Abstract
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 t + 4, and the best ANFIS model was model t + 6.
Highlights
In hydrology and water resource management, machine learning models (ML) have solved highly complex problems and have allowed a broad spectrum of opportunities and uses for technicians and experts in the area [1]. Their growth in popularity began in the nineties, when they were applied for predicting rainfall and runoff time series, which normally have a high degree of temporal variability and non-linear relationships [2,3]
The Salve Faccha dam is a hydraulic structure of the Papallacta system located in the Andean part of northern Ecuador (Figure 1)
The one and two-day predictions were more similar to the naive-type predictions. These models are characterized by the replication of the previous value as the prediction value. This result is similar to the neural network models with a three-day delay
Summary
In hydrology and water resource management, machine learning models (ML) have solved highly complex problems and have allowed a broad spectrum of opportunities and uses for technicians and experts in the area [1]. Their growth in popularity began in the nineties, when they were applied for predicting rainfall and runoff time series, which normally have a high degree of temporal variability and non-linear relationships [2,3]. Neural networks (NN) and adaptive neuro-fuzzy inference systems (ANFIS) models are two of the most used ML tools These models are mainly used for predicting, filling, and classifying data series. Applications are continually being developed to meet new needs
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