Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

Yao, Jingyu; Qin, Shengwu; Qiao, Shuangshuang; Che, Wenchao; Chen, Yang; Su, Gang; Miao, Qiang

doi:10.3390/app10165640

Cited by 42 publications

(18 citation statements)

References 80 publications

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“…Relatively better performance of DLNN than of ANN was also achieved in landslide vulnerability mapping for the present research. The efficiency of deep learning models compared to ML models was found to be better as per the study of Yao et al 54 where authors have developed the deep neural network model based on semi-supervised analysis (SSL-DNN) for the landslide susceptibility estimation. For comparison, supervised models were introduced, including deep neural network (DNN), SVM, and logistic regression (LR).…”

Section: Discussionmentioning

confidence: 82%

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Sarkar

Saha

Hembram³

et al. 2021

Sci Rep

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Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may—support landslide prediction and management.

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Section: Discussionmentioning

confidence: 82%

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Sarkar

Saha

Hembram³

et al. 2021

Sci Rep

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“…As for logistic regression and bootstrap models, they borrowed statistical models into ML models to dilute the distinction between the two [23]. However, these models have a shallow structure with only one or zero hidden layers, which have disadvantages such as limited training time, easy to fall into local optimum, and unstable convergence [24].…”

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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“…Semi-supervised learning has been widely used in sample data analysis and evaluation [26][27][28]. In landslide susceptibility prediction and landslide detection, supervised learning frameworks, semi-supervised learning frameworks, and unsupervised learning frameworks have also demonstrated their superiority [29][30][31]. This paper selects Fu'an City, Fujian Province, China, as the research area.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

Zhang

Yan

2022

IJGI

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Landslide susceptibility prediction has the disadvantages of being challenging to apply to expanding landslide samples and the low accuracy of a subjective random selection of non-landslide samples. Taking Fu’an City, Fujian Province, as an example, a model based on a semi-supervised framework using particle swarm optimization to optimize extreme learning machines (SS-PSO-ELM) is proposed. Based on the landslide samples, a semi-supervised learning framework is constructed through Density Peak Clustering (DPC), Frequency Ratio (FR), and Random Forest (RF) models to expand and divide the landslide sample data. The landslide susceptibility was predicted using high-trust sample data as the input variables of the data-driven model. The results show that the area under the curve (AUC) valued at the SS-PSO-ELM model for landslide susceptibility prediction is 0.893 and the root means square error (RMSE) is 0.370, which is better than ELM and PSO-ELM models without the semi-supervised framework. It shows that the SS-PSO-ELM model is more effective in landslide susceptibility. Thus, it provides a new research idea for predicting landslide susceptibility.

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Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

Cited by 42 publications

References 80 publications

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

Measuring landslide vulnerability status of Chukha, Bhutan using deep learning algorithms

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

Landslide Susceptibility Prediction Based on High-Trust Non-Landslide Point Selection

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