Application of a particle swarm optimization algorithm to hydrological problems

It is significant to acquire a reliable correlation between rainfall-runoff in any locations, whether in urban cities or rural areas. It enables the local authority to gain more ample time for formulation of appropriate decision making, issuance of an advanced flood forewarning, and execution of earlier evacuation measures. Since a variety of existing methods such as rainfall-runoff modeling or statistical techniques involve exogenous input and different assumptions, artificial neural networks have the potential to be a cost-effective solution, provided that their drawbacks can be overcome. Usual problems in the training with gradient algorithms are the slow convergence and easy entrapment in a local minimum. This paper presents a particle swarm optimization model for training perceptrons applying to hydrological problems. The model is applied to effect discharge forecasting and water stage prediction in Shing Mun River of Hong Kong with different lead times on the basis of the upstream gauging stations or at the specific station. It is demonstrated that the results are both more accurate and faster to attain, when compared with the benchmark backward propagation algorithm. The results demonstrate the novel algorithm is promising in the hydrological field.