Abstract
In this paper, a novel ANN flood forecasting model is proposed. The ANN model is combined with traditional hydrological concepts and methods, taking the initial Antecedent Precipitation Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input of model, and flood flow forecast of the next time steps as output of the 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. The 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 Mozitan and Xianghongdian reservoirs in the upper reaches of Pi river in Huaihe Basin, and the Fengman reservoir in the upper reach of Second Songhua river in Songhua basin in China.
Highlights
Accurate flood forecasting is important for flood control and disaster reduction and prevention
The ANN model proposed in this paper takes the key influencing factors according to the physical process of rainfall-runoff formation, the initial Antecedent Precipitation
Index (API), rainfall, upstream inflow and initial flow at the forecast river section as input, obtains a model to simulate the whole process of a flood event
Summary
Accurate flood forecasting is important for flood control and disaster reduction and prevention. Various hydrological models for flood forecasting have been developed in history. These models can be classified into three major types: empirical statistical model, lumped conceptual hydrological model and distributed hydrological model. Difficulties in the long-term practice of flood forecasting exist due to the complexity of rainfall-runoff process in a watershed. The heterogeneity of spatial distribution of rainfall always has been an important factor affecting the accuracy of flood forecasting. While it is difficult to solve these problems using the empirical statistical model and lumped conceptual hydrological model, since these models usually take the forecast basin as a whole unit and use the area average rainfall as the model input. The distributed hydrological model could solve the problems, but this kind of model is complex in structure, complicated in calculation, and requires different types of data (Yu, 2008)
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