Analysis and modelling of flood risk assessment using information diffusion and artificial neural network

Abstract

Floods are a serious hazard to life and property. The traditional probability statistical method is acceptable in analysing the flood risk but requires a large sample size of hydrological data. This paper puts forward a composite method based on artificial neural network (ANN) and information diffusion method (IDM) for flood analysis. Information diffusion theory helps to extract as much useful information as possible from the sample and thus improves the accuracy of system recognition. Meanwhile, an artificial neural network model, back-propagation (BP) neural network, is used to map the multidimensional space of a disaster situation to a one-dimensional disaster space and to enable resolution of the grade of flood disaster loss. These techniques all contribute to a reasonable prediction of natural disaster risk. As an example, application of the method is verified in a flood risk analysis in China, and the risks of different flood grades are determined. Our model yielded very good results and suggests that the methodology is effective and practical, with the potentiality to be used to forecast flood risk for use in flood risk management. It is also hoped that by conducting such analyses lessons can be learned so that the impact of natural disasters such as floods can be mitigated in the future.

Ensemble learning analysis of influencing factors on the distribution of urban flood risk points: a case study of Guangzhou, China

“…Exploring existing data and identifying potential structures for learning strategies, machine learning simulates humankind using computer-based learning algorithms, and analyzes and predicts based on the resulting models (Kohavi and Provost, 1998). With the advancement of machine learning technology, algorithms such as decision tree (Merz et al, 2013), artificial neural network (Li et al, 2013), support vector machine (Tehrany et al, 2019), weakly labeled support vector machine (Zhao et al, 2019) have been widely used in research on urban waterlogging. These models have significantly improved computational capacity and are effective at solving nonlinear problems.…”

Risk mapping of integrated natural disasters in China

“…Zhang et al (2010) used a natural disaster risk index based on the administrative regions, grids, and the spatial scale of settlements to assess the risks of storm floods in the middle and lower reaches of the Songhua River area. Li et al (2013) proposed a composite method based on artificial neural networks and an information diffusion method for flood risk mapping by analyzing the affected area, the number of deaths, the number of collapsed houses, and other historical disaster data.…”