Abstract: This study presents the development of Artificial Neural Networks (ANNs) and Fuzzy Logic (FL) models for prediction of daily reservoir inflow. Furthermore, a Linear Regression (LR) model was also developed as a traditional method for flood forecasting. To illustrate the applicability and capability of the ANNs and FL models, the Dez reservoir, located in the south-west of Iran, was used as a case study. The results demonstrated that ANNs model can predict the reservoir inflow for 1-day-ahead, especially for training pattern better than the FL and LR models. It was found that the accuracy of ANNs model predictions decreased for flood forecasting more than 1-day ahead (e.g., 2, 3, or 4 days ahead), whereas the results obtained from the FL and LR models showed better correlation with the corresponding measured values in this conditions. One of the main findings of this research was that the fuzzy logic model generally underestimated the flood even for, whereas the other two considered models predicted the flood discharge relatively good. The peak value of the hydrograph, which is very important from the flood hazard viewpoint, was estimated good by the ANNs and LR models for the short period (1-day ahead), with the error being 3, 4.5 and 26% for the ANNs, LR and FL models, respectively. For the long periods (e.g., 3-days ahead) the flood discharge was predicted by the LR and FL models slightly better than the ANNs model.
19 December, 2014
Prisilla Jayanthi: i am trying to working on the new model for the predicting of floods in emergency management system. so kindly help get the access to the full text of
Application of Fuzzy Systems and Artificial Neural Networks for Flood Forecasting
R. Tareghian and S.M. Kashefipour