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Streamflow Prediction Based on Artificial Intelligence Techniques
- Source :
- Iranian Journal of Science and Technology, Transactions of Civil Engineering. 46:2393-2403
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
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Abstract
- The application of Artificial Intelligence (AI) techniques has become popular in science and engineering applications since the middle of the twentieth century. In this present study, three AI techniques (ANFIS, GP and ANN) have been used for forecasting streamflow into Shakkar watershed (Narmada Basin), India. The models have been used considering previous streamflow and cyclic terms in the input vector to provide a suitable time series model for streamflow forecasting. To evaluate the model performance, RMSE, MAE, CORR and CE were employed. Results showed that the ANFIS has the best performance in forecasting streamflow time series for Shakkar watershed. The GP and ANN are in the 2nd and 3rd ranks, respectively. According to the results, in all the AI methods (ANFIS, GP and ANN), the model with cyclic terms had better performance compared to those models not considering periodic nature and being applied by only considering the previous streamflow.
- Subjects :
- Adaptive neuro fuzzy inference system
Watershed
Series (mathematics)
Mean squared error
Artificial neural network
business.industry
Computer science
Science and engineering
Geotechnical Engineering and Engineering Geology
Streamflow
Artificial intelligence
Time series
business
Civil and Structural Engineering
Subjects
Details
- ISSN :
- 23641843 and 22286160
- Volume :
- 46
- Database :
- OpenAIRE
- Journal :
- Iranian Journal of Science and Technology, Transactions of Civil Engineering
- Accession number :
- edsair.doi...........2df6a56d2e5d950060e2e2dd5c06ead6