Flood risk zoning using a rule mining based on ant colony algorithm
Abstract Risk assessment is a preliminary step in flood management and mitigation, and risk zoning provides a quantitative measure of flood risk. The difficulty in flood risk zoning is to deal with the complicated non linear relationship among indices and risk levels. To solve this problem, the ant colony algorithm based on rule mining (Ant Miner) is promoted in this paper to map the regional flood risk at grid scale. For the case study in the Dongjiang River Basin in Southern China, 11 and 14 indices (without and with the socio economic indices considered) are respectively chosen to construct the zoning model based on Ant Miner. The results show that Ant Miner exhibits higher accuracy and more simple rules that can be used to generate flood risk zoning map quickly and easily than decision tree method (DT) compared to random forest (RF) and fuzzy comprehensive evaluation (FCE) Ant Miner has significant advantages both in implementation step reducing and computing time saving. Although the comprehensive measure and natural hazard measure of flood risk distributed similarly over the entire region, the former one which considered the socio economic indices is more reasonable in term of real impact to natural and socio economy. The areas with high risk level obtained in this paper matched well with the integrated risk zoning map and the inundation areas of historical floods, suggesting that the proposed Ant Miner method is capable of zoning the flood risk at grid scale. This study shows the potential to provide a novel and successful approach to flood risk zoning. Evaluation results provide a reference for flood risk management, prevention, and reduction of natural disasters in the study basin.