Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks

Zhang, Han; Yin, Chao; Wang, Shaoping; Guo, Bing

doi:10.1007/s11069-022-05748-3

Cited by 6 publications

(6 citation statements)

References 47 publications

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“…Compared with landslides, collapse also has many influencing factors and complicated causes. The CNN has been widely used in landslide susceptibility prediction [47][48][49], so we try to apply the CNN to the assessment of collapse susceptibility.…”

Section: Discussionmentioning

confidence: 99%

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

Li,

Hu,

Lin

et al. 2024

Water

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The cause mechanism of collapse disasters is complex and there are many influencing factors. Convolutional Neural Network (CNN) has a strong feature extraction ability, which can better simulate the formation of collapse disasters and accurately predict them. Taihe town’s collapse threatens roads, buildings, and people. In this paper, road distance, water distance, normalized vegetation index, platform curvature, profile curvature, slope, slope direction, and geological data are used as input variables. This paper generates collapse susceptibility zoning maps based on the information value method (IV) and CNN, respectively. The results show that the accuracy of the susceptibility assessment of the IV method and the CNN method is 85.1% and 87.4%, and the accuracy of the susceptibility assessment based on the CNN method is higher. The research results can provide some reference for the formulation of disaster prevention and control strategies.

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Section: Discussionmentioning

confidence: 99%

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

Li,

Hu,

Lin

et al. 2024

Water

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“…> 3000 This study utilizes GIS applications in the form of ArcGIS 10.8 to process data as overlays, scoring, and map making (Chen, & Wang, 2019). Google Earth Engine for land cover/use classification and Google Earth Pro for advanced analysis of disaster safe areas with settlements (Tan & Lee, 2017).…”

Section: Table 3: Annual Rainfallmentioning

confidence: 99%

On Relocation of Settlement After the Cianjur Earthquake and Landslide 2022

Sabita,

Amhar,

Fatchurohman

2024

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An Earthquake and landslide occurred in Cianjur, Indonesia, in November 2022. This resulted in a lot of losses, both fatalities and the disappearance of some settlements. Relocation would be obligatory as post-disaster mitigation policies for areas with highest disaster threats. This research was conducted to find its relocation areas. The method used is overlay and scoring by utilizing GIS applications. The used data are rainfall data, slope, geological maps, Cianjur earthquake hazard maps, fault line buffering, and supervised classification of land cover/use maps. Analysis of the results in the form of landslide hazard maps and disaster safety maps. The search for the expected relocation area is carried out by overlaying a disaster-safe map with residential areas, so that a post-disaster residential relocation recommendation map is produced with five classes, namely very good, good, medium, bad, and very bad. An analysis is also carried out for directions for the use of the affected area for the very bad class used as a protected area and no buildings are allowed on it. Bad class is not allowed to live on it, but can be used for social activities, plantations and paddy fields. The moderate vulnerability class can still be used as a residence, economic centre, or other social activities. Meanwhile, the safe and very safe vulnerability classes do not have any land use restrictions.

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“…The greater the number of layers, the more complex and abstract things the neural networks can learn, and the closer they become to resembling human reasoning. However, it is difficult to develop effective learning mechanisms for each hidden layer (Bui, 2019; Bui, Tsangaratos, et al, 2020; Zhang, Yin, et al, 2022). According to the existing literature, there are no universal rules for determining the number of hidden layers and neurons.…”

Section: Deep Learning Neural Networkmentioning

confidence: 99%

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

Nguyen,

et al. 2023

Geological Journal

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Landslides lead to widespread devastation and significant loss of life in mountainous regions around the world. Susceptibility assessments can provide critical data to help decision‐makers, for example, local authorities and other organizations, mitigating the landslide risk, although the accuracy of existing studies needs to be improved. This study aims to assess landslide susceptibility in the Thua Thien Hue province of Vietnam using deep neural networks (DNNs) and swarm‐based optimization algorithms, namely Adam, stochastic gradient descent (SGD), Artificial Rabbits Optimization (ARO), Tuna Swarm Optimization (TSO), Sand Cat Swarm Optimization (SCSO), Honey Badger Algorithm (HBA), Marine Predators Algorithm (MPA) and Particle Swarm Optimization (PSO). The locations of 945 landslides occurring between 2012 and 2022, along with 14 conditioning factors, were used as input data to build the DNN and DNN‐hybrid models. The performance of the proposed models was evaluated using the statistical indices receiver operating characteristic curve, area under the curve (AUC), root mean square error, mean absolute error (MAE), R2 and accuracy. All proposed models had a high accuracy of prediction. The DNN‐MPA model had the highest AUC value (0.95), followed by DNN‐HBA (0.95), DNN‐ARO (0.95), DNN‐Adam (0.95), DNN‐SGD (0.95), DNN‐TSO (0.93), DNN‐PSO (0.9) and finally DNN‐SCSO (0.83). High‐precision models have identified that the majority of the western region of Thua Thien Hue province is very highly susceptible to landslides. Models like the aforementioned ones can support decision‐makers in updating large‐scale sustainable land‐use strategies.

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Landslide susceptibility mapping based on landslide classification and improved convolutional neural networks

Cited by 6 publications

References 47 publications

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

Collapse Susceptibility Assessment in Taihe Town Based on Convolutional Neural Network and Information Value Method

On Relocation of Settlement After the Cianjur Earthquake and Landslide 2022

Landslide susceptibility prediction using machine learning and remote sensing: Case study in Thua Thien Hue province, Vietnam

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