Rainstorm flash flood risk assessment using genetic programming: a case study of risk zoning in Beijing

HaiBo Hu ()
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HaiBo Hu: CMA

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2016, vol. 83, issue 1, No 24, 485-500

Abstract: Abstract In this study, a genetic programming (GP) algorithm is introduced to solve the symbolic regression problem for flash flood risk zoning in Beijing. GP operates in simulation of biological revolution and can avoid arbitrariness in risk estimates. Herein, this revolutionary computing searched for an appropriate model to best fit the training samples which comprise the data fields of the predictand of Ripley’s K-values to be the posterior risk, and the predictors of the rainstorm hazard index value (RHIV), physical vulnerability, terrain factor, impervious surface area, and population density. After generations of revolution, the optimal fit regressions for estimating the risk value were determined in the form of function (parse) trees. Also, the grid risk values were calculated using the deduced regression. The risk zoning map indicates that the risk values are higher in urban areas, which is reasonable in comparison with the distribution of historical flash flood events. With an explicit model structure, this symbolic regression manifests that the risk value is mainly determined by the RHIV and impervious land surface and is weakly correlated with the other risk factors, e.g., the physical vulnerability, the terrain factor, and population density. Our research demonstrates that GP in an artificial intelligence manner meets the needs of risk assessment in determining the optimal fit regressions and is a promising technique for future applications. Meanwhile, approaches are still available for improving the GP application in the risk assessment, e.g., considering the historical losses in posterior risk estimations and improvement in the sampling training data.

Keywords: Genetic programming; Flood; Risk assessment; Artificial intelligence (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11069-016-2325-x

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