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Streamflow Prediction Based on Artificial Intelligence Techniques
https://orcid.org/http://orcid.org/0000-0001-5453-3791
https://orcid.org/http://orcid.org/0000-0001-7927-9718
https://orcid.org/https://orcid.org/0000-0002-4167-1690
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.
Streamflow Prediction Based on Artificial Intelligence Techniques
https://orcid.org/http://orcid.org/0000-0001-5453-3791
https://orcid.org/http://orcid.org/0000-0001-7927-9718
https://orcid.org/https://orcid.org/0000-0002-4167-1690
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.
Streamflow Prediction Based on Artificial Intelligence Techniques
https://orcid.org/http://orcid.org/0000-0001-5453-3791
https://orcid.org/http://orcid.org/0000-0001-7927-9718
https://orcid.org/https://orcid.org/0000-0002-4167-1690
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.
Streamflow Prediction Based on Artificial Intelligence Techniques
Iran J Sci Technol Trans Civ Eng
Meshram, Sarita Gajbhiye (author) / Meshram, Chandrashekhar (author) / Santos, Celso Augusto Guimarães (author) / Benzougagh, Brahim (author) / Khedher, Khaled Mohamed (author)
2022-06-01
11 pages
Article (Journal)
Electronic Resource
English
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