Spatial Predictions of Debris Flow Susceptibility Mapping Using Convolutional Neural Networks in Jilin Province, China

Abstract

Debris flows are a major geological disaster that can seriously threaten human life and physical infrastructures. The main contribution of this paper is the establishment of two–dimensional convolutional neural networks (2D–CNN) models by using SAME padding (S–CNN) and VALID padding (V–CNN) and comparing them with support vector machine (SVM) and artificial neural network (ANN) models, respectively, to predict the spatial probability of debris flows in Jilin Province, China. First, the dataset is randomly divided into a training set (70%) and a validation set (30%), and thirteen influencing factors are selected to build the models. Then, multicollinearity analysis and gain ratio methods are used to quantify the predictive ability of factors. Finally, the area under the receiver operatic characteristic curve (AUC) and statistical methods are utilized to measure the accuracy of the models. The results show that the S–CNN model gets the highest AUC value of 0.901 in the validation set, followed by the SVM model, the V–CNN model, and the ANN model. Three statistical methods also show that the S–CNN model produces minimum errors compared with other models. The S–CNN model is hailed as an important means to improve the accuracy of debris–flow susceptibility mapping and provides a reasonable scientific basis for critical decisions.

Landslide Detection from Open Satellite Imagery Using Distant Domain Transfer Learning

“…Regarding the CNN model and the pretrained model, we used the Longgang landslide dataset, which only contains 177 landslide images, to train the classification model, and we divided the whole landslide dataset into a training set and validation set according to the ratio of 7:3, as in previous research [1,71]. After multiple training steps, we obtained the training results.…”

“…The convolutional neural network is widely used to solve image classification problems and is one of the most popular deep learning algorithms [18]. Compared with traditional machine learning algorithms, it has outstanding ability in image classification because of the convolution layers and subsampling layers, which can effectively extract the useful feature maps of images [64,[69][70][71]. As shown in Table 2, DDTL achieved the highest accuracy (88.01%) for the classification task, but the CNN model only achieved 86.16% classification accuracy because the training data were insufficient [18].…”

Dense neural network outperforms other machine learning models for scaling-up lichen cover maps in Eastern Canada

“…The training tiles were passed through a data augmentation step, which randomly flipped and rotated tiles to expose the model to different orientations. To reduce the number of features, the CNNs in this study use valid padding strategies to discard the data at the edges of the tile [19,45]. Valid padding ensures that convolutional filters are only applied to values in the tile and that the height and width of the output tile is eroded [19,45,46].…”

“…To reduce the number of features, the CNNs in this study use valid padding strategies to discard the data at the edges of the tile [19,45]. Valid padding ensures that convolutional filters are only applied to values in the tile and that the height and width of the output tile is eroded [19,45,46]. Same padding maintains the same output tile size as the input by adding zeros to the edges and is used to maintain the size of the feature…”

Improving debris flow evacuation alerts in Taiwan using machine learning

“…Data-driven machine-learning models are increasingly employed in improving early warning models, weather and natural hazard forecasts, and disaster evacuation management. Examples include weather forecasting 20,21 , landslide displacement prediction 22 , spatial mapping of debris flow susceptibility [23][24][25][26] , predicting scales of landslides 27 and monthly rainfall for early warning of landslide occurrence 28 , differentiating between ground vibrations generated by debris flows and other seismic signals 29 , and enhancing disaster response and emergency evacuation planning [30][31][32][33] . However, to the best of our knowledge, none of the existing studies predict the occurrences of debris flows within a selected time using machine learning algorithms trained on historical hourly rainfall data alone.…”