Application of a Novel Artificial Neural Network Model in Flood Forecasting

In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, takes the initial Antecedent Precipitation Index ( API ), rainfall, upstream inflow and initial flow at the forecast river section as input of model. The distributed rainfall is realized as the input of the model. The simulation is processed by dividing the watershed into several rainfall-runoff processing units. Two hidden layers are used in the ANN, and the topology of ANN is optimized by connecting the hidden layer neurons only with the input which has physical conceptual causes. Topological structure of the proposed ANN model and its information transmission process are more consistent with the physical conception of rainfall-runoff, and the weight parameters of the model are reduced. The arithmetic moving-average algorithm is added to the output of the model to simulate the pondage action of the watershed. Satisfactory results have been achieved in the upstream of Second Songhua river in Songhua basin from the Baishan reservoir to Fengman reservoir sections, and the Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin in China.