Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

Xing, Yin; Yue, Jianping; Guo, Zhengfu; Yang, Chen; Hu, Jia; Travé, Anna

doi:10.3389/feart.2021.722491

Cited by 28 publications

(21 citation statements)

References 73 publications

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“…To ensure acceptable model accuracy, the final models were evaluated using the area under the curve (AUC), which was obtained by constructing a receiver operating characteristic (ROC) curve comparing true-and false-positive rates, an F1 score by Equation (3) [50], and the overall accuracy by Equation ( 4) [51]. The all-accuracy score takes a value between 0% and 100%, and a higher score indicates higher model accuracy [46,51,52]. All geoprocessing was performed with QGIS (ver.…”

Section: Discussionmentioning

confidence: 99%

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Lusiana

Shinohara

2022

Water

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Landslides often cause deaths and severe economic losses. In general, forests play an important role in reducing landslide probability because of the stabilizing effect of the tree roots. Although fruit groves consist of trees, which are similar to forests, practical land management, such as the frequent trampling of fields by laborers and compression of the terrain, may cause such land to become prone to landslides compared with forests. Fruit groves are widely distributed in hilly regions, but few studies have examined their role in landslide initiation. This study aims at filling this gap evaluating the predisposing and triggering conditions for rainfall-triggering landslides in part of Uwajima City, Japan. A large number of landslides occurred due to a heavy rainfall event in July 2018, where citrus groves occupied about 50% of the study area. In this study, we combined geodata with a regression model to assess the landslide hazard of fruit groves in hilly regions. We developed maps for five conditioning factors: slope gradient, slope aspect, normalized difference vegetation index (NDVI), land use, and geology. Based on these five maps and a landslide inventory map, we found that the landslide area density in citrus groves was larger than in forests for the categories of slope gradient, slope aspect, NDVI, and geology. Ten logistic regression models along with different rainfall indices (i.e., 1-h, 3-h, 12-h, 24-h maximum rainfall and total rainfall) and different land use (forests or citrus groves) in addition to the other four conditioning factors were produced. The result revealed that “citrus grove” was a significant factor with a positive coefficient for all models, whereas “forest” was a negative coefficient. These results suggest that citrus groves have a higher probability of landslide initiation than forests in this study area. Similar studies targeting different sites with various types of fruit groves and several rainfall events are crucial to generalize the analysis of landslide hazard in fruit groves.

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Section: Discussionmentioning

confidence: 99%

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Lusiana

Shinohara

2022

Water

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“…Due to the observed upward tendency in landslide occurrence, authorities of all the administration levels (national, regional and local) are called on to collaborate with the scientific community to spatially determine potential landslide instances and mitigate, or even prevent, the damage and losses that they may cause. LS assessment and mapping is the first and most basic step for effective risk management and disaster response [58]. Several LS assessment models have been developed and applied, with their own advantages and disadvantages [59].…”

Section: Discussionmentioning

confidence: 99%

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Polykretis

Grillakis

Argyriou

et al. 2021

Land

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Over the last few years, landslides have occurred more and more frequently worldwide, causing severe effects on both natural and human environments. Given that landslide susceptibility (LS) assessments and mapping can spatially determine the potential for landslides in a region, it constitutes a basic step in effective risk management and disaster response. Nowadays, several LS models are available, with each one having its advantages and disadvantages. In order to enhance the benefits and overcome the weaknesses of individual modeling, the present study proposes a hybrid LS model based on the integration of two different statistical analysis models, the multivariate Geographical Detector (GeoDetector) and the bivariate information value (IV). In a GIS-based framework, the hybrid model named GeoDIV was tested to generate a reliable LS map for the vicinity of the Pinios artificial lake (Ilia, Greece), a Greek wetland. A landslide inventory of 60 past landslides and 14 conditioning (morphological, hydro-lithological and anthropogenic) factors was prepared to compose the spatial database. An LS map was derived from the GeoDIV model, presenting the different zones of potential landslides (probability) for the study area. This map was then validated by success and prediction rates—which translate to the accuracy and prediction ability of the model, respectively. The findings confirmed that hybrid modeling can outperform individual modeling, as the proposed GeoDIV model presented better validation results than the IV model.

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“… Landslide influencing factors of 424 landslide samples and 424 non-landslide samples are extracted by using ArcGIS (Environmental Systems Research Institute, Inc., Redlands, CA, USA). Then, 80% and 20% of samples are randomly assigned as the training set and testing set, respectively (Asadi et al 2022 ; Nhu et al 2020 ; Xing et al 2021 ). Bayesian algorithm is used to optimize the CNN hyperparameters and training options.…”

Section: Methodsmentioning

confidence: 99%

Application of convolutional neural networks based on Bayesian optimization to landslide susceptibility mapping of transmission tower foundation

Lin

Chen

2023

Bull Eng Geol Environ

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The stability of tower foundation slopes is an important factor to maintain the operation of a power system. However, it is time-consuming and expensive to evaluate tower foundation slopes one by one due to the large area. The aim of this study is to investigate the performance of CNNs with different architectures and training options for transmission tower foundation landslide spatial prediction (LSP) by Bayesian optimization. Accordingly, fourteen influencing factors related to landslide evaluated by gain ratio technique are considered and 424 historical landslide locations in Luoding and Xinyi Counties (Guangdong Province, China) are randomly divided into 80% for training and 20% for testing the CNNs. The CNN performances are investigated by permutating and combining different numbers of convolutional layers, pooling layers and learning rate strategy. In 59 Bayesian optimized cases, three conclusions are drawn: (a) the CNNs yielded the best result with 3 convolution layers, (b) the CNN without a pooling layer performs best, and (c) a piece-wise decay learning rate strategy yields better performance. Meanwhile, the excellent performance of the CNN obtained by Bayesian optimization (CNN B ) has also been validated by comparisons with gravitational search optimization algorithm and other landslide spatial models, which indicates that CNN B can be applied to generate the susceptibility maps for locating transmission tower foundations in high landslide susceptibility zones and reducing the impact of landslides on power supply by taking measures in advance.

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Large-Scale Landslide Susceptibility Mapping Using an Integrated Machine Learning Model: A Case Study in the Lvliang Mountains of China

Cited by 28 publications

References 73 publications

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

The Role of Citrus Groves in Rainfall-Triggered Landslide Hazards in Uwajima, Japan

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Application of convolutional neural networks based on Bayesian optimization to landslide susceptibility mapping of transmission tower foundation

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