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

Mwakapesa, Deborah Simon; Mao, Yimin; Lan, Xiaoji; Nanehkaran, Yaser Ahangari

doi:10.3390/su15054218

Cited by 6 publications

(5 citation statements)

References 105 publications

(159 reference statements)

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“…Topographic data are the most commonly used factor in LSM studies [18]. Landslides can occur when elevation and slope exhibit certain conditions [18,48]; a higher curvature means that the slope has a stronger capacity for water accumulation and is more prone to landslides [34,48]; meteorological processes regulate sunlight, hydrological elements, and wind direction, which affect slope stability [48].…”

Section: Topographic Datamentioning

confidence: 99%

“…The third is to divide the study area into multiple regions using the clustering analysis method based on the distribution of landslides in the study area and the similarity of environmental factors and to use the category attribute of each region as one of the input variables of the machine learning model [16,41]. The introduction of more variables, however, may lead to a decrease in the generalization ability in the clustering analysis method [48]. In addition, it is difficult to quantitatively evaluate the clustering result, which may further increase the uncertainty of the LSM model [48].…”

Section: Introductionmentioning

confidence: 99%

“…The introduction of more variables, however, may lead to a decrease in the generalization ability in the clustering analysis method [48]. In addition, it is difficult to quantitatively evaluate the clustering result, which may further increase the uncertainty of the LSM model [48].…”

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Huang

Zheng³

et al. 2023

Sustainability

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Landslides pose a serious threat to human lives and property. Accurate landslide susceptibility mapping (LSM) is crucial for sustainable development. Machine learning has recently become an important means of LSM. However, the accuracy of machine learning models is limited by the heterogeneity of environmental factors and the imbalance of samples, especially for large-scale LSM. To address these problems, we created an improved random forest (RF)-based LSM model and applied it to Guangdong Province, China. First, the RF-based LSM model was constructed using rainfall-induced landslide samples and 13 environmental factors and by exploring the optimal positive-to-negative and training-to-test sample ratios. Second, the performance of the RF-based LSM model was evaluated and compared with three other machine learning models. The results indicate that: (1) the proposed RF-based model has the best performance with the highest area under curve (AUC) of 0.9145, based on optimal positive-to-negative and training-to-test sample ratios of 1:1 and 8:2, respectively; (2) the introduction of rainfall and global human modification (GHM) can increase the AUC from 0.8808 to 0.9145; and (3) rainfall and topography are two dominant factors in Guangdong landslides. These findings can facilitate landslide risk prevention and serve as a technical reference for large-scale accurate LSM.

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Section: Topographic Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Huang

Zheng³

et al. 2023

Sustainability

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“…The K-means clustering algorithm is an unsupervised machine-learning algorithm [26]. The major steps are as follows [40]:…”

Section: K-meansmentioning

confidence: 99%

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

Ma,

Chen,

et al. 2023

Sustainability

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Landslide susceptibility prediction (LSP) is the basis for risk management and plays an important role in social sustainability. However, the modeling process of LSP is constrained by various factors. This paper approaches the effect of landslide data integrity, machine-learning (ML) models, and non-landslide sample-selection methods on the accuracy of LSP, taking the Yinghu Lake Basin in Ankang City, Shaanxi Province, as an example. First, previous landslide inventory (totaling 46) and updated landslide inventory (totaling 46 + 176) were established through data collection, remote-sensing interpretation, and field investigation. With the slope unit as the mapping unit, twelve conditioning factors, including elevation, slope, aspect, topographic relief, elevation variation coefficient, slope structure, lithology, normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), distance to road, distance to river, and rainfall were selected. Next, the initial landslide susceptibility mapping (LSM) was obtained using the K-means algorithm, and non-landslide samples were determined using two methods: random selection and semi-supervised machine learning (SSML). Finally, the random forest (RF) and artificial neural network (ANN) machine-learning methods were used for modeling. The research results showed the following: (1) The performance of supervised machine learning (SML) (RF, ANN) is generally superior to unsupervised machine learning (USML) (K-means). Specifically, RF in the SML model has the best prediction performance, followed by ANN. (2) The selection method of non-landslide samples has a significant impact on LSP, and the accuracy of the SSML-based non-landslide selection method is controlled by the ratio of the number of landslide samples to the number of mapping units. (3) The quantity of landslides has an impact on how reliably the results of LSM are obtained because fewer landslides result in a smaller sample size for LSM, which deviates from reality. Although the results in this dataset are satisfactory, the zoning results cannot reliably anticipate the recently added landslide data discovered by the interpretation of remote-sensing data and field research. We propose that the landslide inventory can be increased by remote sensing in order to achieve accurate and impartial LSM since the LSM of adequate landslide samples is more reasonable. The research results of this paper will provide a reference basis for uncertain analysis of LSP and regional landslide risk management.

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“…Although this method can ensure a uniform sample distribution, it has a high degree of randomness and is prone to reducing the accuracy of model training. In addition, Mwakapesa et al proposed a clustering analysis of landslide points in the study area based on the ROCK algorithm, but this method negatively impacted clustering quality when the sample set density was uneven, and the cluster spacing difference was large (Mwakapesa et al 2023). Consequently, quantitative analysis is highly dependent on data quality, which has a direct impact on the accuracy of predictions and evaluation results.…”

Section: Introductionmentioning

confidence: 99%

An analytic hierarchy process method weighted by an improved Random Forest model considering sample optimization selection for the evaluation of landslide susceptibility

Zhang

Xie

et al. 2023

Preprint

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Landslides often cause great losses to people, so it is important to know the extent of landslide damage to reduce the impact of disasters. Although many scholars have conducted on landslide disaster susceptibility, there are still some issues, such as an unreasonable negative sample selection strategy and an absence of subjective environmental information of the study area in a single machine learning evaluation model. Therefore, an analytic hierarchy process (AHP) method weighted by an improved Random Forest (RF) model is proposed for evaluating landslide susceptibility based on sample optimization. On the basis of density analysis of landslide data, this method employs the specific factor method (CF) to generate negative sample data. The improved RF model based on adaptive boosting (ADB_RF) is proposed to obtain objective weights, which are then combined with the subjective weights obtained by AHP. Meanwhile, a case study of the evaluation of landslide disasters is conducted in the Chuxiong Autonomous Prefecture of Yunnan Province in China. The results show: (1) The landslide susceptibility evaluation method proposed in this study can objectively reflect the area prone to landslides with high accuracy and effectiveness. (2) The area under the line of the CF-combination model reached 96.1%, indicating a high degree of accuracy. (3) In the northwest of Chuxiong Prefecture, there are a greater number of extremely high-risk areas than in the southeast, the possibility of another landslide disaster is high, which needs to be focused. The research findings have significant reference value for preventing disasters and mitigating losses.

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

Cited by 6 publications

References 105 publications

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Landslide Susceptibility Mapping in Guangdong Province, China, Using Random Forest Model and Considering Sample Type and Balance

Landslide Susceptibility Prediction Using Machine Learning Methods: A Case Study of Landslides in the Yinghu Lake Basin in Shaanxi

An analytic hierarchy process method weighted by an improved Random Forest model considering sample optimization selection for the evaluation of landslide susceptibility

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