Exploring the Risky Areas Due to Landslide Using Decision Tree Analysis: Case Study Tasmania, Australia

SHAFAPOURTEHRANY, Mahyat

doi:10.55549/epstem.1192352

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Similarly, Reichenbach et al [41] and Shano et al [42] reviewed the most popular and wellknown approaches in this domain. Most of the existing studies were noted to use RF [43], logistic regression [44], artificial neural network [45], SVM [37], decision tree [46], K-nearest neighbors [47], weights of evidence [48], adaptive neuro-fuzzy inference systems [49], and evidential belief functions [50]. In general, landslides have complex structures due to numerous inherent causative and external triggering factors [51].…”

Section: Introductionmentioning

confidence: 99%

Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms

Shafapourtehrany,

Rezaie,

Jun

et al. 2023

Remote Sensing

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Landslides are among the most frequent secondary disasters caused by earthquakes in areas prone to seismic activity. Given the necessity of assessing the current seismic conditions for ensuring the safety of life and infrastructure, there is a rising demand worldwide to recognize the extent of landslides and map their susceptibility. This study involved two stages: First, the regions prone to earthquake-induced landslides were detected, and the data were used to train deep learning (DL) models and generate landslide susceptibility maps. The application of DL models was expected to improve the outcomes in both stages. Landslide inventory was extracted from Sentinel-2 data by using U-Net, VGG-16, and VGG-19 algorithms. Because VGG-16 produced the most accurate inventory locations, the corresponding results were used in the landslide susceptibility detection stage. In the second stage, landslide susceptibility maps were generated. From the total measured landslide locations (63,360 cells), 70% of the locations were used for training the DL models (i.e., convolutional neural network [CNN], CNN-imperialist competitive algorithm, and CNN-gray wolf optimizer [GWO]), and the remaining 30% were used for validation. The earthquake-induced landslide conditioning factors included the elevation, slope, plan curvature, valley depth, topographic wetness index, land cover, rainfall, distance to rivers, and distance to roads. The reliability of the generated susceptibility maps was evaluated using the area under the receiver operating characteristic curve (AUROC) and root mean square error (RMSE). The CNN-GWO model (AUROC = 0.84 and RMSE = 0.284) outperformed the other methods and can thus be used in similar applications. The results demonstrated the efficiency of applying DL in the natural hazard domain. The CNN-GWO predicted that approximately 38% of the total area consisted of high and very high susceptibility regions, mainly concentrated in areas with steep slopes and high levels of rainfall and soil wetness. These outcomes contribute to an enhanced understanding of DL application in the natural hazard domain. Moreover, using the knowledge of areas highly susceptible to landslides, officials can actively adopt steps to reduce the potential impact of landslides and ensure the sustainable management of natural resources.

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