Landslide is a serious natural disaster next only to earthquake and flood, which will cause a great threat to people’s lives and property safety. The traditional research of landslide disaster based on experience-driven or statistical model and its assessment results are subjective , difficult to quantify, and no pertinence. As a new research method for landslide susceptibility assessment, machine learning can greatly improve the landslide susceptibility model’s accuracy by constructing statistical models. Taking Western Henan for example, the study selected 16 landslide influencing factors such as topography, geological environment, hydrological conditions, and human activities, and 11 landslide factors with the most significant influence on the landslide were selected by the recursive feature elimination (RFE) method. Five machine learning methods [Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Linear Discriminant Analysis (LDA)] were used to construct the spatial distribution model of landslide susceptibility. The models were evaluated by the receiver operating characteristic curve and statistical index. After analysis and comparison, the XGBoost model (AUC 0.8759) performed the best and was suitable for dealing with regression problems. The model had a high adaptability to landslide data. According to the landslide susceptibility map of the five models, the overall distribution can be observed. The extremely high and high susceptibility areas are distributed in the Funiu Mountain range in the southwest, the Xiaoshan Mountain range in the west, and the Yellow River Basin in the north. These areas have large terrain fluctuations, complicated geological structural environments and frequent human engineering activities. The extremely high and highly prone areas were 12043.3 km2 and 3087.45 km2, accounting for 47.61% and 12.20% of the total area of the study area, respectively. Our study reflects the distribution of landslide susceptibility in western Henan Province, which provides a scientific basis for regional disaster warning, prediction, and resource protection. The study has important practical significance for subsequent landslide disaster management. 相似文献
The pathogenic species of genus Vibrio cause vibriosis, one of the most prevalent diseases of maricultured animals and seafood consumers. Monitoring their kinetics in the chain of seafood production, processing and consumption is of great importance for food and mariculture safety. In order to enrich Vibrio-representing 16S ribosomal RNA gene (rDNA) fragments and identify these bacteria further real-timely and synchronously among bacterial flora in the chain, a pair of primers that selectively amplify Vibrio 16S rDNA fragments were designed with their specificities and coverage testified in the analysis of seawater Vibrio community. The specificities and coverage of two primers, VF169 and VR744, were determined theoretically among bacterial 16S rDNAs available in GenBank by using BLAST program and practically by amplifying Vibrio 16S rDNA fragments from seawater DNA. More than 88.3% of sequences in GenBank, which showed identical matches with VR744, belong to Vibrio genus. A total of 33 clones were randomly selected and sequenced. All of the sequences showed their highest similarities to and clustered around those of diverse known Vibrio species. The primers designed are capable of retrieving a wide range of Vibrio 16S rDNA fragments specifically among bacterial flora in seawater, the most important natural environment of seafood cultivation. 相似文献
China Ocean Engineering - Numerical simulations of evolution characteristics of slug flow across a 90° pipe bend have been carried out to study the fluid—structure interaction response... 相似文献