Performance of multi-level association rule mining for the relationship between causal factor patterns and flash flood magnitudes in a humid area

Abstract

Integrated with K-means clustering and Apriori algorithm, the multi-level association rule mining is proposed to investigate the causal factor patterns of flash floods, which consists of the following three steps: first, the association between causal factors and flash flood occurrence is being analysed; second, to identify the contribution of soil moisture (SM) to flash flood hazards, the association between risk indicators and SM, and the linkage between SM and risk magnitude are being discussed; finally, with the consideration of total 24-h rainfall and SM pattern, the association rules for risk magnitude are extracted. The method has been tested in a humid area of southern China, results show: (1) flash flood hazards are especially active after the prolonged and periodic intense rainfalls, and because of the saturated SM, flash floods are easily triggered even by slight rainfall; (2) severe flash floods are easily triggered by extreme rainfall, and SM is the critical indicator of 5-year floods and 20-year floods; and (3) owing to the differences in steady infiltration rate and instability in soil type, conservation of water and soil is an indispensable and co-ordinate part of flood control. Results are expected to be applicable for decision-making in flood control and flood prediction.