Zongjia Zhang

The increasing impact of urban floods, driven by global climate change and the growing frequency of extreme weather events, poses significant threats to public safety, disrupts infrastructure, and hampers economic development. This paper presents a two-stage model for shortest path planning and dynamic dispatching of rescue forces (firefighters and fire engines) in response to urban floods caused by extreme rainfall. In the first stage, a path selection model for rescue vehicles is developed, supported by an efficient customized A* algorithm to determine worst-case travel times from fire stations to flood sites. A preference-based version of the algorithm is also introduced, incorporating driver preferences into path selection. In the second stage, rescue forces are dynamically allocated based on demand at flooded locations, which is estimated using population density and real-time flood depth data. The travel times derived in the first stage serve as inputs to a bi-objective dynamic dispatch model that utilizes real-time flood data to optimize emergency response. By integrating path planning with rescue force dispatching, this study provides essential support for effective flood response operations.

The impact of Urban flood is expanding as global climate change intensifies and extreme weather events increase. It not only poses a threat to the safety of citizens, but also has adverse impacts on the infrastructure and economic development of cities. In this paper, a two-stage modelling approach for shortest path planning and dynamic dispatching of rescue forces (firefighters and fire engines) is proposed for urban flood caused by extreme heavy rainfall. The first stage is to acquire the worst-case optimal travel times from fire stations to local flooding sites. A path selection model for rescue vehicles in a flood is developed and a customized A* algorithm with high search efficiency is created. In addition, a preference-based customized A* algorithm that takes into account travel path preferences is also designed to select paths that consider driver preferences. The second stage is to dynamically dispatch rescue forces to flooded locations. The demand for firefighters at each flood location is estimated based on the population density and the detected real-time flood depth. The travel times obtained in the first stage are used as inputs to a bi-objective dynamic dispatch model, which innovatively incorporates real-time flood monitoring data to make emergency dispatch responses accordingly. This paper jointly considers the path planning and rescue force dispatching, providing rapid auxiliary flood response support for decision makers.

The complex formation mechanism and numerous influencing factors of urban waterlogging disasters make the identification of their risk an essential matter. This paper proposes a framework for identifying urban waterlogging risk that combines multi-source data fusion with hydrodynamics (MDF-H). The framework consists of a source data layer, a model parameter layer, and a calculation layer. Using multi-source data fusion technology, we processed urban meteorological information, geographic information, and municipal engineering information in a unified computation-oriented manner to form a deep fusion of a globalized multi-data layer. In conjunction with the hydrological analysis results, the irregular sub-catchment regions are divided and utilized as calculating containers for the localized runoff yield and flow concentration. Four categories of source data, meteorological data, topographic data, urban underlying surface data, and municipal and traffic data, with a total of 12 factors, are considered the model input variables to define a real-time and comprehensive runoff coefficient. The computational layer consists of three calculating levels: total study area, sub-catchment, and grid. The surface runoff inter-regional connectivity is realized at all levels of the urban road network when combined with hydrodynamic theory. A two-level drainage capacity assessment model is proposed based on the drainage pipe volume density. The final result is the extent and depth of waterlogging in the study area, and a real-time waterlogging distribution map is formed. It demonstrates a mathematical study and an effective simulation of the horizontal transition of rainfall into the surface runoff in a large-scale urban area. The proposed method was validated by the sudden rainstorm event in Futian District, Shenzhen, on 11 April 2019. The average accuracy for identifying waterlogging depth was greater than 95%. The MDF-H framework has the advantages of precise prediction, rapid calculation speed, and wide applicability to large-scale regions.