Streamflow Prediction of Karuvannur River Basin Using ANFIS, ANN and MNLR Models
| Autor: | K. Anusree, K.O. Varghese |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Hydrology
geography Adaptive neuro fuzzy inference system Hydrological modeling geography.geographical_feature_category 010504 meteorology & atmospheric sciences Mean squared error Rain gauge Artificial neural network Multiple Nonlinear Regression Fuzzy inference system Drainage basin 010501 environmental sciences 01 natural sciences Streamflow General Earth and Planetary Sciences Precipitation Nonlinear regression Neural networks 0105 earth and related environmental sciences General Environmental Science |
| Zdroj: | Procedia Technology. 24:101-108 |
| ISSN: | 2212-0173 |
| DOI: | 10.1016/j.protcy.2016.05.015 |
| Popis: | For the planning, design and management of water resources systems, streamflow forecasting is important. The use of artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting daily flow at the outlet of Karuvannur river basin, located in Thrissur district, is presented in this study. Precipitation data from nine raingauge stations were used to develop the models. Input vectors for simulations included different combinations of antecedent precipitation and flows, with different time lags. Performances of the models were evaluated with the RMSE and Nash-Sutcliffe model efficiency values. The results showed that ANFIS model predicts daily flow more accurately compared to ANN and MNLR models. Furthermore, ANFIS model with an input combination of antecedent flow with one day time lag and antecedent rainfall with three and four day time lags, is better than all other cases considered here. Therefore by using the ANFIS model with these 3 inputs we can forecast the daily discharge of Karuvannur river basin with a better accuracy. |
| Databáze: | OpenAIRE |
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