Assessing Debris Flow Susceptibility in Mountainous Area of Beijing, China Using a Combination Weighting and an Improved Fuzzy C-means Algorithm

Susceptibility analysis is important in any study of debris flows. This paper presents a model for debris flow susceptibility analysis using a combination weighting and an improved fuzzy C-means algorithm. Twelve factors of influence were acquired by 3S technologies. Analytic hierarchy process (AHP) and entropy method were performed to obtain subjective and objective weighting of the factors, respectively. Game theory was carried out to determine the combination weighting since it provides analytical tools to model interactions among factors. An improved fuzzy C-means clustering analysis was applied to determine the susceptibility level of debris flows. This method is based on a particle swarm optimization algorithm, which is an evolutionary algorithm that can achieve global optimization, and is not sensitive to the initial cluster centers. Results showed that the susceptibility levels for one of the debris flow catchments was high, four were moderate, and five were low. Our quantitative assessments based on these nonlinear methods were consistent with field investigations.