Landslide hazard susceptibility evaluation based on SBAS-InSAR technology and SSA-BP neural network algorithm:A case study of Baihetan Reservoir Area

Landslide hazard susceptibility evaluation takes on critical significance in early warning and disaster prevention and reduction.In order to solve the problems of poor effectiveness of landslide data and complex calculation of weights for multiple evaluation factors in the existing landslide susceptibility evaluation models,in this study,a method of landslide hazard susceptibility evaluation is proposed by combining SBAS-InSAR(Small Baseline Subsets-Interferometric Synthetic Aperture Radar)and SSA-BP(Sparrow Search Algorithm-Back Propagation)neural network algorithm.The SBAS-InSAR technology is adopted to identify potential landslide hazards in the study area,update the cataloging data of landslide hazards,and 11 evaluation factors are chosen for constructing the SSA-BP model for training and validation.Baihetan Reservoir area is selected as a case study for validation.As indicated by the results,the application of SBAS-InSAR technology,combined with both ascending and descending orbit data,effectively addresses the incomplete identification of landslide hazards caused by geometric distortion of single orbit SAR data(e.g.,shadow,overlay,and perspective contraction)in deep canyon areas,thereby enabling the acquisition of up-to-date landslide hazard data.Moreover,in comparison to the conventional BP(Back Propagation)algorithm,the accuracy of the model constructed by the SSA-BP algorithm exhibits a significant increase,with mean squared error and mean absolute error reduced by 0.0142 and 0.0607,respectively.Additionally,during the process of susceptibility evaluation,the SSA-BP model effectively circumvents the issue of considerable manual interventions in calculating the weight of evaluation factors.The area under the curve of this model reaches 0.909,surpassing BP(0.835),random forest(0.792),and the information value method(0.699).The risk of landslide occurrence in the Baihetan Reservoir area is positively correlated with slope,surface temperature,and deformation rate,while it is negatively correlated with fault distance and normalized difference vegetation index.Geological lithology exerts minimal influence on the occurrence of landslides,with the risk being low in forest land and high in grassland.The method proposed in this study provides a useful reference for disaster prevention and mitigation departments to perform landslide hazard susceptibility evaluations in deep canyon areas under complex geological conditions.

Susceptibility weighted imaging in the evaluation of hemorrhagic diffuse axonal injury

Diffuse axonal injury（DAI）is axonal and small vessel injury produced by a sudden acceleration of the head by an external force,and is a major cause of death and severe disability（Paterakis et al.,2000）.Prognosis is poorer in patients with apparent hemorrhage than in those without（Paterakis et al.,2000）.Therefore,it is important to identify the presence and precise position of hemorrhagic foci for a more accurate diagnosis.CT and magnetic resonance imaging（MRI）have long been applied in the diagnosis of DAI, but they are not sensitive enough for the detection of small hemorrhagic foci, and cannot meet the requirements for early diagnosis. A major advance in MRI has been the development of susceptibility weighted imaging （SWI）, which has greatly increased the ability to detect small hemorrhagic foci after DAI （Ashwal et al., 2006）.

ArcGIS-based evaluation of geo-hazards at Yaozhou County,Shaanxi,China

In conventional susceptibility evaluation of geo-hazards,there are some limits,such as unreasonable division of evaluated region,difficulty in quantifying evaluation indicators,time-consuming calculation.To address these problems,we try to employ the software ArcGIS to evaluate geo-hazards susceptibility.The study area of Yaozhou County is automatically divided into 3562 units.Based on the spatial overlay analysis function of ArcGIS,quantitative evaluation of geo-hazards susceptibility is implemented in the study area,and the geo-hazards susceptibility zoning is mapped.It is observed that the evaluation results match well with field investigations.

Evaluation of the early defoliation trait and identification of resistance genes through a comprehensive transcriptome analysis in pears

Early defoliation,which usually occurs during summer in pear trees,is gradually becoming a major problem that poses a serious threat to the pear industry in southern China.However,there is no system for evaluating the responses of different cultivars to early defoliation,and our knowledge of the potential molecular regulation of the genes underlying this phenomenon is still limited.In this study,we conducted field investigations of 155 pear accessions to assess their resistance or susceptibility to early defoliation.A total of 126 accessions were found to be susceptible to early defoliation,and only 29 accessions were resistant.Among them,19 resistant accessions belong to the sand pear species(Pyrus pyrifolia).To identify the resistance genes related to early defoliation,the healthy and diseased samples of two sand pear accessions,namely,the resistant early defoliation accession‘Whasan’and the susceptible early defoliation accession‘Cuiguan’,were used to perform RNA sequencing.Compared with‘Cuiguan’,a total of 444 genes were uniquely differentially expressed in‘Whasan’.Combined with GO and KEGG enrichment analyses,we found that early defoliation was closely related to the stress response.Furthermore,a weighted gene co-expression network analysis revealed a high correlation of WRKY and ethylene responsive factor(ERF)transcription factors with early defoliation resistance.This study provides useful resistant germplasm resources and new insights into potentially essential genes that respond to early defoliation in pears,which may facilitate a better understanding of the resistance mechanism and molecular breeding of resistant pear cultivars.

Machine Learning-Based Evaluation of Susceptibility to Geological Hazards in the Hengduan Mountains Region, China

The Hengduan Mountains Region(HMR) is one of the areas that experience the most frequent geological hazards in China. However, few reports are available that address the geological hazard susceptibility of the region.This study developed six machine learning models to assess the geological hazard susceptibility. The results show that areas with medium and high susceptibility to geological hazards as a whole account for almost 21% of the total area, while both are 18% when it comes to the single hazard of landslide and rockfall respectively. Medium and high geological hazard susceptibility is found in three parts of the HMR with different characteristics:(1)the central and southern parts, where the population of the region concentrates;(2) the northern part, where higher geological hazard susceptibility is found along the mountain ranges;and(3) the junction of Tibet, Yunnan, and Sichuan in the eastern part, which is prone to larger-scale geological hazards. Of all the potential influencing factors,topographic features and climatic variables act as the major driving factors behind geological hazards and elevation,slope, and precipitation are crucial indicators for geological hazard susceptibility assessment. This study developed the geological hazard susceptibility maps of the HMR and provided information for the multi-hazard risk assessment and management of the region.