Abstract
Two types of flooding, namely fluvial flood (FF) and pluvial flash flood (PFF), exist in tropical cities located close to permanent rivers, where extreme precipitation intensity occurs. Although several methods are available for assessment of FF, however, PFF has received minimal attention from the researchers. Studies rarely presented joint FF and PFF hazards. Therefore, the current study not only aims to evaluate probability and hazards for FF and PFF independently but also implements combined FF with PFF probabilistic inundation analysis. First, an integrated model was developed to analyze probability using fully distributed geographic information system (GIS)-based algorithms. These methods were performed on Damansara River Catchment in Kuala Lumpur, because yearly monsoon triggers FFs and simultaneously coincides with heavy local rainfalls. A hydraulic 2D high-resolution sub-grid model of Hydrologic Engineering Center River Analysis System was performed to simulate FF probability and hazard. Nine significant contributing parameters were trained with PFF inventory by GIS-based random forest (RF) model and each RF parameter was optimized by particle swarm optimization algorithm (PSO) to model the PFF probabilistic hazard. Finally, PFF was combined with FF probabilities to discover the impact and contribution of each type of urban flood hazard. This study is the first attempt to model PFF hazard using GIS and physical-based PSO–RF model and combined FF and PFF probabilistic map. The results provide detailed flood information for urban managers to smartly equip infrastructures, such as highways, roads, and sewage network.
Highlights
Flooding is one of the most frequent natural disasters; analysis, forecasting, and modeling flood at various temporal scenarios and spatial scales bear significance [1]
Related pluvial flash flood (PFF) conditioning factors contributing to PFF probabilistic hazard assessment were extracted as follows: curvature, stream power index (SPI), topographic roughness index (TRI), topographic wetness index (TWI), digital surface model (DSM), surface slope, surface runoff, maximum precipitation intensity, and LU/land cover (LULC)
Significant contribution parameters were successfully trained with PFF inventory by coupling geographic information system (GIS)-based random forest (RF) with particle swarm optimization algorithm (PSO) models. 2D high-resolution subgrid (2D-HRS) hydraulic model was designed and calibrated to determine fluvial flood (FF) probability and hazards
Summary
Flooding is one of the most frequent natural disasters; analysis, forecasting, and modeling flood at various temporal scenarios and spatial scales bear significance [1]. Tropical countries have received significant attention in flood mitigation plans because of frequent occurrences of urban floods. Malaysia suffers from frequent flood events especially during the monsoon period [2]. Flood events are generally unanticipated to a high certain extent, they can be governed using recent and precise deterministic and probabilistic flood modeling to predict flood and decrease the amount of damages or losses [3]. Simulated flood inundation and flood plain system can provide significant information, benefitting probability and emergency cases to mitigate loss and damages to human lives and properties [4]. A significant portion of the urban flood damages occur especially in dense populations and areas of concentrated urban infrastructures [5]
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