Artificial Neural Networks Based Hydrological Processes Simulation Research

Hydrological process is a highly complex and non-linear process. Before artificial intelligence extensively applied in hydrological problems, the physical-based distributed and conceptual models are utilised everywhere. The distributed physical model adapts the different patterns of one- or two-dimension Santa Venent Equations, and try to represent precisely every sub-process of hydrological cycle. Due to the limitation of man's intelligence, this kinds of models have to use simplified way to represent physical rules. And in reality, the implementation of distributed models is very difficult, and complex mathematical tools needed. The users of such models also need some experience and knowledge, amount of data needed to calibrate the models. After the calibration, using less parameters to represent the catchment characteristics, the models omit the spatial distribution, temporal features and stochastically of runoff process, then the models become the semi-lumped models.The lumped conceptual models have advantages compared to distributed models from the data requirement, calculation time and model structure point of view. But the calibration of such models also need amount of time, and the model users should own some experience and knowledge. Such kind of models can not consider heterogeneous and nonlinealities of the system, and essentially, this kind models use few calibrated parameters to represent the characteristics of the catchment. It is difficult to interpret the calibrated parameters for during calibration, in order to balance the observed data, the parameters always lost their physical meaning.Artificial Neural Networks - ANNs is kind of artificial intelligence tool which is widely applied in the fields of time series analysis, pattern identification etc. The possibility of using ANNs models to simulate the rainfall-runoff relationship of urban areas will be explored first. In this application, ANN model will be set up to serve as the rainfall-runoff models of urban areas, then the important parameter Percentage of Imperious Areas - PIA, which represent the process of urbanisation will be add to the input patterns to find the effect to the relationship between rainfall and runoff of urbanisation. Amount of numerical experience illustrated that after proper setting-up of neural network models, the underlying rainfall-runoff relationship will be reproduced to generate precisely and pragmatically forecasting results and then offer quick and effective support to urban storm water real time control.For continues runoff simulation, clustering will be performed upon the runoff sequence and then several local feed-forward neural networks will be formulated for each class. When new data fed into the modified ANN model, a classifier will direct the new data into different non-linear local ANN model. The performance comparison with that of the singular ANN illustrated that the classifier based local ANN rainfall-runoff model had the superior performance.