@article { author = {Kooyian Afzal, F. and Mousavi, J. and Sedghi, H. and Porhemmat, J.}, title = {Real-Time Flood Forecasting Using Hybrid Neural Networks}, journal = {Journal of Hydraulics}, volume = {3}, number = {1}, pages = {1-18}, year = {2008}, publisher = {Iranian Hydraulic Association}, issn = {2345-4237}, eissn = {2645-8063}, doi = {10.30482/jhyd.2008.85445}, abstract = {Hybrid models which are based on methods which divide a complex simulation problem to severalsimple local models and combine the results, potentially could result in different output. The inputspace in this method is divided into subspaces, and then some single models are assigned to eachspecific region of the space. In this research by using some floods generated by a hydrologic model,advantages of hybrid models in real-time flood forecasting compared to global models wasinvestigated. To do this, the results of a global ANN model which simulates whole of the floodprocesses using a single model, are compared with that of two hybrid models, one consisting of a 4ANN and the other consisting of 8 ANN. The results shows that hybrid models have significantlybetter results in flood forecasting specially in forecasting time and amount of peak discharges. This isvery important in flood forecasting in flood warning systems because of their important role in floodmitigation activities.}, keywords = {}, title_fa = {پیش بینی زمان واقعی سیل با استفاده از شبکه های عصبی ترکیبی}, abstract_fa = {}, keywords_fa = {}, url = {https://jhyd.iha.ir/article_85445.html}, eprint = {https://jhyd.iha.ir/article_85445_2e5a2f3ac36490b83efd2f88eb6b790e.pdf} }