A numerical model is developed in this study with various components for simulating the complex flow phenomena in urban drainage basins. The model integrates the HEC-1 model, a 1-D dynamic channel-flow model, a 2-D non-inertia overland-flow model and the SWMM model to reflect the hydraulic processes in areas with different characteristics. The inundation of underground infrastructure during flood is also considered in the model. The typhoon Nari event in 2001, which resulted in severe flood in downtown Taipei, is simulated by the model. The result is compared with the survey records of flooded areas, which reveals the storage effect of underground infrastrucures is significant to the simulation results of highly developed urban areas.

This study reports on a numerical investigation of transport behavior of indoor airflow and size-dependent particulate matter (PM) in multi-room buildings. An indoor size-dependent PM transport approach, combining the Eulerian large-eddy simulation of turbulent flow with the Lagrangian particle trajectory tracking, was developed to investigate indoor airflow pattern and PM1/PM2.5/PM10 removal efficiency in naturally ventilated multi-room buildings. A displacement ventilation with a measured indoor PM10 profile in Taipei Metropolis as the initial condition was carried out to characterize spatial and temporal variations of indoor PM1/PM2.5/PM10 removal behavior. The effects of indoor airflow pattern on particle transport mechanisms, e.g., deposition, suspension, migration and escape, were analyzed. Two comparison scenarios, which considered the effects of no indoor partition and different air change rate, respectively, were also conducted. In comparison with the effectiveness of PM1/PM2.5/PM10 removal, the simulated results showed that coarse particles were easier to be removed out of the building than fine particles. Natural ventilation was not an effective way to remove fine particles such as PM1 and PM2.5 in a multi-room building. Indoor partitions can impede 12% of the mean streamwise velocities and significantly increase 30-50% turbulence intensities. However, indoor partitions increased particle deposition and decreased particle escape. As a result of the two opposite particle removal mechanisms, i.e., deposition and escape, the impact of indoor partitions on PM1/PM2.5/PM10 removal behavior was not as significant as the results of airflow velocities. This work developed a computational fluid dynamics technique to investigate indoor airflow patterns and PM1/PM2.5/PM10 removal ability in ventilated multi-room buildings. The results of this paper can help to identify adequate PM1/PM2.5/PM10 cleaning procedure and provide useful size-dependent PM control strategy in multi-room buildings.

Modelling urban inundation and its associated health implications is numerous in its many applications. Flood modelling research contains a broad wealth of material, and microbial risk assessment has gained more popularity over the last decade. However, there is still a relative lack of understanding of how the microbial risk can be quantified from urban sewer flooding. This article intends to review the literature encompassing contemporary urban flood modelling approaches. Hydrodynamic and microbial models that can be applied for quantitative microbial risk assessment will be discussed. Consequently, urban sewer flooding will be the focus. This review found that the literature contains a variety of different hazards posed by urban flooding. Yet, far fewer examples encompass microbial risk from sewer system exceedance. To date, there is no evidence of a perfect model or technique, to carry out a quantitative microbial risk assessment from hydrodynamic simulations. The literature details many different methods. We intend to detail the advantages and limitations of each method. Along similar lines, hydraulic data constitutes a large part of the uncertainty which is inherent to this research field. Many studies in the literature detail data paucity and uncertainty in input data. As such, any advancement in this discipline will very likely aid future research.

This study attempts to achieve real-time rainfall-inundation forecasting in lowland regions, based on a synthetic potential inundation database. With the principal component analysis and a feed-forward neural network, a rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast 1-h-ahead inundation depth as hydrographs at specific representative locations using spatial rainfall intensities and accumulations. A systematic procedure is presented to construct the RiHNN, which combines the merits of detailed hydraulic modeling in flood-prone lowlands via a two-dimensional overland-flow model and time-saving calculation in a real-time rainfall-inundation forecasting via ANN model. Analytical results from the RiHNNs with various principal components indicate that the RiHNNs with fewer weights can have about the same performance as a feed-forward neural network. The RiHNNs evaluated through four types of real/synthetic rainfall events also show to fit inundation-depth hydrographs well with high rainfall. Moreover, the results of real-time rainfall-inundation forecasting help the emergency manager set operational responses, which are beneficial for flood warning preparations.