Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan (original) (raw)

Abstract

Heavy rainfall in mountainous terrain can trigger numerous landslides in hill slopes. These landslides can be deadly to the community living downslope with their fast pace, turning failures into catastrophic debris flows and avalanches. Active tectonics coupled with rugged topography in a complex geoenvironment multiplies this likelihood. The available hazard maps are usually helpful in mitigating disasters. However, fool-proof predicting landslide susceptibility identification remains a challenge in landslide discipline. Recently, ensemble machine learning (ML) techniques have proved the potential to provide a more accurate and efficient solution in spatial modeling. The main purposes of the current study are to examine and evaluate the predictive capability of support vector machine hybrid ensemble ML algorithms, i.e., the bagging, boosting, and stacking for modeling the catastrophic rainfall-induced landslide occurrences in the Northern parts of Kyushu Island, at the watershed scale in Japan. In this study, a landslide inventory map containing 265 landslide polygons was first interpreted from the aerial photographs and fieldwork after the September 2017 rainfall event. The raw data were randomly separated into two parts using a 70/30 sampling strategy for training and validating the landslide models. Then, 13 predisposing factors were prepared as predictors and dependent variable. The landslide susceptibility maps (LSM) were validated by the area under the receiver operating characteristic curve (AUC). The results of validation showed that the AUC values of the four models (SVM-Stacking, SVM, SVM-Bagging, and SVM-Boosting) varied from 0.74 to 0.91. The SVM-boosting model outperformed the other models, while SVM-stacking model has found to be the lowest performance. The outcome suggests that an ensemble ML model does not necessarily mean good performance. It is always preferable to select an appropriate model, such as the one proposed the hybrid novel ensemble SVM-boosting model, which could significantly improve the accuracies of LSM. Also, from Information Gain Ratio (IGR) we found that the rainfall factor mainly affects the results, that agrees with the analogy of present study.

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References

• Alves A (2017) Stacking machine learning classifiers to identify Higgs bosons at the LHC. J Instrum. https://doi.org/10.1088/1748-0221/12/05/T05005
  Article Google Scholar
• Beasley TM, Zumbo BD (2003) Comparison of aligned Friedman rank and parametric methods for testing interactions in split-plot designs. Comput Stat Data Anal. https://doi.org/10.1016/S0167-9473(02)00147-0
  Article Google Scholar
• Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127. https://doi.org/10.1561/2200000006
  Article Google Scholar
• Brabb EE (1984) Innovative approaches to landslide hazard mapping. Proc. 4th Int. Symp. Landslides 1:307–324
  Google Scholar
• Breiman L (1996) Bagging predictors. Mach Learn. https://doi.org/10.1007/BF00058655
  Google Scholar
• Bui DT, Pradhan B, Lofman O, Revhaug I (2012) Landslide susceptibility assessment in Vietnam using support vector machines, decision tree, and Naïve Bayes models. https://doi.org/10.1155/2012/974638
  Article Google Scholar
• Camilo DC, Lombardo L, Mai PM et al (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized generalized linear model. Environ Model Softw 97:145–156. https://doi.org/10.1016/j.envsoft.2017.08.003
  Article Google Scholar
• Chang KT, Dou J, Chang Y et al (2016) Spatial resolution effects of digital terrain models on landslide susceptibility analysis. ISPRS Int Arch Photogramm Remote Sens Spat Inf Sci XLI-B8:33–36. https://doi.org/10.5194/isprs-archives-XLI-B8-33-2016
  Article Google Scholar
• Chang K-T, 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
• Chen W, Panahi M, Pourghasemi HR (2017a) 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
  Article Google Scholar
• Chen W, Xie X, Peng J et al (2017b) GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models. Geomat Nat Hazards Risk 8:950–973. https://doi.org/10.1080/19475705.2017.1289250
  Article Google Scholar
• Choubin B, Moradi E, Golshan M et al (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064
  Article Google Scholar
• Dietterich TG (2000) Ensemble methods in machine learning, pp 1–15
  Google Scholar
• Dormann CF, Elith J, Bacher S et al (2012) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop) 36:27–46. https://doi.org/10.1111/j.1600-0587.2012.07348.x
  Article Google Scholar
• Dou J, Bui DT, Yunus AP et al (2015a) Optimization of causative factors for landslide susceptibility evaluation using remote sensing and GIS data in parts of Niigata, Japan. PLoS One 10:e0133262. https://doi.org/10.1371/journal.pone.0133262
  Article Google Scholar
• Dou J, Chang KT, Chen S et al (2015b) Automatic case-based reasoning approach for landslide detection: integration of object-oriented image analysis and a genetic algorithm. Remote Sens 7:4318–4342. https://doi.org/10.3390/rs70404318
  Article Google Scholar
• Dou J, Li X, Yunus AP, Paudel U, Chang KT, Zhu Z, Pourghasemi HR (2015c) Automatic detection of sinkhole collapses at finer resolutions using a multi-component remote sensing approach. Nat Hazards 78:1021–1044. https://doi.org/10.1007/s11069-015-1756-0
  Article Google Scholar
• Dou J, Paudel U, Oguchi T et al (2015d) Shallow and deep-seated landslide differentiation using support vector machines: a case study of the Chuetsu Area, Japan. Terr Atmos Ocean Sci 26:227. https://doi.org/10.3319/TAO.2014.12.02.07(EOSI)
  Article Google Scholar
• Dou J, Yamagishi H, Pourghasemi HR et al (2015e) An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan. Nat Hazards 78:1749–1776. https://doi.org/10.1007/s11069-015-1799-2
  Article Google Scholar
• Dou J, Yamagishi H, Xu Y et al (2017) Characteristics of the torrential rainfall-induced shallow landslides by Typhoon Bilis, in July 2006, using remote sensing and GIS. In: Yamagishi H, Bhandary NP (eds) GIS landslide. Springer, Japan, pp 221–230
  Chapter Google Scholar
• Dou J, Yamagishi H, Zhu Z et al (2018) TXT-tool 1.081-6.1 A comparative study of the binary logistic regression (BLR) and artificial neural network (ANN) models for GIS-based spatial predicting landslides at a regional scale. In: Landslide dynamics: ISDR-ICL landslide interactive teaching tools. Springer, Cham, pp 139–151
  Chapter Google Scholar
• Dou J, Yunus AP, Tien Bui D et al (2019a) 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
  Article Google Scholar
• Dou J, Yunus AP, Tien Bui D et al (2019b) Evaluating GIS-based multiple statistical models and data mining for earthquake and rainfall-induced landslide susceptibility using the LiDAR DEM. Remote Sens 11:638. https://doi.org/10.3390/rs11060638
  Article Google Scholar
• Fagerland MW, Sandvik L (2009) The Wilcoxon-Mann-Whitney test under scrutiny. Stat Med. https://doi.org/10.1002/sim.3561
  Article Google Scholar
• Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc. https://doi.org/10.1080/01621459.1937.10503522
  Article Google Scholar
• Ganjisaffar Y, Caruana R, Lopes CV (2011) Bagging gradient-boosted trees for high precision, low variance ranking models. In: Proceedings of the 34th International ACM SIGIR Conference on Research and development in Information - SIGIR ‘11
• Guzzetti F (1999) Landslide cartography, hazard assessment and risk evaluation: overview, limits and prospective, p 1–12
• Healey SP, Cohen WB, Yang Z et al (2018) Mapping forest change using stacked generalization: an ensemble approach. Remote Sens Environ. https://doi.org/10.1016/j.rse.2017.09.029
  Article Google Scholar
• Hengl T, Mendes de Jesus J, Heuvelink GBM et al (2017) SoilGrids250m: global gridded soil information based on machine learning. PLoS One 12:e0169748. https://doi.org/10.1371/journal.pone.0169748
  Article Google Scholar
• Holec J, Bednarik M, Sabo M et al (2013) A small-scale landslide susceptibility assessment for the territory of Western Carpathians. Nat Hazards 69:1081–1107
  Article Google Scholar
• Hong H, Pradhan B, Xu C, Tien Bui D (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
  Article Google Scholar
• Jenness J (2006) Topographic position index (tpi_jen.avx) extension for ArcView3.x,version 1.3a. In: Jenness Enterp
• Kanda T, Takata Y, Kohyama K, et al (2018) New soil maps of Japan based on the comprehensive soil classification system of Japan - first approximation and its application to the world reference base for soil resources 2006. Jpn Agric Res Q
• Kannan SS, Ramaraj N (2010) A novel hybrid feature selection via symmetrical uncertainty ranking based local memetic search algorithm. Knowl Based Syst. https://doi.org/10.1016/j.knosys.2010.03.016
  Article Google Scholar
• Khosravi K, Shahabi H, Thai B et al (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073
  Article Google Scholar
• Kokusaki Kogyo (2007) Aerial photo interpretation of earthquake damage from the 2007 Niigata Chuetsu–oki Earthquake consultant report, Kokusai Kogyo Co., Ltd, Japan. http://www.kkc.co.jp/social/disaster/200707_nigata/parts_tuika/gaikyozu.pdf. (in Japanese), Accessed 1 May 2008
• Le LT, Nguyen H, Dou J, Zhou J (2019a) A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning. Appl Sci 9:2630. https://doi.org/10.3390/app9132630
  Article Google Scholar
• Le LT, Nguyen H, Jian Z, Dou J (2019b) Estimating the heating load of energy efficiency of buildings for smart city planning using a novel artificial intelligence technique PSO-XGBoost. Appl Sci
• Lee S, Min K (2001) Statistical analysis of landslide susceptibility at Yongin, Korea. Environ Geol 40:1095–1113. https://doi.org/10.1007/s002540100310
  Article Google Scholar
• Merghadi A, Abderrahmane B, Tien Bui D (2018) Landslide susceptibility assessment at Mila Basin (Algeria): a comparative assessment of prediction capability of advanced machine learning methods. ISPRS Int J Geo-Inf 7. https://doi.org/10.3390/ijgi7070268
  Article Google Scholar
• Michel GP, Kobiyama M, Goerl RF (2014) Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil. J Soils Sediments 14:1266–1277. https://doi.org/10.1007/s11368-014-0886-4
  Article Google Scholar
• National Research Institute for Earth Science and Disaster Resilience (NIED). Available online: http://www.bosai.go.jp/mizu/dosha.html. (accessed on 10 Jan 2019)
• Nguyen QK, Tien Bui D, Hoang ND, Trinh P, Nguyen VH, Yilmaz I (2017) A novel hybrid approach based on instance based learning classifier and rotation forest ensemble for spatial prediction of rainfall-induced shallow landslides using GIS. Sustainability 9(5):813. https://doi.org/10.3390/su9050813
  Article Google Scholar
• O’Brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41:673–690. https://doi.org/10.1007/s11135-006-9018-6
  Article Google Scholar
• Oliveira SC, Zêzere JL, Lajas S, Melo R (2017) Combination of statistical and physically based methods to assess shallow slide susceptibility at the basin scale. Nat Hazards Earth Syst Sci 17:1091–1109. https://doi.org/10.5194/nhess-17-1091-2017
  Article Google Scholar
• Peters A, Hothorn T, Lausen B (2002) ipred: improved predictors. R News
• Pham BT, Pradhan B, Tien Bui D et al (2016) A comparative study of different machine learning methods for landslide susceptibility assessment: a case study of Uttarakhand area (India). Environ Model Softw 84:240–250. https://doi.org/10.1016/j.envsoft.2016.07.005
  Article Google Scholar
• Pham BT, Tien Bui D, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena. https://doi.org/10.1016/j.catena.2016.09.007
  Article Google Scholar
• Pham TB, Prakash I, Dou J et al (2018) A novel hybrid approach of landslide susceptibility modeling using rotation forest ensemble and different base classifiers. Geocarto Int 0:1–38. https://doi.org/10.1080/10106049.2018.1559885
• 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
  Article Google Scholar
• Pourghasemi HR, Jirandeh AG, Biswajeet P et al (2013) Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran. J Earth Syst Sci 122:349–369
  Article Google Scholar
• Pradhan B, Sezer EA, Gokceoglu C, Buchroithner MF (2010) Landslide susceptibility mapping by neuro-fuzzy approach in a landslide-prone area (Cameron Highlands, Malaysia). IEEE Trans Geosci Remote Sens 48:4164–4177. https://doi.org/10.1109/TGRS.2010.2050328
  Article Google Scholar
• Quinlan JR (1996) Bagging, boosting, and C4. 5. In: AAAI/IAAI, Vol. 1
• Ray RL, Jacobs JM, de Alba P (2010) Impacts of unsaturated zone soil moisture and groundwater table on slope instability. J Geotech Geoenviron Eng. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000357
  Article Google Scholar
• Saito H, Nakayama D, Matsuyama H (2010) Two types of rainfall conditions associated with shallow landslide initiation in Japan as revealed by normalized soil water Index. Sola. https://doi.org/10.2151/sola.2010-015
  Article Google Scholar
• Saito H, Uchiyama S, Hayakawa YS, Obanawa H (2018) Landslides triggered by an earthquake and heavy rainfalls at Aso volcano, Japan, detected by UAS and SfM-MVS photogrammetry. Prog Earth Planet Sci 5:1–10. https://doi.org/10.1186/s40645-018-0169-6
  Article Google Scholar
• Sekiguchi T, Sato HP (2006) Feature and distribution of landslides induced by the Mid Niigata Prefecture Earthquake in 2004, Japan. J Jpn Landslide Soc 43:142–154. https://doi.org/10.3313/jls.43.142
  Article Google Scholar
• Shaikhina T, Lowe D, Daga S et al (2017) Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control:1–7. https://doi.org/10.1016/j.bspc.2017.01.012
  Article Google Scholar
• Shirzadi A, Soliamani K, Habibnejhad M et al (2018) Novel GIS based machine learning algorithms for shallow landslide susceptibility mapping. Sensors 18:3777. https://doi.org/10.3390/s18113777
  Article Google Scholar
• Tien Bui D, Pradhan B, Lofman O, Revhaug I (2012, 2012) Landslide susceptibility assessment in vietnam using support vector machines, decision tree, and nave bayes models. Math Probl Eng. https://doi.org/10.1155/2012/974638
  Article Google Scholar
• Tien Bui D, Ho T-C, Pradhan B et al (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 75:1101. https://doi.org/10.1007/s12665-016-5919-4
  Article Google Scholar
• van Asch TWJ, Malet J-P, van Beek LPH, Amitrano D (2007) Techniques, issues and advances in numerical modelling of landslide hazard. Bull Soc Geol Fr 178:65–88. https://doi.org/10.2113/gssgfbull.178.2.65
  Article Google Scholar
• Vapnik VN (1998) Statistical learning theory (Adaptive and learning systems for signal processing, communications and control series). Wiley-Interscience
• Wartman J, Dunham L, Tiwari B, Pradel D (2013). Landslides in eastern Honshu induced by the 2011 Tohoku earthquake. Bulletin of the Seismological Society of America, 103(2B), 1503–1521
  Article Google Scholar
• Wolpert D (1992) Stacked generalization (stacking). Neural Netw. https://doi.org/10.1016/S0893-6080(05)80023-1
  Article Google Scholar
• Yagi H, Sato G, Higaki D et al (2009) Distribution and characteristics of landslides induced by the Iwate-Miyagi Nairiku Earthquake in 2008 in Tohoku District, Northeast Japan. Landslides. https://doi.org/10.1007/s10346-009-0182-3
  Article Google Scholar
• Yalcin A (2008) GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey): comparisons of results and confirmations. Catena 72:1–12
  Article Google Scholar
• Yamagishi H, Iwahashi J (2007) Comparison between the two triggered landslides in Mid-Niigata, Japan by July 13 heavy rainfall and October 23 intensive earthquakes in 2004. Landslides 4:389–397. https://doi.org/10.1007/s10346-007-0093-0
  Article Google Scholar
• Yamagishi H, Yamazaki F (2018) Landslides by the 2018 Hokkaido Iburi-Tobu Earthquake on September 6. Landslides 15:2521–2524. https://doi.org/10.1007/s10346-018-1092-z
  Article Google Scholar
• Yan J, Han S (2018) Classifying imbalanced data sets by a novel RE-sample and cost-sensitive stacked generalization method. Math Probl Eng. https://doi.org/10.1155/2018/5036710
  Google Scholar
• Youssef AM, Pourghasemi HR, Pourtaghi ZS, Al-Katheeri MM (2015) 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. https://doi.org/10.1007/s10346-015-0614-1
  Article Google Scholar
• Yunus AP, Dou J, Song X, Avtar R (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
  Article Google Scholar
• Zêzere JL, Pereira S, Melo R et al (2017) Science of the total environment mapping landslide susceptibility using data-driven methods. Sci Total Environ 589:250–267. https://doi.org/10.1016/j.scitotenv.2017.02.188
  Article Google Scholar
• Zhou Z-H (2014) Ensemble methods. In: Combining pattern classifiers. Wiley, Hoboken, pp 186–229
  Google Scholar

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Acknowledgments

We would like to thank GSI, JMA, and GSJ for providing the research data. We sincerely thank the anonymous reviewers and editor for improving the quality of our manuscript.

Funding

This study was financially supported by CAS Pioneer Hundred Talents Program, the National Key R&D Program of China (Grant No. 2018YFC1505401, Z. Han), the National Natural Science Foundation of China (Grant No. 41702310, Z. Han), and the Natural Science Foundation of Hunan (Grant No. 2018JJ3644, Z. Han). Also, this work was supported by the National Nature Science Foundation of China (Grant Nos. 51679127 and 51439003) and the National Key R&D Program of China (ID: 2018YFC1504803). This research is partially supported by Japan Society for the Promotion of Science.

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Authors and Affiliations

1. Department of Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata, 940-2188, Japan
   Jie Dou
2. State Key Laboratory of Geo-hazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, 610059, China
   Ali P. Yunus
3. GIS Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, 3800, Bø i Telemark, Norway
   Dieu Tien Bui
4. Research Laboratory of Sedimentary Environment, Mineral and Water resources of Eastern Algeria, Larbi Tebessi University, Tebessa, Algeria
   Abdelaziz Merghadi
5. Department of Geography, Faculty of Natural Science, Jamia Millia Islamia, New Delhi, 110025, India
   Mehebub Sahana
6. College of Water Sciences, Beijing Normal University, Xinjiekouwai Street 19, Beijing, 100875, China
   Zhongfan Zhu
7. National Science and Technology Center for Disaster Reduction, No. 200, Sec. 3, Beixin Road, Xindian District, New Taipei City, Taiwan
   Chi-Wen Chen
8. School of Civil Engineering, Central South University, Changsha, 410075, China
   Zheng Han
9. State Key Laboratory of Geohazard Prevention and Geo-environment Protection, Chengdu University of Technology, Chengdu, 610059, China
   Zheng Han
10. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
    Binh Thai Pham

Authors

1. Jie Dou
2. Ali P. Yunus
3. Dieu Tien Bui
4. Abdelaziz Merghadi
5. Mehebub Sahana
6. Zhongfan Zhu
7. Chi-Wen Chen
8. Zheng Han
9. Binh Thai Pham

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Correspondence toJie Dou, Zheng Han or Binh Thai Pham.

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Dou, J., Yunus, A.P., Bui, D.T. et al. 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 (2020). https://doi.org/10.1007/s10346-019-01286-5

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• Received: 11 February 2019
• Accepted: 13 September 2019
• Published: 25 October 2019
• Version of record: 25 October 2019
• Issue date: March 2020
• DOI: https://doi.org/10.1007/s10346-019-01286-5

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