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

Mao, Y.; Mwakapesa, Deborah Simon; Xu, Kaibin; Lei, Chen; Liu, Youcun; Zhang, Maosheng

doi:10.1007/s12665-021-09896-w

Cited by 8 publications

(9 citation statements)

References 39 publications

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“…The landslides are of rotational and translational types with most of them being rotational landslides (Mao et al, 2017). Moreover, in terms of size, the landslides are reported to be large Frontiers in Environmental Science frontiersin.org (ranging between 10 2 × 10 4 and 10 3 × 10 4 m 3 ), medium (between 10 1 × 10 4 and 10 2 × 10 4 m 3 ), and small sizes (less than 10 1 × 10 4 m 3 ); medium-sized landslides are more (about 52.6%) (Mao et al, 2021b).…”

Section: Landslide Databasementioning

confidence: 99%

“…There is no standard for the selection of these factors, and different studies select their factors based on various criteria. Based on previous research studies in Baota District (Hu et al, 2019;Mao et al, 2021a;Mao et al, 2021b), we selected 7 landslideconditioning factors for modeling: elevation, slope angle, slope aspect, profile curvature, lithology, vegetation coverage index (NDVI), and rainfall. The elevation factor is associated with landslide occurrences, especially in plateau areas (Lee et al, 2018).…”

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

“…Over decades, these methods have been widely used in other fields such as marketing research, pattern recognition, and image processing, but very rarely explored in LSM studies (Huang et al, 2020;Su et al, 2022). In recent years, making use of the advantages of these methods, some landslide researchers have also shown interest and conducted LSM studies using these methods (Wan et al, 2015;Wang et al, 2017;Hu et al, 2019;Mao et al, 2021a;Mao et al, 2021b;Hu et al, 2021;Pokharel et al, 2021;Yimin et al, 2021;Mao et al, 2022). From the analysis of these studies and other traditional clustering algorithms, some limitations were observed: the inability to detect subclasses with arbitrary shapes, sensitivity to noise, inability to perform well in large study areas with large datasets, and principally a standard method to process the uncertain data (rainfall) has not being obtained yet.…”

Section: Introductionmentioning

confidence: 99%

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Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Mwakapesa

Lan²,

Nanehkaran³

et al. 2023

Front. Environ. Sci.

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Landslide susceptibility mapping (LSM) is a crucial step during landslide assessment and environmental management. Clustering algorithms can construct effective models for LSM. However, a random selection of important parameters, inconsideration of uncertain data, noise data, and large datasets can limit the implementation of clustering in LSM, resulting in low and unreliable performance results. Thus, to address these problems, this study proposed an optimized clustering algorithm named O-CURE, which combines: the traditional Clustering Using REpresentatives algorithm (CURE), that is, efficient for large datasets and noise data, the partition influence weight (PIW)-based method to enhance the selection of sample sets and the city block distance (CIBD) for processing of the uncertain data in CURE clustering during LSM modeling. A database containing 293 landslide location samples, 213 non-landslide samples, and 7 landslide conditioning factors was prepared for the implementation and evaluation of the method. Also, a Multicollinearity analysis was conducted to select the most appropriate factors, and all the factors were acceptable for modeling. Based on O-CURE, landslide density, and the partitioning around medoids (PAM) algorithm a susceptibility map was constructed and classified into very high (33%), high (18%), moderate (24%), low (13%), and very low (12%) landslide susceptible levels. To evaluate the performance of the O-CURE model, five statistic metrics including accuracy, sensitivity, specificity, kappa, and AUC were applied. The analysis shows that O-CURE obtained accuracy = .9368, sensitivity = .9215, specificity = .9577, kappa = .8496, and AUC = .896 is an indication of high-performance capability. Also, the proposed method was compared with the CURE algorithm, three existing clustering methods, and popular supervised learning methods. From this assessment, O-CURE outperformed the other clustering methods while showing significant and more consistent performance than the supervised learning methods. Therefore, we recommend that the O-CURE model and the constructed map can be useful in assessing landslides and contribute to sustainable land-use planning and environmental management in light of future disasters.

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Section: Landslide Databasementioning

confidence: 99%

Section: Landslide Conditioning Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Mwakapesa

Lan²,

Nanehkaran³

et al. 2023

Front. Environ. Sci.

Self Cite

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“…Various Machine learning approaches have been developed and tested, including logistic regression, support vector machines, random forest, artificial neural networks, and convolutional neural networks. Random forest, a tree-based machine learning model, provides reliable performance in landslide susceptibility assessment that has been widely applied (Youssef et al, 2015;Merghadi et al, 2020;Nanehkaran et al, 2021;Ozer et al, 2020;Nefeslioglu and Gorum, 2020;Sevgen et al, 2019;Taalab et al, 2018;Jones et al, 2021;Mao et al, 2021;Merghadi et al, 2020;Youssef et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Gnyawali

Dahal²,

Talchabhadel

et al. 2023

Science of The Total Environment

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“…Generally, clustering can be categorized into partition-based methods, hierarchical methods, density-based methods, and model-based clustering methods. Based on these categories, various clustering methods have been proposed for conducting LSM modeling [41][42][43][44][45][46][47]. However, from the literature review, it can be noted that these methods are rare in the field of LSM compared to supervised learning-based methods; at present, there is no agreement on the most suitable method for LSM [48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

et al. 2023

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Landslide susceptibility mapping (LSM) studies provide essential information that helps various authorities in managing landslide-susceptible areas. This study aimed at applying and comparing the performance of DIvisive ANAlysis (DIANA) and RObust Clustering using linKs (ROCK) algorithms for LSM in the Baota District, China. These methods can be applied when the data has no labels and when there is insufficient inventory data. First, based on historical records, survey reports, and previous studies, 293 landslides were mapped in the study area and 7 landslide-influencing attributes were selected for modeling. Second, the methods were clustered in the study area mapping units into 469 and 476 subsets, respectively; for mapping landslide susceptibility, the subsets were classified into 5 susceptibility levels through the K-means method using landslide densities and attribute values. Then, their performances were assessed and compared using statistical metrics and the receiver operating curve (ROC). The outcomes indicated that similarity measures influenced the accuracy and the predictive power of these clustering models. In particular, when using a link-based similarity measure, the ROCK performed better with overall performance accuracy of 0.8933 and an area under the curve (AUC) of 0.875. The maps constructed from the models can be useful in landslide assessment, prevention, and mitigation strategies in the study area, especially for areas classified with higher susceptibility levels. Moreover, this comparison provides a new perspective in the selection of a considerable model for LSM in the Baota District.

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

Cited by 8 publications

References 39 publications

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Framework for rainfall-triggered landslide-prone critical infrastructure zonation

Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance

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