The surface water in the lakeshore zone is the primary area where cyanobacteria bloom floats intensively. In lake water environment monitoring, it has become pressing to accurately identify the distribution and accumulation coverage area of cyanobacteria blooms in the surface water of the lakeshore zone. This study proposes a real-time and dynamic monitoring technology for cyanobacteria blooms in surface water using a shore-based camera monitoring network. The specific work is as follows: Chaohu Lake, a large eutrophic lake in China, is selected as the research object. The multithreading technology is used to dynamically obtain the hourly video images of 43 cameras around Chaohu Lake. The semantic segmentation method is used to identify the cyanobacteria blooms in the video images, calculate the coverage of cyanobacteria blooms, and draw the spatial distribution map of cyanobacteria blooms in the lakeshore zone of Chaohu Lake. To improve the accuracy of cyanobacteria blooms recognition, we use the ResNet-50 network to integrate three semantic segmentation models, namely FCN, U-net, and DeeplabV3+. By comparing the cyanobacteria blooms results identified by the three methods, it is found that the boundary of the cyanobacteria blooms results identified by DeeplabV3+(ResNet-50) is clear, which is more consistent with the real spatial information of the distribution of cyanobacteria blooms and is more suitable for monitoring the hourly dynamic changes of cyanobacteria blooms in the Chaohu Lake lakeshore zone. The results demonstrated that the time requirement of monitoring cyanobacteria blooms in real time on an hourly basis could be met by utilizing technology that uses multiple threads. The OA (Overall Accuracy), MPA (Mean Pixel Accuracy), IOU (Intersection Over Union) of cyanobacteria blooms, and the IOU of water values of the DeeplabV3+(ResNet-50) were the highest, which were 0.83, 0.82, 0.71, and 0.74, and the RMSE between the predicted and real cyanobacterial blooms coverage of 43 cameras was 6.65%. The above values show that DeeplabV3+(ResNet-50) is this technology’s most suitable semantic segmentation model. This technique can provide technical support for the scientific development of a cyanobacteria blooms management plan in the lakeshore zone of Chaohu Lake by calculating the coverage area of cyanobacteria blooms and drawing the spatial distribution map of cyanobacteria blooms in the lakeshore zone.

Crowd flow prediction plays a vital role in modern city management and public safety prewarning. However, the existing approaches related to this topic mostly focus on single sites or road segments, and indoor regional crowd flow prediction has yet to receive sufficient academic attention. Therefore, this paper proposes a novel prediction model, named the spatial–temporal attention-based crowd flow prediction network (STA-CFPNet), to forecast the indoor regional crowd flow volume. The model has four branches of temporal closeness, periodicity, tendency and external factors. Each branch of this model takes a convolutional neural network (CNN) as its principal component, which computes spatial correlations from near to distant areas by stacking multiple CNN layers. By incorporating the output of the four branches into the model’s fusion layer, it is possible to utilize ensemble learning to mine the temporal dependence implicit within the data. In order to improve both the convergence speed and prediction performance of the model, a building block based on spatial–temporal attention mechanisms was designed. Furthermore, a fully convolutional structure was applied to the external factors branch to provide globally shared external factors contexts for the research area. The empirical study demonstrates that STA-CFPNet outperforms other well-known crowd flow prediction methods in processing the experimental datasets.

Snow disasters are severe natural hazards in Jilin Province, China. This research formulated a holistic risk assessment framework which integrates the factors of hazardousness, sensitivity, vulnerability, and disaster prevention and mitigation capacity. Using multi-source data and Geographic Information System (GIS), we evaluated snow disaster risks through 15 selected indicators weighted by the entropy method. Results show high-risk areas primarily concentrated in southeastern mountainous regions, including Jingyu, Fusong, Changbai, and Linjiang. Disaster sensitivity decreases from southeast to northwest, while high-vulnerability zones appear in central urban areas like Changchun. The comprehensive risk generally declines from the southeastern mountains to the northwestern plains. This assessment model effectively reflects snow disaster formation mechanisms and spatial patterns, providing scientific support for disaster prevention and emergency management in Jilin Province.