Abstract
The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three sampling strategies studied were the centroid of the scarp area (COSA), the centroid of the flowing area (COFA), and the centroid of the accumulation area (COAA) of debris flow watersheds. An inventory consisting of 150 debris flow watersheds and 12 conditioning factors were prepared for research. Firstly, the information gain ratio (IGR) method was used to analyze the predictive ability of the conditioning factors. Subsequently, 12 conditioning factors were involved in the modeling of artificial neural network (ANN), random forest (RF) and support vector machine (SVM). Then, the receiver operating characteristic curves (ROC) and the area under curves (AUC) were used to evaluate the model performance. Finally, a scoring system was used to score the quality of the debris flow susceptibility maps. Samples obtained from the accumulation area have the strongest predictive ability and can make the models achieve the best performance. The AUC values corresponding to the best model performance on the validation dataset were 0.861, 0.804 and 0.856 for SVM, ANN and RF respectively. The sampling strategy of the centroid of the scarp area is optimal with the highest quality of debris flow susceptibility maps having scores of 373470, 393241 and 362485 for SVM, ANN and RF respectively.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Youssef AM, Pourghasemi HR, Pourtaghi ZS, et al. (2016) Erratum to: 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: 1315–1318. https://doi.org/10.1007/s10346-015-0667-1
Akgun A, Dag S, Bulut F (2008). Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ Geol 54(6): 1127–1143. https://doi.org/10.1007/s00254-007-0882-8
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
Bergstra J, Yamins D, Cox DD (2013) Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. Proc. 30th Int. Conf. Mach. Learn.
Bui DT, Ho TC, Pradhan B, et al. (2016a) 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: 22. https://doi.org/10.1007/s12665-016-5919-4
Bui DT, Tuan TA, Klempe H, et al. (2016b) 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 13:361–378. https://doi.org/10.1007/s10346-015-0557-6
Chen W, Xie XS, Peng JB, et al. (2017) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naive-Bayes tree, and alternating decision tree models. Geomat Nat Hazards Risk 8: 950–973. https://doi.org/10.1080/19475705.2017.1289250
Chen W, Xie XS, Peng JB, et al. (2018) GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method. Catena 164:135–149. https://doi.org/10.1016/j.catena.2018.01.012
Chen Y, Qin SW, Qiao SS, et al. (2020). Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China. Water 12(2079): 2079.https://doi.org/10.3390/w12082079
Clerici A, Perego S, Tellini C, et al. (2006). A GIS-based automated procedure for landslide susceptibility mapping by the conditional analysis method: the Baganzvalley case study (Italian Northern Apennines). Environ Geol 50(7): 941–961. https://doi.org/10.1007/s00254-006-0264-7
Carrara A, Crosta G, Frattini P (2008) Comparing models of debris-flow susceptibility in the alpine environment. Geomorphology 94: 353–378. https://doi.org/10.1016/i.geomorph.2006.10.033
Chang KT, Merghadi A, Yunus AP, et al. (2019) Evaluating scale effects of topographic variables in landslide susceptibility models using GIS-based machine learning techniques. Sci Rep 9: 12296. https://doi.org/10.1038/s41598-019-48773-2
Corominas J, Westen CV, Frattini P, et al. (2014). Recommendations for the quantitative analysis of landslide risk. Bull Eng Geol Environ 73(2): 209–263. https://doi.org/10.1007/s10064-013-0538-8
Dou J, Yunus AP, Xu Y, et al. (2019a). Torrential rainfall-triggered shallow landslide characteristics and susceptibility assessment using ensemble data-driven models in the Dongjiang Reservoir Watershed, China. Nat Hazards 97: 579–609. https://doi.org/10.1007/s11069-019-03659-4
Dou J, Yunus AP, Tien BD, et al. (2019b) Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan. Sci Total Environ 662: 332–346. https://doi.org/10.1016/j.scitotenv.2019.01.221
Dou J, Yunus AP, Merghadi A, et al. (2020a) Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning. Sci Total Environ. 720: 137320. https://doi.org/10.1016/j.scitotenv.2020.137320
Dou J, Yunus AP, Bui DT, et al. (2020b) Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan. Landslides 17:641–658. https://doi.org/10.1007/s10346-019-01286-5
Demir G, Aytekin M, Akgún A, et al. (2013). A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods. Nat Hazards 65(3): 1481–1506. https://doi.org/10.1007/s11069-012-0418-8
Fan XM, Scaringi G, Fan Y, et al. (2018) Two multi-temporal datasets to track the enhanced landsliding after the 2008 Wenchuan earthquake. Earth Syst Sci Data Discuss 2018: 1–29. https://doi.org/10.5281/zenodo.1405490
Hong H, Pradhan B, Jebur MN, et al. (2016a) Spatial prediction of landslide hazard at the Luxi area (China) using support vector machines. Environ Earth Sci 75: 40. https://doi.org/10.1007/s12665-015-4866-9
Hong H, Pourghasemi HR, Pourtaghi ZS (2016b) 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, Tsangaratos P, llia I, et al. (2017) Comparing the Performance of a Logistic Regression and a Random Forest Model in Landslide Susceptibility Assessments. the Case of Wuyaun Area, China. Advancing Culture of Living with Landslides. pp 1043–1050.
Khosravi K, Shahabi H, Pham BT, et al. (2019) A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods. Hydrol 573:311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073
Liang Z, Wang CM, Zhang ZM, et al. (2020a) 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
Liang Z, Wang CM, Han SL, et al. (2020b) Classification and susceptibility assessment of debris flow based on a semiquantitative method combination of the fuzzy C-means algorithm, factor analysis and efficacy coefficient. Nat Hazards Earth Syst Sci 20:1287–1304.https://doi.org/10.5194/nhess-20-1287-2020
Liu XQ (2019) Risk assessment of geological hazards in Beichuan Qiang Autonomous County. Master Thesis, Southwest Jiaotong University, Chengdu, Sichuan Province. p 8–13.
Lee S, Ryu JH, Won JS, et al. (2004). Determination and application of the weights for landslide susceptibility mapping using an artificial neural network. Eng Geol 71(3): 289–302. https://doi.org/10.1016/S0013-7952(03)00142-X
Merghadi A, Yunus AP, Dou J, et al. (2020) Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance. Earth-Sci Rev 207: 103225.https://doi.org/10.1016/j.earscirev.2020.103225
Nguyen MD, Pham BT, Tuyen T, et al. (2019) Development of an Artificial Intelligence Approach for Prediction of Consolidation Coefficient of Soft Soil: A Sensitivity Analysis. Open Constr Build Technol J 13: 178–188. https://doi.org/10.2174/187483680191301
Ohlmacher, GC (2007) Plan curvature and landslide probability in regions dominated by earth flows and earth slides. Eng Geol 91: 117–134. https://doi.org/10.1016/j.enggeo.2007.01.005
Oh HJ, Lee S (2017). Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree. Appl Sci 7(10): 1000. https://doi.org/10.3390/app7101000
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: 965–996. https://doi.org/10.1007/s11069-012-0217-2
Pham BT, Shirzadi A, Tien BD, et al. (2018). A hybrid machine learning ensemble approach based on a radial basis function neural network and rotation forest for landslide susceptibility modeling: A case study in the Himalayan area, India. Int J Sediment Res 33(2): 157–170. https://doi.org/10.1016/j.ijsrc.2017.09.008
Pham BT, Prakash I, Dou J, et al. (2019) A novel hybrid approach of landslide susceptibility modelling using rotation forest ensemble and different base classifiers. Geocarto Int 35: 1267–1292. https://doi.org/10.1080/10106049.2018.1559885
Pham BT, Nguyen TT, Qi CC, et al. (2020) Coupling RBF neural network with ensemble learning techniques for landslide susceptibility mapping. Catena 195: 104805. https://doi.org/10.1016/j.catena.2020.104805
Provost F, Fawcett T (2001) Robust classification for imprecise environments. Mach Learn. 42: 203–231. https://doi.org/10.1023/A:1007601015854
Pradhan AMS, Kim YT (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. https://doi.org/10.1007/s11069-014-1065-z
Qiao W, Li W, Li T, et al. (2017) Effects of coal mining on shallow water resources in Semiarid Regions: a case study in the Shennan mining area, Shaanxi, China. Mine Water Environ. 36:104–113. https://doi.org/10.1007/s10230-016-0414-4
Reichenbach P, Rossi M, Malamud B, et al. (2018) A review of statistically-based landslide susceptibility models. Earth Sci Rev 180: 60–9. https://doi.org/10.1016/j.earscirev.2018.03.001
Solaimani K, Mousavi SZ, Kavian, A (2013). Landslide susceptibility mapping based on frequency ratio and logistic regression models. Arab J Geosci 6(7): 2557–2569. https://doi.org/10.1007/s12517-012-0526-5
San BI (2014) An evaluation of SVM using polygon-based random sampling in landslide susceptibility mapping: the Candir catchment area (western Antalya, Turkey). Int J Appl Earth Obs Geoinf 26: 399–412. https://doi.org/10.1016/j.jag.2013.09.010
Tunusluoglu MC, Gokceoglu C, Sonmez H, et al. (2007). An artificial neural network application to produce debris source areas of Barla, Besparmak, and Kapi Mountains (NW Taurids, Turkey). Nat Hazards Earth Syst Sci. 7(5): 557–570. https://doi.org/10.5194/nhess-7-557-2007
Thiery Y, Malet JP, Sterlacchini S, et al. (2007). Landslide susceptibility assessment by bivariate methods at large scales: application to a complex mountainous environment. Geomorphology 92(1–2): 38–59 https://doi.org/10.1016/j.geomorph.2007.02.020
Tien BD, Shirzadi A, Shahabi H, et al. (2019) New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed. Forests 10: 743. https://doi.org/10.3390/f10090743
Van Den Eeckhaut M, Vanwalleghem T, Poesen J, et al. (2006) Prediction of landslide susceptibility using rare events logistic regression: a case-study in the Flemish Ardennes (Belgium). Geomorphology 76(3–4), 392–410. https://doi.org/10.1016/j.geomorph.2005.12.003
Wang LJ, Guo M, Sawada K, et al. (2016) A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network. Geosci I 20: 117–136. https://doi.org/10.1007/s12303-015-0026-1
Xiong K, Adhikari BR, Stamatopoulos CA, et al. (2020). Comparison of Different Machine Learning Methods for Debris Flow Susceptibility Mapping: A Case Study in the Sichuan Province, China. Remote Sens 12(2): 295. https://doi.org/10.3390/rs12020295
Xu C, Dai F, Xu X, et al. (2012) GIS-based support vector ma chine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145:70–80. https://doi.org/10.1016/j.geomorph.2011.12.040
Yilmaz I (2010). The effect of the sampling strategies on the landslide susceptibility mapping by conditional probability and artificial neural networks. Environ Earth Sci 60(3): 505–519. https://doi.org/10.1007/s12665-009-0191-5
Youssef AM, Pourghasemi HR, Pourtaghi ZS, et al. (2016) 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 13:839–856. https://doi.org/10.1007/s10346-015-0614-1
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
Yunus AP, Dou J, Song X, et al. (2019) Improved Bathymetric Mapping of Coastal and Lake Environments Using Sentinel-2 and Landsat-8 Images. Sensors 19: 2788. https://doi.org/10.3390/s19122788
Zhang YH, Ge TT, Tian W, et al. (2019). Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China. Remote Sens 11(23): 2801. https://doi.org/10.3390/rs11232801
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant no. 41972267 and no. 41572257) and Graduate Innovation Fund of Jilin University (Grant no. 101832020CX232).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gao, Ry., Wang, Cm. & Liang, Z. Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds. J. Mt. Sci. 18, 1476–1488 (2021). https://doi.org/10.1007/s11629-020-6471-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11629-020-6471-y