Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

Chen, Yangyang; Ming, Dongping; Xiao, Litian; Lv, Xianwei; Zhou, Chenghu

doi:10.1109/jstars.2021.3066378

Cited by 34 publications

(26 citation statements)

References 63 publications

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“…Therefore, this method is beneficial to reflect the complex nonlinear relationship between causative factors and samples in LSM [27]. Deep belief network (DBN), Convolutional neural network (CNN) and improved methods as a novel DL algorithm have been successfully used in LSM [28]- [31]. Multiple restricted Boltzmann machine (RBM) in DBN had extreme variability which corresponds to the translational invariance of image.…”

Section: Landslide Susceptibility Mapping Using Ant Colony Optimizati...mentioning

confidence: 99%

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

Xiong¹,

Zhou²,

Wang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Landslide susceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for post-earthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson's correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this paper shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.Index Terms-ant colony optimization (ACO) algorithm, deep belief network (DBN), Jiuzhaigou earthquake, landslide susceptibility mapping (LSM). I. INTRODUCTIONs one of the most common geological hazards worldwide, landslides have caused huge human casualties and economic losses every year. Especially in China, landslides are the most widespread and costly geological disasters [1]. According to the statistics yearbook of the Geological Disaster Guidance Center of the Ministry of Natural Resources, in 2020, there were 7840 geological disasters in China, of which 4810 Manuscript received xx xx, xx.

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Section: Landslide Susceptibility Mapping Using Ant Colony Optimizati...mentioning

confidence: 99%

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

Xiong¹,

Zhou²,

Wang³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Most of the above automatic or * Corresponding author (E-mail address: hanling@chd.edu.cn) semi-automatic identification methods are based on the shallow features of landslides on optical remote sensing images to achieve landslide identification and localization. These methods are severely limited by the quality of the data and the upper limit of the recognition accuracy that can be achieved is low (Chen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Fusion Network to Multi Source Heterogeneous Data for Landslide Recognition

Han²,

Li³

2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. In recent years, the frequency of landslide disasters has been increasing year by year due to the extension of human activities to the natural environment. Fast and detailed landslide surveys are important for landslide disaster prediction and management. There are many driving factors for landslide formation, and most of the current deep learning-based landslide identification methods use optical remote sensing images in a short period or a few types of fused data for prediction. Therefore the upper limit of accuracy they can achieve is low. This paper proposes a landslide identification network model based on the spatio-temporal fusion of heterogeneous data from multiple sources. The model takes observations such as time-series optical remote sensing images, DEM, geological formations, and meteorological data as inputs. To address the problems of non-uniform data forms and redundancy caused by time-series data, we design the temporal phase fusion module of coupled CNN-LSTM to fuse the temporal features of multi-source data based on the extraction of their spatial features. Subsequently, we design the spatial feature fusion module based on DCNN-DBN to realize the deep expression of temporal phase and spatial features of landslides and improve the recognition efficiency and accuracy of the network. Through experimental verification, the AUC value of our proposed model is 0.8976, the F1 score is 0.8352, and the MIoU is 0.8624. The evaluation results reflect that the model can provide support for large-scale landslide disaster investigation.

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“…Convolutional neural networks (CNN) extract image features by convolution [ 31 ]; in the CNN network structure, pooling layers reduce the amount of data while retaining useful information, and fully connected layers obtain activation values. CNNs can be used in the field of LSM as the landslide conditioning factors are stacked and formally consistent with RGB images [ 32 , 33 , 34 , 35 , 36 ]. Recurrent neural networks (RNN), another important development in DL, combine landslide conditioning factors as serialized data in LSM [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Transformer Models to Landslide Susceptibility Mapping

Bao

Liu

Wang

et al. 2022

Sensors

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Landslide susceptibility mapping (LSM) is of great significance for the identification and prevention of geological hazards. LSM is based on convolutional neural networks (CNNs); CNNs use fixed convolutional kernels, focus more on local information and do not retain spatial information. This is a property of the CNN itself, resulting in low accuracy of LSM. Based on the above problems, we use Vision Transformer (ViT) and its derivative model Swin Transformer (Swin) to conduct LSM for the selected study area. Machine learning and a CNN model are used for comparison. Fourier transform amplitude, feature similarity and other indicators were used to compare and analyze the difference in the results. The results show that the Swin model has the best accuracy, F1-score and AUC. The results of LSM are combined with landslide points, faults and other data analysis; the ViT model results are the most consistent with the actual situation, showing the strongest generalization ability. In this paper, we believe that the advantages of ViT and its derived models in global feature extraction ensure that ViT is more accurate than CNN and machine learning in predicting landslide probability in the study area.

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Landslide Susceptibility Mapping Using Feature Fusion-Based CPCNN-ML in Lantau Island, Hong Kong

Cited by 34 publications

References 63 publications

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

A Spatiotemporal Fusion Network to Multi Source Heterogeneous Data for Landslide Recognition

Application of Transformer Models to Landslide Susceptibility Mapping

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