Search

The modeling process and uncertainty of different machine learning models to predict landslide susceptibility are different. High-precision assessment of post-seismic landslide susceptibility is the key to effective disaster prevention and mitigation. This paper established the post-seismic landslide database of the Jiuzhaigou earthquake, and 12 causative factors were finally selected for landslide susceptibility modeling by eliminating two factors from 14 initial landslide factors through factor covariance diagnosis. Then selects logical regression（LR）, support vector machine（SVM）, random forest（RF）, and artificial neural network（ANN） machine learning models and 10-fold cross-validation data sampling methods. Finally, the receiver operating characteristic curve and the distribution characteristics of the susceptibility index（mean and standard deviation） are used to discuss the modeling of landslide susceptibility based on machine learning and its uncertainty. The results show that the high susceptibility areas of landslides after the earthquake are mainly developed and distributed along the epicenter and valleys. The AUC values of the four machine learning models all exceed 0.87, which has achieved good applicability for predicting potential post-seismic landslides in the study area. Among them, the mean value and standard deviation of the susceptibility index of SVM are small, indicating that the model has good recognition ability for landslides, and it has the highest prediction accuracy(CVmean=0.925), whereas the other models are ANN（0.920）, LR（0.894） and RF（0.877）. The results of this paper are of great significance for popularizing and promoting the development of machine-learning models in other regions. [ABSTRACT FROM AUTHOR]

Displacement prediction has been one of the hot spots in landslide studies. In recent years, with the development of computer science, artificial intelligent models have been increasingly used in displacement prediction of landslide. Compared with the commonly used machine learning methods like LSTM neural network, the XGBoost（eXtreme Gradient Boosting） model in integrated algorithms is not common in landslide displacement prediction. However, it has become a hot field of research in academia because it has higher prediction accuracy and faster operating speed in the prediction. Taking the monitoring data of the landslide disaster site in Yaoshan Village, Anxi County, Quanzhou, Fujian as an example, Python is used to build the XGBoost model in this paper. By comparing the maximum information coefficients, the feature with a higher correlation to the displacement is selected and put into the model for landslide displacement prediction. The results show as follows: the XGBoost model can predict rainfall-induced step-like landslide displacement more accurately than most machine learning models. This is because it has advantages such as introducing a regular term in the objective function to control model overfitting, and using forward validation for model dataset partitioning. Meanwhile, it is of important significance for the displacement prediction, monitoring and early warning of such landslides. [ABSTRACT FROM AUTHOR]

The traditional landslide identification is mainly through remote sensing visual interpretation and human-computer interaction identification, which has the problems of time-consuming and laborious, subjective and low extraction accuracy. In this paper, a highway in Fujian Province is taken as the experimental area, and a rapid landslide identification method based on high-resolution remote sensing data for mountainous roads in southeast Fujian is proposed. Firstly, by analyzing and studying landslide features such as hue, topography, spectrum, vegetation index and texture using sub-meter high-resolution remote sensing images, the multi-dimensional multi-scale landslide classification and identification rules applicable to the mountainous roads in southeast Fujian are established. Secondly, a road landslide identification model is constructed by combining the classification tool of machine learning algorithm with the multi-dimensional multi-scale feature screening set, and the preliminary identification of landslides based on high-resolution remote sensing images in terms of hue. Finally, the landslides are further segmented and extracted by slope, normalized vegetation index and texture feature screening set, so as to accurately identify the spatial distribution of landslides on mountainous roads. The average accuracy of landslide identification by this method reaches 85.73%, and the morphological features extracted from the landslides are complete, which can clearly show the "tongue" and "dustpan" shape of the landslides, and also clearly identify the slide walls and piles of the landslides. The research results can provide scientific reference for landslide identification and risk assessment of mountainous traffic routes in vegetation development areas in China. [ABSTRACT FROM AUTHOR]

Qualitative safety evaluation and analysis can not meet individual safety requirements gradually. In order to better quantify the degree of risk in life and find the quantitative relationship between outliers and individual risk, this paper uses logical regression method to analyze the specific dependencies between independent variables and dependent variables. Taking a cement plant in Henan Province as the investigation object, an emergency early warning model based on logistic regression method is established. The linear relationship between independent variables and accident consequences is calculated and analyzed by SAS software, and compared with cluster analysis method. The results show that compared with clustering analysis, logistic regression model has better accuracy in monitoring outliers, which provides a good analysis basis for individual risk warning and accident prevention. [ABSTRACT FROM AUTHOR]

Landslide disasters threaten the safety of human life and properties. It is important to accurately assess the risk of regional landslides. At present, common machine learning methods cannot extract deeper data features and obtain higher precision landslide hazard assessment maps. Therefore, this paper proposes a deep one-dimensional convolutional neural network method for landslide risk assessment, in order to extract deeper features and obtain higher precision evaluation results by based on its powerful learning ability. Taking Lushan, Sichuan, China as an example, 346 historical landslide pionts of Lushan in 2016 were selected for model training and verification. In order to verify the validity and precision of the model, the support vector machine, the information value method and the proposed convolutional neural network model were compared in the experiments. The overall accuracies of the convolutional neural network and the support vector machine are 0. 9015 and 0. 842. The AUC values of the one-dimensional convolutional neural network, support vector machine, and information value model are 0. 888, 0. 876, and 0. 818, respectively, The results show that the proposed convolutional neural network model has a higher accuracy and is superior to the support vector machine and the information value method in data fitting ability and evaluation effect, and can be effectively applied to the regional landslide risk evaluation. [ABSTRACT FROM AUTHOR]