In this paper, based on remote sensing image processing and SWMM simulation, we evaluate the effectiveness of a coupled LIDs system for the watershed scale. The main content and contributions include: 1) the extraction and classification of historical and recent LULC data (in 1979, 1989, 1999, 2009, 2017) of the study area and an analysis of the characteristics of the LULC change; 2) a watershed-based SWMM applied to the simulation of the runoff and an analysis of the runoff change characteristics; 3) the proposal and design of three coupled LIDs scenarios which treat runoff change as evaluation metric to systematically discuss the effectiveness of LIDs in the watershed. The results show that the combination structure and scale can significantly affect the coupled LIDs effectiveness. A system with multiple LIDs is more effective than one with only single LIDs. With the increase of spatial scale, the effectiveness of the coupled LIDs gradually weakens. Our research enriches the application scale of LIDs and SWMM, and can be beneficial to the construction of the "Sponge City", storm management and urban planning.
For the protection and management of coastal ecosystems, it is crucial to monitor typical coastal objects and examine their characteristics of spatial and temporal variation. There are limitations to the conventional object-oriented and spectrum-based approaches to HSRIs interpretation. The majority of recently conducted studies on semantic segmentation based on DCNNs concentrate on improving the accuracy of single objects at local scales. The completeness, generalization, and edge accuracy of the extraction and classification of multiple objects with the complex background at regional scales still need to be improved. We created a benchmark dataset CSRSD for coastal supervision using HSRIs and GIS in this study to address the aforementioned problems. In the meantime, by combining the traditional U-Net and DeepLabV3+ feature fusion strategies, we propose a novel fully connected fusion pattern by switching to deepwise separable convolution from conventional convolution and introducing spatial dropout to create a brand new CBS module. The LFCSDN, a new lightweight fully connected spatial dropout network, has been suggested. The findings demonstrate that our constructed semantic segmentation dataset, which has produced reliable results on U-Net and DeepLabV3+, can be used as a benchmark for applications based on DCNNs for coastal scenes. While maintaining high accuracy, LFCSDN can significantly reduce the number of parameters. Our suggested CBS module can increase the model’s generalization by reducing overfitting. In order to analyze the spatiotemporal characteristics of target changes in the study area, tests on expansive remote sensing imagery were also conducted. The findings can be applied to ecological restoration, coastal area mapping, and integrated management. Additionally, it serves as a resource for studies on multiscale semantic segmentation in computer vision.
On the basis of being familiar with the traditional decision tree algorithm, some improvements are made to the C4.5 algorithm to reduce the mining time. After preliminary processing of students’ scores, the improved algorithm is used to mine the management association rules among students’ scores, and the results of association rules are analyzed and interpreted.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.