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Landslides are one of the most common geological disasters in China, characterized by suddenness and uncertainty. Traditional methods are not sufficient for the accurate identification, early warning, and forecasting of landslide disasters. As high-resolution remote sensing satellites and interferometric synthetic aperture radar (InSAR) surface deformation monitoring technology have been leaping forward, the traditional methods of landslide monitoring data sources are limited, and there have been few effective methods to excavate the characteristics of the spatial distribution of landslide hazards and their triggering factors, etc. In this study, an area extending 10 km from the VII isobar of the Gengma earthquake was taken as the study area, and 13 evaluation factors were screened out by integrating the factors of InSAR surface deformation, topography, and geological environment. Landslide susceptibility was evaluated through the Bayesian optimized convolutional neural network (BO-CNN), and the Bayesian optimized random forests (BO-RF) and particle swarm optimization support vector machines (PSO-SVM) models were selected for comparative analyses. The accuracy of the model was evaluated by using three indices, including the ROC curve, the AUC value, and the FR value. Specifically, the ROC curves of PSO-SVM, BO-RF, and BO-CNN were close to the upper-left corner, indicating excellent model performance. Moreover, the AUC values were computed as 0.9388, 0.9529, and 0.9535, respectively, and the FR value of landslides in the high susceptibility area of BO-CNN reached up to 14.9 and exceeded those of PSO-SVM and BO-RF, respectively. Furthermore, the mentioned values of the SVM and BO-RF models were 4.55 and 3.69 higher. The experimental results indicated that, compared with other models, the BO-CNN model used in this study had a better effect on landslide susceptibility evaluation, and the research results are of great significance to the disaster prevention and mitigation measures of local governments. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we first analyze the 5 most cited papers with the title containing “Urban ecosystem health” in Chinese academic journals, and 5 newer papers retrieved from the CSSCI (Chinese Social Sciences Citation Index). The results show that the number of indicators to be used together in more than three papers is 28, and then we select 27 of them to assess Beijing and Shanghai's urban ecosystem health from 2000 to 2011. Secondly, when we standardize the original data, the worst value adjustment coefficient is introduced innovatively. Thirdly, using the entropy method, the weights of concrete indicators of Beijing and Shanghai in the different adjustment coefficients are calculated respectively. Fourthly, based on the fuzzy matter-element method, using the Hamming approach degree, the two cities' ecosystem health index and the contribution value to overall health index from each component are calculated. Lastly, using gray prediction model, the evolutionary time response sequence function of Beijing and Shanghai's urban ecosystem health index is identified, and thus both cities' urban ecosystem health is predicted. [Copyright &y& Elsevier]

The article provides information on the endangered fish Brachymystax lenok, popularly known as Lenok. It notes that lenok inhabits in Amur River, which prefers swift currents and clear water over large gravel substrates, and consumes larval and adult insects as well as small fishes and frogs. It mentions that overfishing, water pollution, and environmental changes threatens lenok . It also suggests to implement a recovery plan to conserve and protect Brachymystax lenok.