Urban waterlogging will harm economic development and people’s life safety; however, the waterlogging risk zoning map provides the necessary decision support for the management of urban waterlogging, urban development and urban planning. This paper proposes an urban waterlogging risk assessment method that combines multi-criteria decision analysis (MCDA) with a geographic information system (GIS). The framework of urban waterlogging risk assessment includes four main elements: hazard, exposure, vulnerability, and emergency response and recovery capability. Therefore, we selected the urban area of Changchun City, Jilin Province as the study area. The Analytic Hierarchy Process (AHP) is a generally accepted MCDA method, it is used to calculate the weight and generate a result map of hazards, exposure, vulnerability, and emergency responses and recovery capability. Based to the principle of natural disaster risk formation, a total of 18 parameters, including spatial data and attribute data, were collected in this study. The model results are compared with the recorded waterlogging points, and the results show that the model is more reliable.

With extreme rainfall events and rapid urbanization, urban flood disaster events are increasing dramatically. As a key flood control city in China, Changchun City suffers casualties and economic losses every year due to floods. The improvement of flood resilience has become an important means for cities to resist flood risks. Therefore, this paper constructs an assessment model of urban flood resilience from four aspects: infrastructure, environment, society and economy. Then, it quantifies infrastructure and environmental vulnerability based on GIS, and uses TOPSIS to quantify social and economic recoverability. Finally, based on k-means clustering of infrastructure and environmental vulnerability and social and economic recoverability, the flood resilience of Changchun City was evaluated. The results show that different factors have different effects on flood resilience, and cities with low infrastructure and environmental vulnerability and high socioeconomic recoverability are more resilient in the face of floods. In addition, cities in the same cluster have the same flood resilience characteristics. The proposed framework can be extended to other regions of China or different countries by simply modifying the indicator system according to different regions, providing experience for regional flood mitigation and improving flood resilience.

In this study, two districts, Erdaojiang District and Dongchang District, in Tonghua City, Jilin Province, were evaluated for their geological environment carrying capacity. A total of 14 evaluation indicators were selected from the three aspects of the geological environment, ecological environment, and social environment to make it more comprehensive to evaluate the carrying capacity of the geological environment. Using the AHP and CRITIC methods, the subjective weight and objective weight of each evaluation index are obtained, and the combined weight is calculated by game theory. When combined with the GIS and combined weights, the distribution map of the geological environment carrying capacity is obtained, and it is classified into four grades: excellent, good, medium, and poor. A comprehensive evaluation of the carrying capacity of the geological environment is carried out. The following conclusions are drawn: the overall carrying capacity of the geological environment in the study area is good, and the carrying capacity of individual areas is poor. The comparative analysis of the good and poor areas provides a scientific basis for future environmental governance and urban planning and provides a scientific basis for geological disasters and mines.

Urban land subsidence (LS) results in a reduction in ground elevation, compromising infrastructure integrity, disrupting the hydrological cycle, and posing significant risks to economic, demographic, and environmental security. This phenomenon is characterized by a certain degree of latency. In recent years, as Shanghai has undergone rapid urban expansion and high-density development, the issue of LS has become increasingly pronounced. This study employs a multi-criteria decision analysis framework, integrating advanced technologies such as Remote Sensing (RS), Google Earth Engine (GEE), and Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR), to develop a comprehensive evaluation index system comprising fifteen indicators, which consider geological, hydrological, and anthropogenic factors. By applying matrix theory, the study utilizes the Analytic Hierarchy Process (AHP) and the Entropy Weight Method (EWM) to integrate subjective and objective weights, thereby determining the comprehensive weight for each indicator. Subsequently, the comprehensive natural disaster risk theory was employed to assess the risk levels across different regions within the study area, which were visualized using ArcGIS. The study area was classified into five risk categories: very low, low, medium, high, and very high, comprising 67.00%, 17.87%, 9.25%, 3.39%, and 2.48% of the total area, respectively. The results closely align with historical cumulative subsidence data and the current LS prevention map of Shanghai, confirming the validity and efficacy of the selected indicators and evaluation methodologies. The findings suggest that the overall risk level in the study area is relatively low, with high-risk zones concentrated in densely populated and economically urbanized central districts.

As climate change intensifies, urban transportation infrastructure faces unprecedented challenges from extreme weather events, such as floods. This study investigates the resilience and vulnerability of such infrastructure under extreme rainfall conditions in Changchun City. Utilizing Multi-Criteria Decision-Making Analysis (MCDM) and Geographic Information System (GIS) techniques, we comprehensively assess the physical, functional, and service vulnerabilities of the transportation network. Our analysis reveals that only 3.57% of the area is classified as highly resilient, demonstrating effective flood management capabilities. In contrast, a significant 61.73% of the area exhibits very low resilience, highlighting substantial vulnerabilities that could impact urban operations. Based on our findings, we propose specific strategies to enhance resilience, including optimizing drainage systems, upgrading infrastructure standards, implementing green infrastructure initiatives, and integrating disaster risk factors into urban planning. These strategies and insights provide valuable references for global cities facing similar climatic challenges.