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 d...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.展开更多
Stage-discharge curves are particularly important in river basin management. For a compound channel, the stage-discharge curve is often difficult to be extrapolated to yield estimates of level for a given frequency of...Stage-discharge curves are particularly important in river basin management. For a compound channel, the stage-discharge curve is often difficult to be extrapolated to yield estimates of level for a given frequency of flow. By analyzing a large number of experimental data from Science and Engineering Research Council Flood Channel Facility (SERC-FCF) and applying system dynamics method, the authors established system dynamics model of conveyance capacity when rivers flow in an overbank mode, spilling onto the adjoining flood plain. The model was applied to a compound channel. And the corresponding simulated results are shown to attain high accurcy.展开更多
文摘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.
文摘Stage-discharge curves are particularly important in river basin management. For a compound channel, the stage-discharge curve is often difficult to be extrapolated to yield estimates of level for a given frequency of flow. By analyzing a large number of experimental data from Science and Engineering Research Council Flood Channel Facility (SERC-FCF) and applying system dynamics method, the authors established system dynamics model of conveyance capacity when rivers flow in an overbank mode, spilling onto the adjoining flood plain. The model was applied to a compound channel. And the corresponding simulated results are shown to attain high accurcy.
