摘要
Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting debris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and useful in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time series of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collected in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.
Debris flow is one of the most destructive phenomena of natural hazards.Recently, major natural hazard, claiming human lives and assets, is due to debris flow in the world.Several practical methods for forecasting debris flow have been proposed, however, the accuracy ofthese methods is not high enough for practical use because of the stochastic and non-linearcharacteristics of debris flow. Artificial neural network has proven to be feasible and useful indeveloping models for nonlinear systems. On the other hand, predicting the future behavior based ona time series of collected historical data is also an important tool in many scientificapplications. In this study we present a three-layer feed-forward neural network model to forecastsurge of debris flow according to the time series data collected in the Jiangjia Ravine, situated innorth part of Yunnan Province of China. The simulation and prediction of debris flow using theproposed approach shows this model is feasible, however, further studies are needed.
基金
UndertheauspicesoftheNationalNaturalScienceFoundationofChina(No.40025103)