Three oversampling methods applied in a comparative landslide spatial research in Penang Island, Malaysia

Gao, Han; Fam, Pei Shan; Tay, Lea Tien; Low, Heng Chin

doi:10.1007/s42452-020-03307-8

Cited by 20 publications

(4 citation statements)

References 49 publications

(60 reference statements)

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“…Multiple covariance analysis is often used to evaluate the correlation between factors to ensure that there is no linear correlation between factors 37 . In LSM, if the VIF value of a factor is greater than 10 or the TOL value is less than 0.1, it means that the factor has serious multicollinearity problems, and the factor should be removed 38 .…”

Section: Methodsmentioning

confidence: 99%

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Jiang

et al. 2023

Sci Rep

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The Zigui–Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM factors, five training sample sets with different sample ratios (1:1, 1:2, 1:4, 1:8, and 1:16), and C5.0, Support Vector Machine (SVM), Logistic Regression (LR), and one-dimensional Convolution Neural Network (CNN) models are used to obtain landslide susceptibility index and landslide susceptibility zoning in the study area, respectively. The prediction performance of the model is evaluated by the receiver operating characteristic curve area under the curve value, five statistical methods, and specific category precision. The results show that the CNN, SVM, and LR models in the sample ratio of 1:2 achieve better performance than on the balanced sample set, which indicates the importance of the unbalanced sample set in training the LSM modeling. The C5.0 model is always in a state of overfitting in this study and needs to be further studied. The conclusions put forward in this study help improve the scientificity and reliability of LSM.

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Section: Methodsmentioning

confidence: 99%

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Jiang

et al. 2023

Sci Rep

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“…Oversampling addresses class imbalance between landslide occurrences/historical records (positive instances) and non-landslide areas (negative instances) in the dataset. The imbalance can lead to biased models that favor the majority class [54]. This allows logistic regression, a binary classification algorithm commonly used in landslide susceptibility assessment, to learn from a broader range of examples and improve its ability to predict landslide occurrences accurately.…”

Section: Studied Case Locationmentioning

confidence: 99%

Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Cemiloglu

Zhu

Mohammednour

et al. 2023

Land

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Landslide susceptibility assessment is the globally approved procedure to prepare geo-hazard maps of landslide-prone areas, which are highly used in urban management and minimizing the possible disasters due to landslides. Multiple approaches to providing susceptibility maps for landslides have one specification. Logistic regression is a statistical-based model that investigates the probabilities of the events which is received extensive success in landslide susceptibility assessment. The presented study attempted to use a logistic regression application to prepare the Maragheh County hazard risk map. In this regard, several predisposing factors (e.g., elevation, slope aspect, slope angle, rainfall, land use, lithology, weathering, distance from faults, distance from the river, distance from the road, and distance from cities) are identified as main responsible for landslide occurrence and 20 historical sliding events which used to prepare hazard risk maps. As verification, the models were controlled by operating relative characteristics (ROC) curves which reported the overall accuracy for susceptibility assessment. According to the results, the region is located in a moderate to high-hazard risk zone. The north and northeast parts of Maragheh County show high suitability for landslides. Verification results of the model indicated that the AUC estimated for the training set is 0.885, and the AUC estimated for the testing set is 0.769. To justify the model, the results of the LR were comparatively checked with several benchmark learning models. Results indicated that LR model performance is reasonable.

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“…Research by Gao [55] had use three oversampling methods to relate the spatial research with landslide in Penang Island. With the purpose to augment the landslide sample data in an effective way and to assign proper unequal cost, this research had come out with the results of landslide susceptibility mapping to conduct landslide spatial assessment.…”

Section: Landslidementioning

confidence: 99%

Geographical Information System (GIS) and Remote Sensing (RS) Applications in Disaster Risk Reduction (DRR) in Malaysia

Sulaiman¹,

Abdullah²,

Nazir³

et al. 2022

IJIE

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In a world today, that is highly dependent on information technologies, Geographical Information System (GIS) and Remote Sensing (RS) has become one of advancement in spatial technologies that had been used to tackle the issue of an uncertain world. Primarily functioned with specialized capabilities in manipulating, analysing, and visualizing the massive data from multiagency, has opened new avenues for these technologies to be adopted in disaster management. Taking this into consideration, it leads to achieving disaster management objectives in Disaster Risk Reduction (DRR) which to reduce or minimize the exposure to hazards, lessened vulnerability of people and property, wise emergency preparation, and enhanced preparedness for an unfavourable situation. This paper aims to make a systematic review of literature in highlighting the significant potential of the GIS and remote sensing, integrating the aspect relevant to disasters socially and physically that helps in forming a comprehensive disaster management operation to reduce the vulnerability and strengthen the resilience to disaster. Accordingly, the paper has presented the outcomes and review of several researchers concerning the implementation of GIS and remote sensing in disaster management, specifically on disaster risk reduction.

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Three oversampling methods applied in a comparative landslide spatial research in Penang Island, Malaysia

Cited by 20 publications

References 49 publications

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Landslide Susceptibility Assessment for Maragheh County, Iran, Using the Logistic Regression Algorithm

Geographical Information System (GIS) and Remote Sensing (RS) Applications in Disaster Risk Reduction (DRR) in Malaysia

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