Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Li, Tang; Yu, Xianyu; Jiang, Weiwei; Zhou, Jinyan

doi:10.1038/s41598-023-33186-z

Cited by 12 publications

(4 citation statements)

References 54 publications

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“…With increased training samples accuracy also increases [ 81 ]. This should be considered in two aspects such as dividing samples during training machine learning models and during the selection of input data [ 82 ].…”

Section: Resultsmentioning

confidence: 99%

A review on landslide susceptibility mapping research in Bangladesh

Chowdhury

2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

A review on landslide susceptibility mapping research in Bangladesh

Chowdhury

2023

Heliyon

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“…Where 𝑛 is the number of landslides causative parameters. Receiver Operating Characteristic (ROC) curve is used to validate the landslide susceptibility map because ROC curve can verify the model performance by showing the success and predictive rate of the model [12]. ROC curve shows the false positive value on the X-axis and the true positive value on the Y-axis in a graphical plot [11].…”

Section: Methodsmentioning

confidence: 99%

Spatial Analysis of Causative Factors for Landslide Susceptibility on Java Island

Kusuma,

Meilano,

Sadisun

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Landslide is the most frequent natural disaster on Java Island with around 7300 occurrences in 1998-2023 (BNPB), causing 1807 fatalities and 45770 infrastructure damages. Landslide susceptibility modeling can be implemented as a basis for landslide risk modeling in Java Island for mitigation purposes. This research aims to model landslide susceptibility in Java Island and validate it based on historical landslide occurrence data. The methodology used for the landslide susceptibility model is based on the relationship between the distribution of landslide events and the causative factors in each of their classification class. Meanwhile, the methodology used for validation is Receiver Operating Characteristic (ROC) curve analysis, conducted by comparing the landslide susceptibility model with landslide occurrence data to obtain the area under the curve (AUC) value that shows the performance of the model. In this study, historical landslide occurrence data from two different sources, NASA and BNPB, were used to generate two separate models to see the difference of performance between both models. Each landslide occurrence data from both sources is divided into two parts; 70% of it is used to develop landslide susceptibility map and the rest is used for validation process. As many as ten causative factors were used to generate the model; elevation, slope, aspect, lithology, land cover, rainfall, river density, PGA (Peak Ground Acceleration), and distance to fault and river. The results show that based on landslide susceptibility map from BNPB data, Java Island is dominated with low susceptibility, that is about 58447 km2. Meanwhile, based on landslide susceptibility map from NASA data, Java Island is dominated by medium susceptibility, that is around 76731 km2. The performance for the models based on AUC values are 0.98-0.99 successful for NASA’s dataset and 0.91-0.92 successful for BNPB’s dataset in assessing landslide susceptibility in Java Island.

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“…The training set was composed of an equal proportion of landslide samples (strain value of 1) and non-landslide samples (strain value of 0) 45 , Furthermore, several scholars have investigated the impact of different sample ratios in the training dataset on the outcomes of LSM 46 . Considering various LSM models, this article opted to construct the training set using an equal proportion of landslide and non-landslide samples.…”

Section: Landslide Susceptibility Mappingmentioning

confidence: 99%

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

Yu,

Chen

2024

Sci Rep

Self Cite

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Landslides, recognized as a significant global natural disaster, necessitate an exploration of the impact of various resolution types in sampling strategies on Landslide Susceptibility Mapping (LSM) results. This study focuses on the segment from Zigui to Badong within the Three Gorges Reservoir Area, utilizing two resolution types: sampling resolution and spatial resolution, The Support Vector Machine (SVM) is employed to obtain LSM results, which are then analyzed using Receiver Operating Characteristic (ROC) curve, specific category accuracy and statistical methods. Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used to verify the reliability of the results. Additionally, five common machine learning models, including Logistic Regression (LR), are used to conduct experiments on four sampling resolutions (10 m,30 m,50 m and 70 m) to further investigate the effect of sampling resolution on LSM results. These are evaluated using a comprehensive quantitative method. The results reveal that increasing spatial resolution improves the prediction accuracy, while increasing sampling resolution produces a contrary effect. Furthermore, the impact of spatial resolution on LSM results is more pronounced than that of sampling resolution. Finally, Fanjiaping landslide and Huangtupo landslide are selected as references for comparative analysis, with the results aligning with engineering reality.

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Comparative study on landslide susceptibility mapping based on unbalanced sample ratio

Cited by 12 publications

References 54 publications

A review on landslide susceptibility mapping research in Bangladesh

A review on landslide susceptibility mapping research in Bangladesh

Spatial Analysis of Causative Factors for Landslide Susceptibility on Java Island

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results

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