) A hybrid optimization method of factor screening predicated on GeoDetector and random forest for landslide susceptibility mapping. Geomorphology 379:107623. https://doi.org/10.1016/j.geomorph.2021.107623
Süzen ML, Doyuran V (2004) A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environ Geol 45(5):665–679
Teerarungsigul S, Torizin J, Fuchs M, Kühn F, Chonglakmani C (2016) An integrative approach for regional landslide susceptibility assessment using weight of evidence method: a case study of Yom River Basin, Phrae Province, Northern Thailand. Landslides 13(5):1151–1165. https://doi.org/10.1007/s10346-015-0659-1
Tien Bui D, 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. https://doi.org/10.1007/s10346-015-0557-6
Wieczorek GF (1996) Landslides: investigation and mitigation. Chapter 4-Landslide triggering mechanisms. Trans Res Board Spec Rep (247)
Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov RR, Le QV (2019) Xlnet: generalized autoregressive pretraining for language understanding. Adv Neural Inf Proces Syst 32
Yan F, Zhang Q, Ye S, Ren B (2019) A novel hybrid approach for landslide susceptibility mapping integrating analytical hierarchy process and normalized frequency ratio methods with the cloud model. Geomorphology 327:170–187. https://doi.org/10.1016/j.geomorph.2018.10.024
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 35:11251138. https://doi.org/10.1016/j.cageo.2008.08.007
Yufeng S, Fengxiang J (2009) Landslide stability analysis based on generalized information entropy. In: 2009 international conference on environmental science and information application technology (vol. 2, pp. 83–85). IEEE
Zhang YG, Tang J, Liao RP, Zhang MF, Zhang Y, Wang XM, Su ZY (2021) Application of an enhanced BP neural network model with water cycle algorithm on landslide prediction. Stochastic Environ Res Risk Assess 35(6):1273–1291
Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: An extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6848–6856
Acknowledgements
The author is grateful to Samantha and Sultan Abbas for tremendous help in editing the manuscript.
Funding
This work was funded by the Chinese Governement Scholarship Council (CSC).
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Appendix
Appendix
Factors | Classes | (STP) stable pixel each class | (LDP) landslide pixel each class | FR | IV | SE | WofE | ||||
W+ | W- | Contrast (C) | S( C ) | C/S( Cs ) | |||||||
Slope | 0–10 | 10,673,912 | 7963 | 0.00 | –0.61 | 0.09 | –1.40 | 0.09 | –1.49 | 0.67 | –2.21 |
10–20 | 12,222,692 | 13,645 | 0.37 | –0.43 | –0.99 | 0.09 | –1.09 | 0.55 | –1.98 | ||
20–30 | 16,617,234 | 53,055 | 1.06 | 0.02 | 0.06 | –0.01 | 0.07 | 0.36 | 0.19 | ||
30–40 | 24,525,672 | 120,091 | 1.62 | 0.21 | 0.48 | –0.26 | 0.74 | 0.30 | 2.45 | ||
40–50 | 17,540,848 | 61,826 | 1.17 | 0.07 | 0.16 | –0.04 | 0.20 | 0.35 | 0.56 | ||
50–60 | 7,842,431 | 16,212 | 0.69 | –0.16 | –0.38 | 0.03 | –0.41 | 0.56 | –0.73 | ||
>60 | 2,325,655 | 3899 | 0.56 | –0.25 | –0.59 | 0.01 | –0.60 | 1.06 | –0.56 | ||
Aspect | Flate (−1) | 810,004 | 4559 | 1.87 | 0.27 | 0.10 | 0.62 | –0.01 | 0.63 | 1.33 | 0.48 |
North (0–22.5) | 6,402,821 | 28,974 | 1.50 | 0.18 | 0.41 | –0.04 | 0.44 | 0.51 | 0.87 | ||
Northeast (22.5–67.5) | 12,467,219 | 47,337 | 1.26 | 0.10 | 0.23 | –0.04 | 0.27 | 0.39 | 0.69 | ||
East (67.5–112.5) | 11,039,494 | 9454 | 0.28 | –0.55 | –1.26 | 0.09 | –1.35 | 0.63 | –2.14 | ||
southeast (112.5–157.5) | 11,073,594 | 19,762 | 0.59 | –0.23 | –0.52 | 0.05 | –0.58 | 0.49 | –1.17 | ||
South (157.5–202.5) | 11,541,955 | 60,733 | 1.74 | 0.24 | 0.56 | –0.11 | 0.67 | 0.39 | 1.73 | ||
Southwest (202.5–247.5) | 12,256,765 | 52,173 | 1.41 | 0.15 | 0.34 | –0.07 | 0.41 | 0.39 | 1.05 | ||
West (247.5–292.5) | 10,294,776 | 19,912 | 0.64 | –0.19 | –0.44 | 0.04 | –0.49 | 0.50 | –0.98 | ||
Northwest(292.5–337.5) | 10,521,353 | 17,456 | 0.55 | –0.26 | –0.60 | 0.06 | –0.65 | 0.52 | –1.26 | ||
North (337.5–360) | 5,340,463 | 16331 | 1.01 | 0.01 | 0.01 | 0.00 | 0.01 | 0.60 | 0.02 | ||
Elevation | 0–1700 | 6 | 0 | 0.00 | 0.00 | 0.08 | 0.00 | 0.00 | 0.00 | 0.14 | 0.00 |
1700–2000 | 290,915 | 6980 | 7.96 | 0.90 | 2.07 | −0.02 | 2.10 | 1.89 | 1.11 | ||
2000–3000 | 4,426,712 | 135,690 | 10.16 | 1.01 | 2.32 | −0.62 | 2.94 | 0.51 | 5.80 | ||
3000–4000 | 16,380,340 | 131,055 | 2.65 | 0.42 | 0.98 | −0.45 | 1.42 | 0.33 | 4.32 | ||
>4000 | 7,065,0471 | 2965 | 0.01 | −1.86 | −4.27 | 1.46 | −5.73 | 1.00 | −5.73 | ||
Curvature | Convex (−1.28–−0.001 | 25,940,251 | 80,636 | 1.03 | 0.01 | 0.08 | 0.03 | −0.01 | 0.04 | 0.31 | 0.14 |
Flat −0.001–1.29) | 55,773,965 | 17,1292 | 1.02 | 0.01 | 0.02 | −0.03 | 0.05 | 0.29 | 0.16 | ||
Concave (1.29–88.32) | 10,034,228 | 24,762 | 0.82 | −0.09 | −0.20 | 0.02 | −0.22 | 0.47 | −0.47 | ||
Lithology | Southern Karakorum metamorphic | 9,357,596 | 70,470 | 2.50 | 0.40 | 0.09 | 0.91 | −0.19 | 1.10 | 0.40 | 2.74 |
Hunza plutonic unit | 6,146,242 | 5614 | 0.30 | −0.52 | −1.19 | 0.05 | −1.24 | 0.81 | −1.53 | ||
Triassic massive limestone | 2,582,095 | 5946 | 0.76 | −0.12 | −0.27 | 0.01 | −0.28 | 0.92 | −0.30 | ||
eclogites | 10,353,152 | 0 | 0.00 | 0.00 | 0.00 | 0.12 | −0.12 | 0.15 | −0.82 | ||
glacier | 35,163,540 | 566 | 0.01 | −2.27 | −5.23 | 0.48 | −5.72 | 2.22 | −2.58 | ||
Permian massive | 10,934,340 | 71,399 | 2.16 | 0.34 | 0.77 | −0.17 | 0.94 | 0.38 | 2.46 | ||
quaternary deposits | 2,938,134 | 94,377 | 10.65 | 1.03 | 2.37 | −0.39 | 2.75 | 0.60 | 4.55 | ||
cretaceous sandstone | 1,881,467 | 21,219 | 3.74 | 0.57 | 1.32 | −0.06 | 1.38 | 0.80 | 1.73 | ||
Misgar slates | 10,143,545 | 5383 | 0.18 | −0.75 | −1.74 | 0.10 | −1.84 | 0.79 | −2.32 | ||
Yasin sediments | 493,283 | 633 | 0.43 | −0.37 | −0.85 | 0.00 | −0.86 | 2.50 | −0.34 | ||
Chalt volcanic | 884,343 | 0 | 0.00 | 0.00 | 0.00 | 0.01 | −0.01 | 0.14 | −0.07 | ||
Kohistan batholith | 41,605 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.14 | 0.00 | ||
Northern Karakoram Terrance | 435,112 | 0 | 0.00 | 0.00 | 0.00 | 0.00 | 0.00 | 0.14 | −0.03 | ||
Dist_fault (m) | 0–1000 | 12,345,198 | 75,160 | 2.02 | 0.30 | 0.09 | 0.70 | −0.17 | 0.87 | 0.37 | 2.37 |
1000–2000 | 7,116,810 | 46,309 | 2.16 | 0.33 | 0.77 | −0.10 | 0.87 | 0.46 | 1.89 | ||
2000–3000 | 4,813,319 | 52,215 | 3.60 | 0.56 | 1.28 | −0.16 | 1.44 | 0.52 | 2.78 | ||
3000–4000 | 4,390,897 | 39,129 | 2.95 | 0.47 | 1.08 | −0.10 | 1.19 | 0.55 | 2.16 | ||
4000–5000 | 7,474,161 | 32,764 | 1.45 | 0.16 | 0.37 | −0.04 | 0.42 | 0.48 | 0.87 | ||
>5000 | 55,608,059 | 31,113 | 0.19 | −0.73 | -1.68 | 0.81 | −2.50 | 0.38 | −6.62 | ||
Dist_river(m) | 0–200 | 4,911,209 | 38,647 | 2.61 | 0.42 | 0.09 | 0.96 | −0.10 | 1.05 | 0.53 | 1.99 |
200–400 | 4,427,540 | 62,049 | 4.65 | 0.67 | 1.54 | −0.20 | 1.74 | 0.52 | 3.32 | ||
400–600 | 4,105,746 | 57,210 | 4.62 | 0.66 | 1.53 | −0.19 | 1.72 | 0.54 | 3.16 | ||
600–800 | 3,963,514 | 45,730 | 3.83 | 0.58 | 1.34 | −0.14 | 1.48 | 0.56 | 2.64 | ||
>800 | 74,340,288 | 73,054 | 0.33 | -0.49 | −1.12 | 1.36 | −2.48 | 0.34 | −7.26 | ||
Dist_Road | 0–500 | 2,889,524 | 129,326 | 14.84 | 1.17 | 0.08 | 2.70 | −0.60 | 3.30 | 0.61 | 5.43 |
500–1000 | 2,402,126 | 88,376 | 12.20 | 1.09 | 2.50 | −0.36 | 2.86 | 0.66 | 4.32 | ||
1000–1500 | 2,287,711 | 36,399 | 5.28 | 0.72 | 1.66 | −0.12 | 1.78 | 0.71 | 2.52 | ||
1500–2000 | 2,214,121 | 10,905 | 1.63 | 0.21 | 0.49 | −0.02 | 0.51 | 0.83 | 0.61 | ||
>2000 | 81,954,962 | 11,684 | 0.05 | −1.33 | −3.05 | 2.19 | −5.25 | 0.59 | −8.84 | ||
SPI | (−5.7–−1.20) | 7,395,668 | 22,606 | 1.01 | 0.01 | 0.08 | 0.00 | 0.00 | 0.00 | 0.52 | 0.00 |
(−1.20–0.37) | 31,839,391 | 55,789 | 0.58 | −0.24 | −0.54 | 0.20 | −0.74 | 0.33 | −2.28 | ||
(0.37–1.20) | 38,492,081 | 131,706 | 1.13 | 0.05 | 0.13 | −0.10 | 0.23 | 0.28 | 0.80 | ||
(1.20–10.43) | 14,021,304 | 66,589 | 1.57 | 0.20 | 0.45 | −0.11 | 0.56 | 0.36 | 1.55 | ||
LC | Natural forest | 342,085 | 48 | 0.05 | −1.33 | 0.09 | −3.07 | 0.00 | −3.08 | 7.77 | −0.40 |
Orchards | 410,062 | 3678 | 2.96 | 0.47 | 1.08 | −0.01 | 1.09 | 1.73 | 0.63 | ||
Agriculture land | 199,084 | 623 | 1.03 | 0.02 | 0.03 | 0.00 | 0.03 | 3.01 | 0.01 | ||
Summer pasture | 2,399,038 | 2649 | 0.36 | −0.44 | −1.01 | 0.02 | −1.03 | 1.20 | −0.85 | ||
Winter pasture | 2,244,705 | 16,382 | 2.41 | 0.38 | 0.88 | −0.04 | 0.91 | 0.77 | 1.18 | ||
River/lakes | 113,378 | 33 | 0.10 | −1.02 | −2.34 | 0.00 | −2.34 | 9.59 | −0.24 | ||
Settlements | 27,535 | 124 | 1.48 | 0.17 | 0.40 | 0.00 | 0.40 | 7.45 | 0.05 | ||
Barren land | 61,298,772 | 253,131 | 1.36 | 0.14 | 0.31 | −1.35 | 1.66 | 0.42 | 3.98 | ||
Snow/glacier | 24,189,260 | 22 | 0.00 | −3.52 | −8.11 | 0.31 | −8.42 | 11.22 | −0.75 | ||
Soil | 1-x 2c | 11,798 | 0 | 0.00 | 0.00 | 0.05 | 0.00 | 0.00 | 0.00 | 0.14 | 0.00 |
GL | 42,408,073 | 11,810 | 0.09 | −1.03 | −2.40 | 0.59 | −2.99 | 0.53 | −5.61 | ||
1-y-2c | 47,719,825 | 0 | 0.00 | 0.00 | 0.00 | 0.75 | −0.75 | 0.18 | −4.26 | ||
1-b-u | 1021 | 264,880 | 8.40 | 4.93 | 11.34 | −3.15 | 14.50 | 29.72 | 0.49 | ||
TWI | >12 | 78,961,564 | 267,502 | 1.12 | 0.05 | 0.08 | 0.12 | −1.43 | 1.55 | 0.63 | 2.47 |
12–24 | 7,237,810 | 3186 | 0.15 | −0.84 | −1.92 | 0.07 | −1.99 | 1.01 | −1.98 | ||
24–36 | 2,559,283 | 713 | 0.09 | −1.03 | −2.38 | 0.03 | −2.41 | 2.06 | −1.17 | ||
36–48 | 614,958 | 165 | 0.09 | −1.05 | −2.42 | 0.01 | −2.43 | 4.28 | −0.57 | ||
>48 | 2,374,829 | 5124 | 0.72 | −0.15 | −0.33 | 0.01 | −0.34 | 0.97 | −0.35 | ||
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Khan, A., Shitao, Z. & Khan, G. Comparative analysis and landslide susceptibility mapping of Hunza and Nagar Districts, Pakistan. Arab J Geosci 15, 1644 (2022). https://doi.org/10.1007/s12517-022-10865-1
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DOI: https://doi.org/10.1007/s12517-022-10865-1