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Comparison of wave-cluster and DBSCAN algorithms for landslide susceptibility assessment

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Abstract

The aim of this study is to assess and compare two clustering algorithms, namely, the Wave-cluster and Density Based Spatial Clustering of Application with Noise (DBSCAN) algorithms, for landslide susceptibility assessment in Baota District, China. Based on historical reports, interpretation of aerial photographs and field survey reports, 293 were identified in the study area. Seven factors associated with landslide occurrence were prepared and used as inputs into the models for landslide susceptibility modelling. The models divided the study area grids into 478 and 465 subclasses, respectively, which were further grouped into five susceptibility levels using K-means algorithm, based on the landslide density values of the obtained subclasses. The resulting two models were validated and compared using area under the receiver operating characteristics (AUC-ROC) curve and statistical measures including sensitivity, specificity and accuracy. The analysis demonstrated that the Wave-cluster model outperformed the DBSCAN model with sensitivity, specificity and accuracy values of 88.05%, 89.2%, and 88.54%, respectively, while DBSCAN had sensitivity, specificity and accuracy values of 86.69%, 86.38%, and 86.56% respectively. In addition, the Wave-cluster model had higher value AUC of 0.823 than the DBSCAN model (0.808). This indicates that, the landslide susceptibility maps (LSMs) generated in this study had good performance for assessing landslide susceptibility in the study area and can be useful tools in assessing landslides, as the foundation for preventing and reducing landslide consequences for suitable social and economic development.

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References

  • Alimohammadlou Y, Najafi A, Gokceoglu C (2014) Estimation of rainfall-induced landslides using ANN and Fuzzy clustering methods: a case study in Saeen Slope, Azerbaijan province, Iran. CATENA 120:149–162

    Article  Google Scholar 

  • Ballabio C, Sterlacchini S (2012) Support vector machines for landslide susceptibility mapping: the Staffora River basin case study, Italy. Math Geosci 44:47–70

    Article  Google Scholar 

  • Caracciolo D, Arnone E, Conti L, Noto V (2017) Exploiting historical rainfall and landslide data in a spatial database for the derivation of critical rainfall thresholds. Environ Earth Sci 76:222

    Article  Google Scholar 

  • Chen XZ, Chen H, You Y (2016) Weights-of-evidence method based on GIS for assessing susceptibility to debris flows in Kangding County, Sichuan Province, China. Environ Earth Sci 75:70–88

    Article  Google Scholar 

  • Chen W, Shirzadi A, Shahabi H, Ahmad B, Zhang S, Hong H, Zhang N (2017) A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naïve Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China. Geomat Nat Hazards 8:1955–1977

    Article  Google Scholar 

  • Cruden DM (1991) A simple definition of a landslide. Bull Int Assoc Eng Geol 43:27–29

    Article  Google Scholar 

  • Dino B, David M, William E, Jim M, Taylor J, Erik S, Brian K (2015) A spectral clustering search algorithm for predicting shallow landslide size and location. J Geophys Res Earth Surf 120(2):300–324

    Article  Google Scholar 

  • Feizizadeh B, Roodposhti MS, Blaschke T, Aryal J (2017) Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping. Arab J Geosci 10:122

    Article  Google Scholar 

  • Frattini P, Crosta G, Carrara A (2010) Techniques for evaluating the performance of landslide susceptibility models. Eng Geol 111:62–72

    Article  Google Scholar 

  • Halkidi M, Batistakis Y, Vazirgiannis M (2001) Clustering validation techniques. J Intell Inf Syst 17:107–145

    Article  Google Scholar 

  • He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, Huiyuan C, Ma J, Chen Y, Wang X, Chapi K, Ahmad B (2019) Landslide spatial modeling using novel bivariate statistical based NaïveBayes, RBF Classifier, and RBF Network machine learning algorithms. Sci Total Environ 663:1–15

    Article  Google Scholar 

  • Hong H, Liu J, Tien D, Pradhan B, Acharya T, Phamh B, Zhu A, Chen W, Ahmad B (2018) Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China). CATENA 163:399–413

    Article  Google Scholar 

  • Huang Y, Zhao L (2018) Review on landslide susceptibility mapping using support vector machines. CATENA 165:520–529

    Article  Google Scholar 

  • Mallat S (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11:674–693

    Article  Google Scholar 

  • Mao Y, Zhang M (2015) Landslide hazards mapping using uncertain Naïve Bayesian classification method. J Cent South Univ 22:3512–3520

    Article  Google Scholar 

  • Mao Y, Zhang M, Sun P, Wang G (2017) Landslide susceptibility assessment using uncertain decision tree model in Loess areas. Environ Earth Sci 76:752–770

    Article  Google Scholar 

  • Melchiorre M, Matteucci A, Azzoni A, Zanchi A (2008) Artificial neural networks and cluster analysis in landslide susceptibility zonation. Geomorphology 94:379–400

    Article  Google Scholar 

  • Pham T, Bui T, Dholakia B, Prakash I, Pham V (2016) A comparative study of least square support vector machine and multiclass alternating decision trees for spatial prediction of rainfall-induced landslides in a tropical cyclones area. Geotech Geol Eng 34:1807–1824

    Article  Google Scholar 

  • Pham T, Prakash I, Bui T (2018) Spatial prediction of landslides using a hybrid machine learning approach based on random subspace and classification and regression trees. Geomorphology 303:256–270

    Article  Google Scholar 

  • Pokharel B, Omar FA, Ali A, Sang-Wan K, Samsung L, Hyuck-Jin P (2020) Spatial clustering and modelling for landslide susceptibility mapping in the north of the Kathmandu Valley, Nepal. Landslides 18:1403–1419. https://doi.org/10.1007/s10346-020-01558-5

    Article  Google Scholar 

  • Romeo S, Matteo D, Melelli L, Cencetti C, Dragoni W, Fredduzzi A (2017) Seismic-induced rock falls and landslide dam following the October 30, 2016 earthquake in Central Italy. Landslide 14:1457–1465

    Article  Google Scholar 

  • Roodposhti MS, Safarrad T, Shahabi H (2017) Drought sensitivity mapping using two one-class support vector machine algorithms. Atmos Res 193:73–82

    Article  Google Scholar 

  • Sheikholeslami G, Chatterjee S, Zhang A (2000) Wave-cluster: a multi-resolution clustering approach for very large spatial databases. VLDB J 8:289–304

    Article  Google Scholar 

  • 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 Env 75:909–922

    Article  Google Scholar 

  • Shirzadi A, Bui T, Pham T, Solaimani K, Chapi K, Kavian A, Shahabi H, Revhaug I (2017) Shallow landslide susceptibility assessment using a novel hybrid intelligence approach. Environ Earth Sci 76:60

    Article  Google Scholar 

  • Tang RX, Kulatilake PHSW, Yan EC, Jing SC (2020) Evaluating landslide susceptibility based on cluster analysis, probabilistic methods, and artificial neural networks. Bull Eng Geol Env 79:2235–2254

    Article  Google Scholar 

  • TsangaratosIlia PI (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

    Article  Google Scholar 

  • Wan S (2013) Entropy-based particle swarm optimization with clustering analysis on landslide susceptibility mapping. Environ Earth Sci 68:1349–1366

    Article  Google Scholar 

  • Wan S (2015) Construction of knowledge-based spatial decision support system for landslide mapping using fuzzy clustering and KPSO analysis. Arab J Geosci 8:1041–1055

    Article  Google Scholar 

  • Wang Q, Wang Y, Niu Q (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 9:938

    Article  Google Scholar 

  • Wu H, Song T (2018) An evaluation of landslide susceptibility using probability statistic modeling and GIS’s spatial clustering analysis. Hum Ecol Risk Assess Int J 24(7):1952–1968

    Article  Google Scholar 

  • Xu C, Xu X (2012) Landslide hazard mapping using GIS and weight of evidence model in Qingshui river watershed of 2008 Wenchuan earthquake struck the region. J Earth Sci 23:97–120

    Article  Google Scholar 

  • Xu L, Dai FC, Tham LG, Zhou YF, Wu CX (2012) Investigating landslide-related cracks along the edge of two loess platforms in northwest China. Earth Surf Process Landf 37:1023–1033

    Article  Google Scholar 

  • Xu H, Ma C, Lian J, Xu K, Chaima E (2018) Urban flooding risk assessment based on an integrated k-means cluster algorithm and improved entropy weight method in the region of Haikou, China. J Hydrol 563:975–986

    Article  Google Scholar 

  • Yu D, Sheikholeslami G, Zhang A (2002) Finding outliers in very large datasets. Knowl Inf Syst 4:387–412

    Article  Google Scholar 

  • Yu C, Chen C, Lin L, Chen Y, Yu H (2006) Landslides and Rainfall characteristics analysis in Taipei City during the Typhoon Nari Event. Nat Hazards 37:153–167

    Article  Google Scholar 

  • Zhang S, Liu J (2010) Controlling factors of loess landslide in Western China. Environ Earth Sci 59:1671–1680

    Article  Google Scholar 

  • Zhang F, Kang C, Chan D, Zhang X, Pei X, Peng J (2017) A study of a flow-slide with significant entrainment in loess areas in China. Earth Surf Process Landf 42(14):2295–2305

    Article  Google Scholar 

  • Zhao Y, Li Y, Zhang L, Wang Q (2016) Ground water level prediction of landslide based on classification and regression tree. Geod Geodyn 5(7):348–355

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Natural Science Foundation of China (41562019), the National Key Research and Development Projects of China (2018YFC1504705) and the National Natural Science Foundation of China (41530640, 41471001).

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Authors and Affiliations

  1. Jiangxi University of Science and Technology of Information Engineering, Ganzhou, 341000, Jiangxi, China

    Yiming Mao, Deborah Simon Mwakapesa, Kaibin Xu, Chen Lei & Youcun Liu

  2. Key Laboratory for Geo-Hazards in Loess Area, MLR, 438 E. Youyi Road, Xi’an, 710054, Shaanxi, China

    Yiming Mao & Maosheng Zhang

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  1. Yiming Mao
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  2. Deborah Simon Mwakapesa
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  3. Kaibin Xu
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  4. Chen Lei
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  5. Youcun Liu
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Correspondence to Maosheng Zhang.

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Mao, Y., Mwakapesa, D.S., Xu, K. et al. Comparison of wave-cluster and DBSCAN algorithms for landslide susceptibility assessment. Environ Earth Sci 80, 734 (2021). https://doi.org/10.1007/s12665-021-09896-w

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