A hybrid method for evaluating the resilience of urban road traffic network under flood disaster: An example of Nanjing, China

The influence of microtopography to road inundation caused by extreme flood

Microtopography plays a critical role in road inundation during urban flood events. The microtopography in this paper was defined as terrain-scale features that encompass surface roughness, slope, road network and urban building layout. This paper aims to explore the mechanism of depression storage and road inundation under different microtopography. Simulations under 4 rainfall intensities (144.0- 182.88 mm/h) and 14 slope combinations (four transverse slope and five longitudinal slope) were implemented in an 800 by 70 cm local model. The correlation heat map directly reflected that longitudinal slope had higher influence on drainage than other factors. Then real topographical and hydrological data was applied to predict road inundation with five different extreme rainfall events in Jiangning District (Nanjing City, China). The microtopography characteristics of frequent inundation road were extracted, which further verified the conclusions of the local model. Results show that: the microtopography depressions drainage process could be divided into six main stages: filling stage, interaction stage, unstable drainage stage, stable flow stage, drainage stage and stage of drainage end. Water was stored on depressions of road, and the storage volume and discharge efficiency were affected by the surface relief and slope. The emergence of slope provided an altered path and power for water drainage. Only 0.3 % slope could contribute a 28.4 % to discharge efficiency. Upon comparation, the best combination for drainage was 2.0 % transverse slope with 3.0 % longitudinal slope. These findings provided meaningful insights and perspectives for urban flood hazard mitigation and were a more detailed reference for road design.

The Relationship Between the Atmospheric Environment and Road Traffic Fatalities - Shandong Province, China, 2012-2021

Introduction

This study aims to analyze the potential impact of the meteorological environment and air pollutants on road traffic fatalities.

Methods

Road traffic fatality data in Shandong Province from 2012 to 2021 were obtained from the Population Death Information Registration Management System. Meteorological and air pollutant data for the same period were collected from the U.S. National Oceanic and Atmospheric Administration and the Ecological Environment Monitoring Center of Shandong Province, China. Pearson's correlation and ridge regression were used to analyze the impact of the meteorological environment and air pollutants on road traffic fatalities.

Results

From 2012 to 2021, there were 163,863 road traffic fatality cases. The results of the ridge regression analysis showed that the daily average temperature was negatively correlated with total fatalities and passengers and positively correlated with pedestrians, nonmotorized drivers, and motorized drivers. The daily minimum temperature was negatively correlated with total fatalities and positively correlated with motorized drivers. The daily maximum temperature was positively correlated with both pedestrian and nonmotorized drivers. The daily accumulated precipitation was negatively correlated with pedestrians. Sunshine duration was positively correlated with both nonmotorized and motorized drivers. Inhalable particulate matter (PM10) and nitrogen dioxide (NO2) were positively correlated with total fatalities, pedestrians, and nonmotorized drivers. Sulfur dioxide (SO2) was positively correlated with total fatalities but negatively correlated with nonmotorized drivers, passengers, and motorized drivers.

Conclusions

Atmospheric factors associated with the occurrence of road traffic fatalities include air temperature, daily accumulated precipitation, sunshine duration, and air pollutants such as PM10, NO2, and SO2.

A combined qualitative-quantitative fuzzy method for urban flood resilience assessment in Karaj City, Iran

This study aims to analyze flood resilience (FR) in Karaj City, Iran, using a new fuzzy method which combines several qualitative and quantitative indices. The qualitative part was estimated by a questionnaire consisting of 42 questions distributed into five indices (social-cultural, economic, infrastructural-physical, organizational-institutional, and hydraulic). A fuzzy method was used for analyzing the results. To quantify the hydraulic index, a 25-year flood was simulated in the Storm Water Management Model and the flooding volume at every grid was estimated. The idea was that the flooding amount could be representative of structural FR of drainage network that cannot be evaluated through a questionnaire well. To calculate the FR of different districts, the obtained FR indices were fuzzified then aggregated. Considering that clustering can assist managers and decision makers for more effective flood risk management, a fuzzy equivalence matrix concept was used for clustering FR in the city. Friedman test showed the significance of differences between FR of every two districts. Based on the results, northwestern and southeastern districts had the highest and the lowest resilience, respectively. Although the impact of infrastructure-physical index on the FR was similar in most of the districts, the contribution of social-cultural, organizational-institutional, and hydraulic indices was significantly different. Also, districts with low scores in the infrastructure-physical, organizational-institutional, and hydraulic indices need more attention for flood risk management.

Advanced machine learning algorithms for flood susceptibility modeling - performance comparison: Red Sea, Egypt

Floods are among the most devastating environmental hazards that directly and indirectly affect people's lives and activities. In many countries, sustainable environmental management requires the assessment of floods and the likely flood-prone areas to avoid potential hazards. In this study, the performance and capabilities of seven machine learning algorithms (MLAs) for flood susceptibility mapping were tested, evaluated, and compared. These MLAs, including support vector machine (SVM), random forest (RF), multivariate adaptive regression spline (MARS), boosted regression tree (BRT), functional data analysis (FDA), general linear model (GLM), and multivariate discriminant analysis (MDA), were tested for the area between Safaga and Ras Gharib cities, Red Sea, Egypt. A geospatial database was developed with eleven flood-related factors, namely altitude, slope aspect, lithology, land use/land cover (LULC), slope length (LS), topographic wetness index (TWI), slope angle, profile curvature, plan curvature, stream power index (SPI), and hydrolithology units. In addition, 420 actual flooded areas were recorded from the study area to create a flood inventory map. The inventory data were randomly divided into training group with 70% and validation group with 30%. The flood-related factors were tested with a multicollinearity test, the variance inflation factor (VIF) was less than 2.135, the tolerance (TOL) was more than 0.468, and their importance was evaluated with a partial least squares (PLS) method. The results show that RF performed the best with the highest AUC (area under curve) value of 0.813, followed by GLM with 0.802, MARS with 0.801, BRT with 0.777, MDA with 0.768%, FDA with 0.763, and SVM with 0.733. The results of this study and the flood susceptibility maps could be useful for environmental mitigation, future development activities in the area, and flood control areas.

Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis

In recent decades, climate change is exacerbating meteorological disasters around the world, causing more serious urban flood disaster losses. Many solutions in related research have been proposed to enhance urban adaptation to climate change, including urban flooding simulations, risk reduction and urban flood-resistance capacity. In this paper we provide a thorough review of urban flood-resilience using scientometric and systematic analysis. Using Cite Space and VOS viewer, we conducted a scientometric analysis to quantitively analyze related papers from the Web of Science Core Collection from 1999 to 2021 with urban flood resilience as the keyword. We systematically summarize the relationship of urban flood resilience, including co-citation analysis of keywords, authors, research institutions, countries, and research trends. The scientometric results show that four stages can be distinguished to indicate the evolution of different keywords in urban flood management from 1999, and urban flood resilience has become a research hotspot with a significant increase globally since 2015. The research methods and progress of urban flood resilience in these four related fields are systematically analyzed, including climate change, urban planning, urban system adaptation and urban flood-simulation models. Climate change has been of high interest in urban flood-resilience research. Urban planning and the adaptation of urban systems differ in terms of human involvement and local policies, while more dynamic factors need to be jointly described. Models are mostly evaluated with indicators, and comprehensive resilience studies based on traditional models are needed for multi-level and higher performance models. Consequently, more studies about urban flood resilience based on local policies and dynamics within global urban areas combined with fine simulation are needed in the future, improving the concept of resilience as applied to urban flood-risk-management and assessment.