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A platform for research: civil engineering, architecture and urbanism
GIS-Based Flood Risk Zoning Based On Data-Driven Models
Increasing the occurrence of floods, especially in cities, and the risks to human, financial, and environmental risks due to its, make flood risk zoning of great importance. The purpose of this study is to estimate the flood risk of the Maneh and Samalghan based on determining effective criteria and spatial and non-spatial data-driven models. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present new combination approaches to determine the effective criteria in flood risk zoning (Maneh and Samalghan). In this regard, the geographically weighted regression (GWR) with exponential and bi-square kernels and artificial neural network (ANN) combined with a binary particle swarm optimization algorithm (BPSO). The best value of the fitness function (1-R2) for ANN, GWR with the exponential kernel, and GWR with bi-square kernel was obtained 0.1757, 0.0461, and 0.0097, respectively, Which indicates higher compatibility of the bi-square kernel than the other models. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.
GIS-Based Flood Risk Zoning Based On Data-Driven Models
Increasing the occurrence of floods, especially in cities, and the risks to human, financial, and environmental risks due to its, make flood risk zoning of great importance. The purpose of this study is to estimate the flood risk of the Maneh and Samalghan based on determining effective criteria and spatial and non-spatial data-driven models. The criteria used in this research include Modified Fournier Index, Topographic Position Index, Curve Number, Flow Accumulation, Slope, Digital elevation model, Topographic Wetness Index, Vertical Overland Flow Distance, Horizontal Overland Flow Distance, and Normalized difference vegetation index. The novelty of this study is to present new combination approaches to determine the effective criteria in flood risk zoning (Maneh and Samalghan). In this regard, the geographically weighted regression (GWR) with exponential and bi-square kernels and artificial neural network (ANN) combined with a binary particle swarm optimization algorithm (BPSO). The best value of the fitness function (1-R2) for ANN, GWR with the exponential kernel, and GWR with bi-square kernel was obtained 0.1757, 0.0461, and 0.0097, respectively, Which indicates higher compatibility of the bi-square kernel than the other models. It was also found that the criteria used have a significant effect on the rate of flooding in the study area.
GIS-Based Flood Risk Zoning Based On Data-Driven Models
Seyed Ahmad Eslaminezhad (author) / Mobin Eftekhari (author) / Mohammad Akbari (author)
2020
Article (Journal)
Electronic Resource
Unknown
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