Abstract
Debris flow susceptibility mapping (DFSM) has been reported in many studies, however, the irrational use of the same conditioning factor system for DFSM in regional-scale has not been thoroughly resolved. In this paper, a region-partitioning method that is based on the topographic characteristics of watershed units was developed with the objective of establishing multiple conditioning factor systems for regional-scale DFSM. First, watershed units were selected as the mapping units and created throughout the entire research area. Four topographical factors, namely, elevation, slope, aspect and relative height difference, were selected as the basis for clustering watershed units. The k-means clustering analysis was used to cluster the watershed units according to their topographic characteristics to partition the study area into several parts. Then, the information gain ratio method was used to filter out superfluous factors to establish conditioning factor systems in each region for the subsequent debris flow susceptibility modeling. Last, a debris flow susceptibility map of the whole study area was acquired by merging the maps from all parts. DFSM of Yongji County in Jilin Province, China was selected as a case study, and the analytical hierarchy process method was used to conduct a comparative analysis to evaluate the performance of the region-partitioning method. The area under curve (AUC) values showed that the partitioning of the study area into two parts improved the prediction rate from 0.812 to 0.916. The results demonstrate that the region-partitioning method on the basis of topographic characteristics of watershed units can realize more reasonable regional-scale DFSM. Hence, the developed region-partitioning method can be used as a guide for regional-scale DFSM to mitigate the imminent debris flow risk.
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References
Alvioli M, Marchesini I, Reichenbach P, et al. (2016) Automatic delineation of geomorphological slope units with r.slopeunits v1.0 and their optimization for landslide susceptibility modeling. Geosci Model Dev 9(11): 3975–3991. https://doi.org/10.5194/gmd-9-3975-2016
Arabameri A, Pradhan B, Rezaei K, et al. (2019) GIS-based landslide susceptibility mapping using numerical risk factor bivariate model and its ensemble with linear multivariate regression and boosted regression tree algorithms. J Mt Sci 16(3): 595–618. https://doi.org/10.1007/s11629-018-5168-y
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
Beven K, Freer J (2001) A dynamic TOPMODEL. Hydrol Process 15(10): 1993–2011. https://doi.org/10.1002/hyp.252
Blais-Stevens A, Behnia P (2016) Debris flow susceptibility mapping using a qualitative heuristic method and Flow-R along the Yukon Alaska Highway Corridor, Canada. Nat Hazards Earth Syst Sci 16(2): 449–462. https://doi.org/10.5194/nhess-16-449-2016
Bregoli F, Medina V, Chevalier G, et al. (2015) Debris-flow susceptibility assessment at regional scale: Validation on an alpine environment. Landslides 12(3): 437–454. https://doi.org/10.1007/s10346-014-0493-x
Cavalli M, Crema S, Trevisani S, et al. (2017) GIS tools for preliminary debris-flow assessment at regional scale. J Mt Sci 14: 2498–2510. https://doi.org/10.1007/s11629-017-4573-y
Chacon J, Irigaray C, Fernandez T, et al. (2006) Engineering geology maps: landslides and geographical information systems. Bull Eng Geol Environ 65(4): 341–411. https://doi.org/10.1007/s10064-006-0064-z
Chang M, Tang C, Zhang D, et al. (2014) Debris flow susceptibility assessment using a probabilistic approach: A case study in the Longchi area, Sichuan province, China. J Mt Sci 11(4): 1001–1014. https://doi.org/10.1007/s11629-013-2747-9
Chang T, Chao R (2006) Application of back-propagation networks in debris flow prediction. Eng Geol 85(3–4): 270–280. https://doi.org/10.1016/j.enggeo.2006.02.007
Chen W, Panahi M, Pourghasemi HR (2017) Performance evaluation of GIS-based new ensemble data mining techniques of adaptive neuro-fuzzy inference system (ANFIS) with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) for landslide spatial modelling. Catena 157: 310–324. https://doi.org/10.1016/j.catena.2017.05.034
Chen W, Pourghasemi HR, Kornejady A, et al. (2017) Landslide spatial modeling: Introducing new ensembles of ANN, MaxEnt, and SVM machine learning techniques. Geoderma 305: 314–327. https://doi.org/10.1016/j.geoderma.2017.06.020
Chen Y, Qin S, Qiao S, et al. (2020) Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China. Water 12(8): 2079. https://doi.org/10.3390/w12082079
Chen Z, Liang SY, Ke YT, et al. (2019) Landslide susceptibility assessment using different slope units based on the evidential belief function model. Geocarto Int 35(15): 1–25. https://doi.org/10.1080/10106049.2019.1582716
Chevalier GG, Medina V, Huerlimann M, et al. (2013) Debris-flow susceptibility analysis using fluvio-morphological parameters and data mining: application to the Central-Eastern Pyrenees. Nat Hazards 67(2): 213–238. https://doi.org/10.1007/s11069-013-0568-3
Costanzo D, Rotigliano E, Irigaray C, et al. (2012) Factors selection in landslide susceptibility modelling on large scale following the gis matrix method: application to the river Beiro basin (Spain). Nat Hazards Earth Syst Sci 12(2): 327–340. https://doi.org/10.5194/nhess-12-327-2012
Dieu TB, Pradhan B, Lofman O, et al. (2012) Landslide susceptibility assessment in the Hoa Binh province of Vietnam: A comparison of the Levenberg-Marquardt and Bayesian regularized neural networks. Geomorphology 171: 12–29. https://doi.org/10.1016/j.geomorph.2012.04.023
Dieu TB, Tran AT, Klempe H, et al. (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
Ding C, He X (2004) Cluster structure of K-means clustering via principal component analysis. Lect Notes Comput Sci 46(4):414–418. https://doi.org/10.1007/978-3-540-24775-3_50
Dou J, Yunus A, Dieu TB, et al. (2019) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17(3):641–658. https://doi.org/10.1007/s10346-019-01286-5
Dou Q, Qin S, Zhang Y, et al. (2019b) A method for improving controlling factors based on information fusion for debris flow susceptibility mapping: A case study in Jilin province, China. Entropy 21(6957).https://doi.org/10.3390/e21070695
Du G, Zhang Y, Yang Z, et al. (2019) Landslide susceptibility mapping in the region of eastern Himalayan syntaxis, Tibetan Plateau, China: a comparison between analytical hierarchy process information value and logistic regression-information value methods. Bull Eng Geol Environ 78(6): 4201–4215. https://doi.org/10.1007/s10064-018-1393-4
Ercanoglu M, Gokceoglu C (2002) Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach. Eng Geol 41(6): 720–730. https://doi.org/10.1007/s00254-001-0454-2
Erener A, Duzgun HSB (2012) Landslide susceptibility assessment: what are the effects of mapping unit and mapping method?. Environ Earth Sci 66(3): 859–877. https://doi.org/10.1007/s12665-011-1297-0
Fekete A (2019) Social vulnerability change assessment: monitoring longitudinal demographic indicators of disaster risk in Germany from 2005 to 2015. Nat Hazards 95(3): 585–614. https://doi.org/10.1007/s11069-018-3506-6
Fernandez T, Irigaray C, El Hamdouni, et al. (2003) Methodology for landslide susceptibility mapping by means of a GIS. Application to the Contraviesa area (Granada, Spain). Nat Hazards 30(3): 297–308. https://doi.org/10.1023/B:NHAZ.0000007092.51910.3f
Hasekiogullari GD, Ercanoglu M (2012) A new approach to use AHP in landslide susceptibility mapping: a case study at Yenice (Karabuk, NW Turkey). Nat Hazards 63(2): 1157–1179. https://doi.org/10.1007/s11069-012-0218-1
Hong H, Pradhan B, Sameen MI, et al. (2018) 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, Naghibi SA, Pourghasemi HR, et al. (2016) GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9(112). https://doi.org/10.1007/s12517-015-2094-y
Hong H, Pradhan B, Xu C, et al. (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. https://doi.org/10.1016/j.catena.2015.05.019
Hughes G (2003) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14(1): 55–63. https://doi.org/10.1109/TIT.1968.1054102
Hu GS, Chen NS, Tanoli JI, et al. (2017) Debris flow susceptibility analysis based on the combined impacts of antecedent earthquakes and droughts: a case study for cascade hydropower stations in the upper Yangtze River, China. J Mt Sci 14(9): 1712–1727. https://doi.org/10.1007/s11629-017-4375-1
Kanungo T, Mount DM, Netanyahu NS, et al. (2002) An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7): 881–892. https://doi.org/10.1109/TPAMI.2002.1017616
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1): 33–41. https://doi.org/10.1007/s10346-006-0047-y
Li WL, Huang RQ, Tang C, et al. (2013) Co-seismic landslide inventory and susceptibility mapping in the 2008 Wenchuan earthquake disaster area, China. J Mt Sci 10(3): 339–354. https://doi.org/10.1007/s11629-013-2471-5
Li Y, Wang H, Chen J, et al. (2017) Debris flow susceptibility assessment in the wudongde dam area, china based on rock engineering system and fuzzy c-means algorithm. Water 9(6699). https://doi.org/10.3390/w9090669
Liu Y, Zhang JJ, Zhu CH, et al. (2019) Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network. J Mt Sci 16(8): 1975–1985. https://doi.org/10.1007/s11629-018-5358-7
Liang Z, Wang CM, Zhang ZM, et al. (2020) A comparison of statistical and machine learning methods for debris flow susceptibility mapping. Stoch. Environ Res Risk Assess 34: 1887–1907. https://doi.org/10.1007/s00477-020-01851-8
Likas A, Vlassis N, Verbeek JJ (2002) The Global K-Means Clustering Algorithm. Pattern Recognit 36(2):451–461. https://doi.org/10.1016/S0031-3203(02)00060-2
Lin PS, Lin JY, Hung HC, et al. (2014) Assessing debris-flow hazard in a watershed in Taiwan. Eng Geol 66(3–4):295–313. https://doi.org/10.1016/S0013-7952(02)00105-9
Ma Z, Qin S, Cao C, et al. (2019) The influence of different knowledge-driven methods on landslide susceptibility mapping: A case study in the Changbai mountain area, Northeast China. Entropy 21(3724). https://doi.org/10.3390/e21040372
Murillo-García FG, Rossi M, Ardizzone F, et al. (2017) Hazard and population vulnerability analysis: a step towards landslide risk assessment. J Mt Sci 14(7): 1241–1261. https://doi.org/10.1007/s11629-016-4179-9
Nonomura A, Hasegawa S, Matsumoto H, et al. (2019) Curvature derived from LiDAR digital elevation models as simple indicators of debris-flow susceptibility. J Mt Sci 16(1): 95–107. https://doi.org/10.1007/s11629-018-5098-8
Oh H, 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
Pastorello R, Michelini T, D’Agostino V (2017) On the criteria to create a susceptibility map to debris flow at a regional scale using Flow-R. J Mt Sci 14(4): 621–635. https://doi.org/10.1007/s11629-016-4077-1
Polat A, Erik D (2020) Debris flow susceptibility and propagation assessment in West Koyulhisar, Turkey. J Mt Sci 17(11): 2611–2623. https://doi.org/10.1007/s11629-020-6261-6
Pourghasemi HR, Kerle N (2016) Random forests and evidential belief function-based landslide susceptibility assessment in Western Mazandaran Province, Iran. Environ. Earth Sci 75(1853). https://doi.org/10.1007/s12665-015-4950-1
Pradhan AMS, Kim YT (2016) Evaluation of a combined spatial multi-criteria evaluation model and deterministic model for landslide susceptibility mapping. Catena 140: 125–139. https://doi.org/10.1016/j.catena.2016.01.022
Pradhan B, Lee S (2009) Landslide risk analysis using artificial neural network model focussing on different training sites. Int J Phys Sci 4(1): 1–15. https://doi.org/10.1002/dev.20499
Pradhan B, Lee S, Buchroithner MF (2010) A GIS-based back-propagation neural network model and its cross-application and validation for landslide susceptibility analyses. Comput Environ Urban Syst 34(3): 216–235. https://doi.org/10.1016/j.compenvurbsys.2009.12.004
Pradhan B, Oh H, Buchroithner M (2010) Weights-of-evidence model applied to landslide susceptibility mapping in a tropical hilly area. Geomat Nat Hazards Risk 1(3): 199–223. https://doi.org/10.1080/19475705.2010.498151
Qiao S, Qin S, Chen J, et al. (2019) The Application of a Three-Dimensional Deterministic Model in the Study of Debris Flow Prediction Based on the Rainfall-Unstable Soil Coupling Mechanism. Processes 7(2): 99. https://doi.org/10.3390/pr7020099
Qin S, Lv J, Cao C, et al. (2019) Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study. Geomat. Nat Hazards Risk 10(1): 1648–1666. https://doi.org/10.1080/19475705.2019.1604572
Rahman MS, Ahmed B, Di L (2017) Landslide initiation and runout susceptibility modeling in the context of hill cutting and rapid urbanization: a combined approach of weights of evidence and spatial multi-criteria. J Mt Sci 14(10): 1919–1937. https://doi.org/10.1007/s11629-016-4220-z
Sahana M, Sajjad H (2017) Evaluating effectiveness of frequency ratio, fuzzy logic and logistic regression models in assessing landslide susceptibility: a case from Rudraprayag district, India. J Mt Sci 14(11): 2150–2167. https://doi.org/10.1007/s11629-017-4404-1
Schlogel R, Marchesini I, Alvioli M, et al. (2018) Optimizing landslide susceptibility zonation: Effects of DEM spatial resolution and slope unit delineation on logistic regression models. Geomorphology 301: 10–20. https://doi.org/10.1016/j.geomorph.2017.10.018
Shi M, Chen J, Song Y, et al. (2016) Assessing debris flow susceptibility in Heshigten Banner, Inner Mongolia, China, using principal component analysis and an improved fuzzy C-means algorithm. Bull Eng Geol Environ 75(3): 909–922. https://doi.org/10.1007/s10064-015-0784-z
Su F, Cui P, Zhang J, et al. (2010) Susceptibility assessment of landslides caused by the wenchuan earthquake using a logistic regression model. J Mt Sci 7(3):234–245. https://doi.org/10.1007/s11629-010-2015-1
Sun J, Qin S, Qiao S, et al. (2021) Exploring the impact of introducing a physical model into statistical methods on the evaluation of regional scale debris flow susceptibility. Nat Hazards 106(1):881–912. https://doi.org/10.1007/s11069-020-04498-4
Suzen ML, Kaya BS (2012) Evaluation of environmental parameters in logistic regression models for landslide susceptibility mapping. Int J Digit Earth 5(4): 338–355. https://doi.org/10.1080/17538947.2011.586443
Tehrany MS, Kumar L, Jebur MN, et al. (2019) Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods. Geomat Nat Hazards Risk 10(1): 79–101. https://doi.org/10.1080/19475705.2018.1506509
Van Den Eeckhaut M, Reichenbach P, Guzzetti F, et al. (2009). Combined landslide inventory and susceptibility assessment based on different mapping units: an example from the Flemish Ardennes, Belgium. Nat Hazards Earth Syst Sci 9(2): 507–521. https://doi.org/10.5194/nhess-9-507-2009
Van Westen CJ (2000) The modelling of landslide hazards using GIS. Surv Geophys 21(2–3): 241–255. https://doi.org/10.1023/A:1006794127521
Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview. Eng Geol 102(3–4SI): 112–131. https://doi.org/10.1016/j.enggeo.2008.03.010
Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30(3): 399–419. https://doi.org/10.1023/B:NHAZ.0000007097.42735.9e
Viet-Ha N, Janizadeh S, Avand M (2020) GIS-Based gully erosion susceptibility mapping: A comparison of computational ensemble data mining models. Appl Sci-Basel 10(6). https://doi.org/10.3390/app10062039
Wang J, Yu Y, Yang S, et al. (2014) A modified certainty coefficient method (M-CF) for debris flow susceptibility assessment: a case study for the meizoseismal areas hit by China Wenchuan earthquake. J Mt Sci 11(5): 1286–1297. https://doi.org/10.1007/s11629-013-2781-7
Wang Q, Wang Y, Niu R, et al. (2017) Integration of information theory, K-Means Cluster analysis and the logistic regression model for landslide susceptibility mapping in the Three Gorges area, China. Remote Sens 9(9389). https://doi.org/10.3390/rs9090938
Xu W, Yu W, Jing S, et al. (2013) Debris flow susceptibility assessment by GIS and information value model in a large-scale region, Sichuan Province (China). Nat Hazards 65(3): 1379–1392. https://doi.org/10.1007/s11069-012-0414-z
Yao J, Qin S, Qiao S, et al. (2020) Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China. Appl Sci-Basel 10(16): 5640. https://doi.org/10.3390/app10165640
Yesilnacar E, Suzen ML (2006) A land-cover classification for landslide susceptibility mapping by using feature components. Int J Remote Sens 27(2): 253–275. https://doi.org/10.1080/0143116050030042
Youssef AM, Pradhan B, Jebur MN, et al. (2015) Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia. Environ. Earth Sci 73(7): 3745–3761. https://doi.org/10.1007/s12665-014-3661-3
Zezere JL, Pereira S, Melo R, et al. (2017) Mapping landslide susceptibility using data-driven methods. Sci Total Environ 589: 250–267.https://doi.org/10.1016/j.scitotenv.2017.02.188
Zhang SJ, Wei FQ, Liu DL, et al. (2014) A regional-scale method of forecasting debris flow events based on water-soil coupling mechanism. J Mt Sci 11(6): 1531–1542. https://doi.org/10.1007/s11629-013-2906-z
Zhang TY, Han L, Zhang H, et al. (2019) GIS-based landslide susceptibility mapping using hybrid integration approaches of fractal dimension with index of entropy and support vector machine. J Mt Sci 16(6): 1275–1288. https://doi.org/10.1007/s11629-018-5337-z
Zhang TY, Mao ZA, Wang T (2020) GIS-based evaluation of landslide susceptibility using a novel hybrid computational intelligence model on different mapping units. J Mt Sci 17(12): 2929–2941. https://doi.org/10.1007/s11629-020-6393-8
Zhang W, Li H, Chen J, et al. (2011) Comprehensive hazard assessment and protection of debris flows along Jinsha River close to the Wudongde dam site in China. Nat Hazards 58(1): 459–477. https://doi.org/10.1007/s11069-010-9680-9
Zhang W, Chen J, Wang Q, et al. (2013) Susceptibility analysis of large-scale debris flows based on combination weighting and extension methods. Nat Hazards 66(2): 1073–1100. https://doi.org/10.1007/s11069-012-0539-0
Zini A, Grauso S, Verrubbi V, et al. (2015) The RUSLE erosion index as a proxy indicator for debris flow susceptibility. Landslides 12(5): 847–859. https://doi.org/10.1007/s10346-014-0515
Acknowledgment
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41977221 and 41202197), Jilin Provincial Science and Technology Department (No.20190303103SF, No.20170101001JC).
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Qiao, Ss., Qin, Sw., Sun, Jb. et al. Development of a region-partitioning method for debris flow susceptibility mapping. J. Mt. Sci. 18, 1177–1191 (2021). https://doi.org/10.1007/s11629-020-6497-1
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DOI: https://doi.org/10.1007/s11629-020-6497-1