A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

Hu, Jian; Xu, Kaibin; Wang, Genglong; Liu, Youcun; Khan, Muhammad Asim; Mao, Yimin; Zhang, Maosheng

doi:10.1007/s10064-020-01863-2

Cited by 14 publications

(11 citation statements)

References 46 publications

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“…e merging process based on a dynamic model facilitates the discovery of natural and homogeneous clusters and applies to all types of data as long as a similarity function is specified [51]. To its advantage, the Chameleon algorithm is a usersupplied model, static independent, as well as adapting to the internal characteristics of the clusters of being independent of the initial partitions, as well as insensitive to noise and outliers [44]; thus, we used Chameleon algorithm to assess the landslide susceptibility. In general, for landslide susceptibility assessment using the clustering algorithm, an object is regarded as a grid.…”

Section: Chameleon Algorithmmentioning

confidence: 99%

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Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

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This study aims at proposing and designing an improved clustering algorithm for assessing landslide susceptibility using an integration of a Chameleon algorithm and an adaptive quadratic distance (CA-AQD algorithm). It targets improving the prediction capacity of clustering algorithms in landslide susceptibility modelling by overcoming the limitations found in present clustering models, including strong dependence on the initial partition, noise, and outliers as well as difficulties in quantifying the triggering factors (such as rainfall/precipitation). The model was implemented in Baota District, Shaanxi province, China. The CA-AQD algorithm was adopted to split all grids in the study area into many groups with more similar characteristic values, which also owed to efficiently quantifying the uncertain (rainfall) value by using AQD. The K-means algorithm divides these groups into five susceptibility classes according to the values of landslide density in each group. The model was then evaluated using statistical metrics and the performance was validated and compared to that of the traditional Chameleon algorithm and KPSO algorithm. The results show that the CA-AQD algorithm attained the best performance in assessing landslide susceptibility in the study area. Thus, this work adds to the literature by introducing the first empirical integration and application of the CA-AQD algorithm to the assessment of landslides in the study area, which then is a new insight to the field. Also, the method can be helpful for dealing with landslides for better social and economic development.

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Section: Chameleon Algorithmmentioning

confidence: 99%

“…Clustering analysis algorithms classify sets of objects (grids) into groups that are more similar to each other than they are to objects in other clusters (groups) [44]. is process is conducted by primitive observation with little or no prior knowledge; that is, it is unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Mao

Mwakapesa

Wang³

et al. 2021

Advances in Civil Engineering

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“…Because of these limitations, SL methods may not be applicable where there are a limited number of labeled samples as they are not always easy to obtain and may be expensive to acquire in abundance through image interpretation and site surveying, especially in a large study area. USL-based approaches are applied and have contributed to improving the implementation and the accuracy of LSM in such situations (Lei et al, 2018;Hu et al, 2021;Yimin et al, 2021;Mao et al, 2022;Su et al, 2022;Liu et al, 2023). USL-based methods such as clustering can be used to map the susceptibility areas, as they can identify the underlying structures in unlabeled datasets, hence, do not require data with predefined labels, and do not involve a training process during their implementation.…”

Section: Introductionmentioning

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%

“…Moreover, to evaluate the performance of the O-CURE method, five statistic metrics are applied, i.e., accuracy, sensitivity, specificity, kappa, and AUC. Also, the traditional CURE, existing LSM clustering methods: Chameleon algorithm and an adaptive quadratic distance-CA-AQD (Yimin et al, 2021), Agglomerative Hierarchical Clustering algorithm based on Overlapped Interval Divergence distance-AHC-OLID (Mao et al, 2021a), and OPTICS algorithm using the Hausdorff distance-OA-HD (Hu et al, 2021) as well as Decision Tree-DT (Nikoobakht et al, 2022), Support Vector Machine-SVM (Nikoobakht et al, 2022), and Uncertain Decision Tree-DTU (Mao et al, 2017) supervised learning models were applied for comparison as benchmark methods using data from the same study area. Furthermore, the key objectives of this study are to develop an improved clustering method-the O-CURE algorithm for LSM modeling in Baota District and apply landslide density, attribute values, and the partitioning around medoids (PAM) algorithm to indicate the landslide susceptibility levels in the study area.…”

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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Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data

Das,

Priyadarshana,

Grebby

2024

Stoch Environ Res Risk Assess

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Slope failures possess destructive power that can cause significant damage to both life and infrastructure. Monitoring slopes prone to instabilities is therefore critical in mitigating the risk posed by their failure. The purpose of slope monitoring is to detect precursory signs of stability issues, such as changes in the rate of displacement with which a slope is deforming. This information can then be used to predict the timing or probability of an imminent failure in order to provide an early warning. Most approaches to predicting slope failures, such as the inverse velocity method, focus on predicting the timing of a potential failure. However, such approaches are deterministic and require some subjective analysis of displacement monitoring data to generate reliable timing predictions. In this study, a more objective, probabilistic-learning algorithm is proposed to detect and characterise the risk of a slope failure, based on spectral analysis of serially correlated displacement time-series data. The algorithm is applied to satellite-based interferometric synthetic radar (InSAR) displacement time-series data to retrospectively analyse the risk of the 2019 Brumadinho tailings dam collapse in Brazil. Two potential risk milestones are identified and signs of a definitive but emergent risk (27 February 2018-26 August 2018) and imminent risk of collapse of the tailings dam (27 June 2018-24 December 2018) are detected by the algorithm as the empirical points of inflection and maximum on a risk trajectory, respectively. Importantly, this precursory indication of risk of failure is detected as early as at least five months prior to the dam collapse on 25 January 2019. The results of this study demonstrate that the combination of spectral methods and second order statistical properties of InSAR displacement time-series data can reveal signs of a transition into an unstable deformation regime, and that this algorithm can provide sufficient early-warning that could help mitigate catastrophic slope failures.

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A novel landslide susceptibility mapping portrayed by OA-HD and K-medoids clustering algorithms

Cited by 14 publications

References 46 publications

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Innovative Landslide Susceptibility Mapping Portrayed by CA‐AQD and K‐Means Clustering Algorithms

Landslide susceptibility mapping using O-CURE and PAM clustering algorithms

Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data

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