Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador
Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/13/15/2011 |
| _version_ | 1797525121864302592 |
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| author | Pablo Páliz Larrea Xavier Zapata-Ríos Lenin Campozano Parra |
| author_facet | Pablo Páliz Larrea Xavier Zapata-Ríos Lenin Campozano Parra |
| author_sort | Pablo Páliz Larrea |
| collection | DOAJ |
| description | Despite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was <i>t</i> + 4, and the best ANFIS model was model <i>t</i> + 6. |
| first_indexed | 2024-03-10T09:07:17Z |
| format | Article |
| id | doaj.art-7a4e1ded4ae046a6a58e272cf21f04af |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T09:07:17Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-7a4e1ded4ae046a6a58e272cf21f04af2023-11-22T06:19:04ZengMDPI AGWater2073-44412021-07-011315201110.3390/w13152011Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern EcuadorPablo Páliz Larrea0Xavier Zapata-Ríos1Lenin Campozano Parra2Departamento de Ingeniería Civil y Ambienal, Escuela Politécnica Nacional, Quito 17-01-2579, EcuadorDepartamento de Ingeniería Civil y Ambienal, Escuela Politécnica Nacional, Quito 17-01-2579, EcuadorDepartamento de Ingeniería Civil y Ambienal, Escuela Politécnica Nacional, Quito 17-01-2579, EcuadorDespite the importance of dams for water distribution of various uses, adequate forecasting on a day-to-day scale is still in great need of intensive study worldwide. Machine learning models have had a wide application in water resource studies and have shown satisfactory results, including the time series forecasting of water levels and dam flows. In this study, neural network models (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models were generated to forecast the water level of the Salve Faccha reservoir, which supplies water to Quito, the Capital of Ecuador. For NN, a non-linear input–output net with a maximum delay of 13 days was used with variation in the number of nodes and hidden layers. For ANFIS, after up to four days of delay, the subtractive clustering algorithm was used with a hyperparameter variation from 0.5 to 0.8. The results indicate that precipitation was not influencing input in the prediction of the reservoir water level. The best neural network and ANFIS models showed high performance, with a r > 0.95, a Nash index > 0.95, and a RMSE < 0.1. The best the neural network model was <i>t</i> + 4, and the best ANFIS model was model <i>t</i> + 6.https://www.mdpi.com/2073-4441/13/15/2011machine learningtime series forecastwater level |
| spellingShingle | Pablo Páliz Larrea Xavier Zapata-Ríos Lenin Campozano Parra Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador Water machine learning time series forecast water level |
| title | Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador |
| title_full | Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador |
| title_fullStr | Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador |
| title_full_unstemmed | Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador |
| title_short | Application of Neural Network Models and ANFIS for Water Level Forecasting of the Salve Faccha Dam in the Andean Zone in Northern Ecuador |
| title_sort | application of neural network models and anfis for water level forecasting of the salve faccha dam in the andean zone in northern ecuador |
| topic | machine learning time series forecast water level |
| url | https://www.mdpi.com/2073-4441/13/15/2011 |
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