Urban floods research has been attracting extensive attention with the increasing threat of flood risk and environmental hazards due to global climate change and urbanization. However, there is rarely a comprehensive review of this field and it remains unclear how the research topics on urban floods have evolved. In this study, we analyzed the development of urban floods research and explored the hotspots and frontiers of this field by scientific knowledge mapping. In total, 3314 published articles from 2006 to 2021 were analyzed. The results suggest that the number of published articles in the field of urban floods generally has an upward trend year by year, and the research focus has shifted from exploring hydrological processes to adopting advanced management measures to solve urban flood problems. Moreover, urban stormwater management and low impact development in the context of climate change and urbanization have gradually become research hotspots. Future research directions based on the status and trends of the urban floods field were also discussed. This research can not only inspire other researchers and policymakers, but also demonstrates the effectiveness of scientific knowledge mapping analysis by the use of the software CiteSpace and VOSviewer.

The construction industry has a significant impact on the environment, and green buildings provide an effective means of reducing environmental impact. Green finance can facilitate industrial transformation and upgrading, reduce construction costs, attract investment, and promote green industry development. However, traditional financing models still dominate the financing of green building projects, even if they are not in line with the core principles of green building. To solve this problem, this paper establishes a three-party evolutionary game model, which, based on the assumption of finite rationality, considers three core stakeholders: bank and financial institutions, developers, and consumers. The conditions for evolutionary stable equilibrium are identified through an equilibrium analysis of the strategic behavior of each subject, followed by a replication dynamic analysis. Simulation analysis was used to study the effects of key parameters on cooperative behavior and evolution. The results of the study show that (1) The high initial willingness of bank and financial institutions and consumers has a significant driving effect on the market. (2) When the market willingness is high, the government can steer the market towards the desired goal at relatively low policy costs. (3) With the support of green finance, green buildings can still flourish even if the return is slightly lower than traditional building projects. The results of the study reveal cooperative behavior and its evolutionary patterns, which help stakeholders coordinate their goals and promote the development of the green building industry.

To address the problems of traditional hydrological and hydraulic methods of estimating disasters in urban flood areas, such as small scale, poor timeliness, and difficulty of obtaining data, an inversion method of estimating urban flood disaster area based on remote sensing spectroscopy is proposed. In this paper, the spatial distribution of urban flood disasters is first inverted based on large-scale multidimensional remote sensing spectral orthography. Then, spatial coupling inversion of the remote sensing spectrum-urban economy-flood disaster is performed by simulating the urban economic density through single spectral remote sensing at night. Finally, losses at the urban flood area are estimated. The results show that (1) the heavy rain in Henan Province on 20 July is centered in Zhengzhou, and the spatial distribution of urban flood disasters accords with Zipf’s law; (2) the estimated damage to the urban flood area in Henan Province is 132,256 billion yuan, and Zhengzhou has the most serious losses at 43,147 billion yuan, accounting for 32.6% of the entire province’s losses. These results are consistent with the official data (accuracy ≥ 90%, R² ≥ 0.95). This study can provide a new approach for accurately and efficiently estimating urban flood damage at a large scale.

Hydraulic engineering construction safety has become a major concern in engineering sustainability. Fall accidents, as a common type of accident during the hydraulic engineering construction process, have caused physical and fatal injuries and property losses on an individual and societal scale. With a sizable workforce, complex operational structures and demanding construction conditions, hydraulic engineering projects present more pronounced safety management challenges than other infrastructure initiatives. As a result, the risk of accidents, particularly fall accidents, is heightened in this domain. To prevent fall accidents and minimize losses, this study used the investigation reports of 389 cases of fall accidents as the analyzed corpus, and 16 contributing factors of fall accidents were extracted with the utilization of text mining. Accident feature terms were visualized through word clouds and ring bar graphs. The logical relationship among the influencing factors was quantified based on Decision-Making Trial and Evaluation Laboratory and Interpretative Structural Modeling (DEMATEL-ISM). The contributing factors and occurrence mechanism of fall accidents in hydraulic engineering were analyzed by establishing a multilevel hierarchical hybrid model. The results showed that the multilevel hierarchical hybrid model was divided into five levels. Thirteen causal chains were obtained. Chaotic security management, weak safety awareness and an inadequate safety system were the most critical factors, while the remaining eleven transitional factors and four surface factors also contributed significantly to the occurrence of accidents. Human and management factors dominated the overall factor transfer pathway. This study proposes countermeasures to the above-mentioned factors and provides a theoretical basis for the sustainable and safe construction of hydraulic engineering.

Improving the hazard identification ability of workers is an important way to reduce safety accidents at construction sites. Although previous studies have succeeded in improving hazard identification performance, an important gap is that they consider only two factors, the worker’s safety experience and objective environmental conditions, to analyze the impact on hazard identification performance. To fill the above gap, a visual cognitive model of hazard identification was established. Sixteen field scenes were selected to represent construction sites in each environmental condition. Eye-movement data were extracted through eye-tracking experiments, and the differences between experts’ and novices’ gazes during danger recognition in these scenes were analyzed. The results indicate the following: bright construction sites can significantly improve the correct recognition rate and information processing in hazard identification; tidy construction sites can improve the search efficiency and correct recognition rate of hazard identification; safety experience can improve workers’ correct recognition rates and information processing; and reducing distractions can effectively improve the correct identification rate of hazards. Overall, optimal site brightness needs to be further studied to improve the efficiency of hazard search and reduce the distraction effect. This study provides recommendations for the direction of safety training and safety management on site.

To address the problems of traditional insurance compensation methods for flood losses, such as difficulty in determining losses, poor timeliness, a complicated compensation process and moral hazard, an urban flood index insurance tiered compensation model integrating remote sensing and rainfall multi-source data was proposed. This paper first extracted the area of water bodies using the Normalized Difference Water Index and estimates the urban flood area loss based on the flood loss model of remote sensing pixels. Second, the tiered compensation mechanism triggered by rainfall was determined, and the urban flood index insurance tiered compensation model was constructed using remote sensing and rainfall multi-source data. Finally, the economic losses and flood insurance compensation in urban flood were estimated. The results show that: (1) the geo-spatial distribution of flood-affected areas by remote sensing inversion is consistent with the actual rainfall characteristics of Henan Province, China; (2) based on the flood losses model of remote sensing pixels, the estimated flood losses for Henan Province are CNY 110.20 billion, which is consistent with the official data (accuracy ≥ 90%); and (3) the proposed model has good accuracy (R² = 0.98, F = 1379.42, p < 0.05). The flood index insurance compensation in Henan Province is classified as a three-tier payout, with a total compensation of CNY 24,137 million. This paper can provide a new approach to estimate large-scale urban flood losses and the scientific design of flood index insurance products. It can also provide theoretical and technical support to many countries around the world, particularly those with underdeveloped flood insurance systems.