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

Accurate and timely landslide susceptibility mapping (LSM) is essential to effectively reduce the risk of landslide. In recent years, deep learning has been successfully applied to landslide susceptibility assessment due to the strong ability of fitting. However, in actual applications, the number of labeled samples is usually not sufficient for the training component. In this paper, a deep neural network model based on semi-supervised learning (SSL-DNN) for landslide susceptibility is proposed, which makes full use of a large number of spatial information (unlabeled data) with limited labeled data in the region to train the mode. Taking Jiaohe County in Jilin Province, China as an example, the landslide inventory from 2000 to 2017 was collected and 12 metrological, geographical, and human explanatory factors were compiled. Meanwhile, supervised models such as deep neural network (DNN), support vector machine (SVM), and logistic regression (LR) were implemented for comparison. Then, the landslide susceptibility was plotted and a series of evaluation tools such as class accuracy, predictive rate curves (AUC), and information gain ratio (IGR) were calculated to compare the prediction of models and factors. Experimental results indicate that the proposed SSL-DNN model (AUC = 0.898) outperformed all the comparison models. Therefore, semi-supervised deep learning could be considered as a potential approach for LSM.

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Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Remote Sensing ◽

10.3390/rs13112166 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2166

Author(s):

Xin Yang ◽

Rui Liu ◽

Mei Yang ◽

Jingjue Chen ◽

Tianqiang Liu ◽

...

Keyword(s):

Land Use ◽

Hybrid Model ◽

Landslide Susceptibility ◽

Spatial Information ◽

Landslide Susceptibility Mapping ◽

Ratio Method ◽

Support Vector ◽

Susceptibility Map ◽

Conditioning Factors ◽

Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

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Landslide susceptibility mapping based on convolutional neural network and conventional machine learning methods

10.21203/rs.3.rs-190195/v1 ◽

2021 ◽

Author(s):

Rui Liu ◽

Xin Yang ◽

Chong Xu ◽

Luyao Li ◽

Xiangqiang Zeng

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Landslide Susceptibility ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Conventional Machine

Abstract Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced Convolutional Neural Network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected the Jiuzhaigou region in Sichuan Province, China as the study area. A total number of 710 landslides and 12 predisposing factors were stacked to form spatial datasets for LSM. The ROC analysis and several statistical metrics, such as accuracy, root mean square error (RMSE), Kappa coefficient, sensitivity, and specificity were used to evaluate the performance of the models in the training and validation datasets. Finally, the trained models were calculated and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine-learning based models have a satisfactory performance (AUC: 85.72% − 90.17%). The CNN based model exhibits excellent good-of-fit and prediction capability, and achieves the highest performance (AUC: 90.17%) but also significantly reduces the salt-of-pepper effect, which indicates its great potential of application to LSM.

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Comparison between Deep Learning and Tree-Based Machine Learning Approaches for Landslide Susceptibility Mapping

Water ◽

10.3390/w13192664 ◽

2021 ◽

Vol 13 (19) ◽

pp. 2664

Author(s):

Sunil Saha ◽

Jagabandhu Roy ◽

Tusar Kanti Hembram ◽

Biswajeet Pradhan ◽

Abhirup Dikshit ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Landslide Susceptibility ◽

Learning Model ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Learning Approaches ◽

Statistical Measures ◽

Deep Learning Model

The efficiency of deep learning and tree-based machine learning approaches has gained immense popularity in various fields. One deep learning model viz. convolution neural network (CNN), artificial neural network (ANN) and four tree-based machine learning models, namely, alternative decision tree (ADTree), classification and regression tree (CART), functional tree and logistic model tree (LMT), were used for landslide susceptibility mapping in the East Sikkim Himalaya region of India, and the results were compared. Landslide areas were delimited and mapped as landslide inventory (LIM) after gathering information from historical records and periodic field investigations. In LIM, 91 landslides were plotted and classified into training (64 landslides) and testing (27 landslides) subsets randomly to train and validate the models. A total of 21 landslide conditioning factors (LCFs) were considered as model inputs, and the results of each model were categorised under five susceptibility classes. The receiver operating characteristics curve and 21 statistical measures were used to evaluate and prioritise the models. The CNN deep learning model achieved the priority rank 1 with area under the curve of 0.918 and 0.933 by using the training and testing data, quantifying 23.02% and 14.40% area as very high and highly susceptible followed by ANN, ADtree, CART, FTree and LMT models. This research might be useful in landslide studies, especially in locations with comparable geophysical and climatological characteristics, to aid in decision making for land use planning.

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Human Activity Recognition Based on Deep Learning Techniques

Proceedings ◽

10.3390/ecsa-6-06539 ◽

2019 ◽

Vol 42 (1) ◽

pp. 15

Author(s):

Manuel Gil-Martín ◽

Marcos Sánchez-Hernández ◽

Rubén San-Segundo

Keyword(s):

Neural Network ◽

Deep Learning ◽

Activity Recognition ◽

Human Activity ◽

Deep Neural Network ◽

Human Activity Recognition ◽

Support Vector ◽

Daily Life Activities ◽

Learning Techniques ◽

The Fourier Transform

Deep learning techniques are being widely applied to Human Activity Recognition (HAR). This paper describes the implementation and evaluation of a HAR system for daily life activities using the accelerometer of an iPhone 6S. This system is based on a deep neural network including convolutional layers for feature extraction from accelerations and fully-connected layers for classification. Different transformations have been applied to the acceleration signals in order to find the appropriate input data to the deep neural network. This study has used acceleration recordings from the MotionSense dataset, where 24 subjects performed 6 activities: walking downstairs, walking upstairs, sitting, standing, walking and jogging. The evaluation has been performed using a subject-wise cross-validation: recordings from the same subject do not appear in training and testing sets at the same time. The proposed system has obtained a 9% improvement in accuracy compared to the baseline system based on Support Vector Machines. The best results have been obtained using raw data as input to a deep neural network composed of two convolutional and two max-pooling layers with decreasing kernel sizes. Results suggest that using the module of the Fourier transform as inputs provides better results when classifying only between dynamic activities.

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Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines in the Three Gorges Reservoir Area, China

10.5194/egusphere-egu2020-12379 ◽

2020 ◽

Author(s):

Lanbing Yu ◽

Yang Wang ◽

Yujie Zhang

Keyword(s):

Landslide Susceptibility ◽

Information Gain ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Three Gorges Reservoir Area ◽

Support Vector ◽

Resistance Force ◽

Reservoir Water ◽

Landslide Development ◽

Information Gain Ratio

The landslide development laws vary in different landslide-prone areas, hence the susceptibility models often perform in varied ways in different regions. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). These landslides seriously threaten the safety of local residents and their property. It is crucial to find the model that can generate a landslide susceptibility map with higher accuracy in the TGRA. The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA.The Wushan segment of TGRA was selected as a case study, which is located in the middle reaches of the TGRA, the southwest of China. In this study, 165 landslides were identified and 14 landslide causal factors were constructed from different data sources at first, including altitude, slope, aspect, curvature, plan curvature, profile curvature, stream power index, topographic wetness index (TWI), terrain roughness index, lithology, bedding structure, distance to faults, distance to rivers, and distance to gully. Subsequently, multicollinearity analysis and information gain ratio model were applied to select landslide causal factors. After removing five factors (altitude, TWI, profile curvature, plan curvature, curvature), the landslide susceptibility mapping using the calculated results of four models, which were support vector machines (SVM), artificial neural networks, classification and regression tree, and logistic regression. Finally, the accuracy of the four models was evaluated and compared using the accuracy statistic methods and the receiver operating characteristic (ROC). The results of accuracy analysis showed that the SVM model performed the best. At the same time, the SVM performance behavior for susceptibility modelling in other areas were collected. In these regions, the accuracy of SVM was always larger than 0.8. We could see that SVM performed acceptably in different regions, and thus it can be used as a recommended model in TGRA and other landslide-prone regions.In this study area, a total of 62% of the landslides were within 300 m from the Yangtze River, and the distance to rivers was the most important factor. The impoundment of the TGRA impacted the landslide development in three aspects: (1) the long-term immersion of reservoir water gradually reducing the strength of rock (soil) at the saturated zone (mostly near the Yangtze river), reducing the resistance force of landslide; (2) the strong dynamic action of water enhancing the lateral erosion on the bank slope, changing the slope shape, and thus reducing the slope stability; (3) the periodic fluctuation of the reservoir water making the self-weight, static, and dynamic water pressure of the landslide change, which could increase the resistance force or reduce the sliding force of the landslide and even cause overall instability and damage. Hence, in order to reduce the losses caused by landslides in TGRA, we should pay more attention to the early warning of reservoir bank landslides.

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Evaluating Deep Learning models for predicting ALK-5 inhibition

PLoS ONE ◽

10.1371/journal.pone.0246126 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0246126

Author(s):

Gabriel Z. Espinoza ◽

Rafaela M. Angelo ◽

Patricia R. Oliveira ◽

Kathia M. Honorio

Keyword(s):

Neural Network ◽

Biological Activity ◽

Deep Learning ◽

Deep Neural Network ◽

External Validation ◽

Machine Learning Techniques ◽

Coefficient Of Determination ◽

Support Vector ◽

Learning Models ◽

Alk 5

Computational methods have been widely used in drug design. The recent developments in machine learning techniques and the ever-growing chemical and biological databases are fertile ground for discoveries in this area. In this study, we evaluated the performance of Deep Learning models in comparison to Random Forest, and Support Vector Regression for predicting the biological activity (pIC50) of ALK-5 inhibitors as candidates to treat cancer. The generalization power of the models was assessed by internal and external validation procedures. A deep neural network model obtained the best performance in this comparative study, achieving a coefficient of determination of 0.658 on the external validation set with mean square error and mean absolute error of 0.373 and 0.450, respectively. Additionally, the relevance of the chemical descriptors for the prediction of biological activity was estimated using Permutation Importance. We can conclude that the forecast model obtained by the deep neural network is suitable for the problem and can be employed to predict the biological activity of new ALK-5 inhibitors.

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Evaluation of Different Machine Learning Models and Novel Deep Learning-based Algorithm for Landslide Susceptibility Mapping

10.21203/rs.3.rs-720898/v1 ◽

2021 ◽

Author(s):

Tingyu Zhang ◽

Huanyuan Wang ◽

Tianqing Chen ◽

Zenghui Sun ◽

Tao Wang ◽

...

Keyword(s):

Deep Learning ◽

Landslide Susceptibility ◽

Reference Model ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Training Dataset ◽

Validation Dataset ◽

Support Vector ◽

Operating Characteristics ◽

Predisposing Factor

Abstract The losses and damage caused by landslides are countless in the world every year. However, the existing approaches of landslide susceptibility mapping cannot fully meet the requirement of landslide prevention, and further excavation and innovation are also needed. Therefore, the main aim of this study is to develop a novel deep learning model namely landslide net (LSNet) to assess the landslide susceptibility in Hanyin County, China, meanwhile, support vector machine model (SVM) and kernel logistic regression model (KLR) were employed as reference model. The inventory map was generated based on 259 landslides, the training dataset and validation dataset were respectively prepared using 70% landslides and the remaining 30% landslides. The variance inflation factor (VIF) was applied to optimize each landslide predisposing factor. Three benchmark indices were used to evaluate the result of susceptibility mapping and area under receiver operating characteristics curve (AUROC) was used to compare the models. Result demonstrated that although the processing speed of LSNet model is the slowest, it still significantly outperformed its corresponding benchmark models with validation dataset, and has the highest accuracy (0.950), precision (0.951), F1 (0.951) and AUROC (0.941), which reflected excellent predictive ability in some degree. The achievements obtained in this study can improve the rapid response capability of landslide prevention for Hanyin County.

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A Deep Learning Algorithm for the Max-Cut Problem Based on Pointer Network Structure with Supervised Learning and Reinforcement Learning Strategies

Mathematics ◽

10.3390/math8020298 ◽

2020 ◽

Vol 8 (2) ◽

pp. 298 ◽

Cited By ~ 2

Author(s):

Shenshen Gu ◽

Yue Yang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Reinforcement Learning ◽

Combinatorial Optimization ◽

Supervised Learning ◽

Learning Strategies ◽

Large Scale ◽

Deep Neural Network ◽

Max Cut Problem ◽

Cut Problems

The Max-cut problem is a well-known combinatorial optimization problem, which has many real-world applications. However, the problem has been proven to be non-deterministic polynomial-hard (NP-hard), which means that exact solution algorithms are not suitable for large-scale situations, as it is too time-consuming to obtain a solution. Therefore, designing heuristic algorithms is a promising but challenging direction to effectively solve large-scale Max-cut problems. For this reason, we propose a unique method which combines a pointer network and two deep learning strategies (supervised learning and reinforcement learning) in this paper, in order to address this challenge. A pointer network is a sequence-to-sequence deep neural network, which can extract data features in a purely data-driven way to discover the hidden laws behind data. Combining the characteristics of the Max-cut problem, we designed the input and output mechanisms of the pointer network model, and we used supervised learning and reinforcement learning to train the model to evaluate the model performance. Through experiments, we illustrated that our model can be well applied to solve large-scale Max-cut problems. Our experimental results also revealed that the new method will further encourage broader exploration of deep neural network for large-scale combinatorial optimization problems.

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A Deep Learning Model for Fault Diagnosis with a Deep Neural Network and Feature Fusion on Multi-Channel Sensory Signals

Sensors ◽

10.3390/s20154300 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4300 ◽

Cited By ~ 2

Author(s):

Qing Ye ◽

Shaohu Liu ◽

Changhua Liu

Keyword(s):

Neural Network ◽

Deep Learning ◽

Deep Neural Network ◽

Feature Fusion ◽

Support Vector ◽

Diagnostic Model ◽

Sensory Signals ◽

Sensory Signal ◽

Deep Architecture ◽

Final Drive

Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.

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Landslide Susceptibility Mapping Combining Information Gain Ratio and Support Vector Machines: A Case Study from Wushan Segment in the Three Gorges Reservoir Area, China

Applied Sciences ◽

10.3390/app9224756 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4756 ◽

Cited By ~ 3

Author(s):

Lanbing Yu ◽

Ying Cao ◽

Chao Zhou ◽

Yang Wang ◽

Zhitao Huo

Keyword(s):

Landslide Susceptibility ◽

Information Gain ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Three Gorges Reservoir Area ◽

Support Vector ◽

The Three Gorges ◽

Vector Machines ◽

Information Gain Ratio

Landslides are destructive geological hazards that occur all over the world. Due to the periodic regulation of reservoir water level, a large number of landslides occur in the Three Gorges Reservoir area (TGRA). The main objective of this study was to explore the preference of machine learning models for landslide susceptibility mapping in the TGRA. The Wushan segment of TGRA was selected as a case study. At first, 165 landslides were identified and a total of 14 landslide causal factors were constructed from different data sources. Multicollinearity analysis and information gain ratio (IGR) model were applied to select landslide causal factors. Subsequently, the landslide susceptibility mapping using the calculated results of four models, namely, support vector machines (SVM), artificial neural networks (ANN), classification and regression tree (CART), and logistic regression (LR). The accuracy of these four maps were evaluated using the receive operating characteristic (ROC) and the accuracy statistic. Results revealed that eliminating the inconsequential factors can perhaps improve the accuracy of landslide susceptibility modelling, and the SVM model had the best performance in this study, providing strong technical support for landslide susceptibility modelling in TGRA.

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