An integrated fluvial and flash pluvial model using 2D high-resolution sub-grid and particle swarm optimization-based random forest approaches in GIS View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-06-15

AUTHORS

Hossein Mojaddadi Rizeei, Biswajeet Pradhan, Maryam Adel Saharkhiz

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. More... »

PAGES

1-20

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40747-018-0078-8

DOI

http://dx.doi.org/10.1007/s40747-018-0078-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1104649839


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