Forecasting of Reservoir Inflow by the Combination of Deep Learning and Conventional Machine Learning

Paul, Topon Kumar; Raghavendra, S.; Ueno, Ken; Fang, Ni; Shin, Hiromasa; Nishino, Ko; Shingaki, Ryusei

doi:10.1109/icdmw53433.2021.00074

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…Recently, instead of hydrological modeling tools, forecasting of inflow to a reservoir or water level of a river by employing various machine-learning techniques is getting attention (Paul et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Reservoir Outflow Prediction Using Adaptive Neuro-Fuzzy Interface System

Tatavarthi,

Vemuri,

Singh

2024

Preprint

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Water supply, flood control, and hydropower generation are among the primary functions that rely on the prediction of reservoir outflow. Accurate prediction of reservoir outflow can help mitigate the flood risk and ensure the long-term sustainability of the hydraulic structure. Traditional prediction methods, such as linear regression analysis, and autoregressive integrated moving average (ARIMA) models, are based on historical data from the watershed. However, due to the uncertainty involved in the prediction of trends in flood events, the use of traditional methods is considered inadequate. To overcome these limitations, machine learning (ML) algorithms such as artificial neural networks (ANNs), support vector regression (SVR), and adaptive neuro-fuzzy inference systems (ANFIS) are used. In this study, ANN and ANFIS techniques are used for predicting the daily flow at the outlet of the Godavari river basin, located in West Godavari district in the province of Andhra Pradesh, India. Three key inputs, i.e. gauge data from the rain gauge station, discharge, and elevation data were used to develop the models. Input vectors for simulations included different combinations of antecedent precipitation and flows, with different time lags. From the results, it is observed that the outflow predictions obtained using the ANFIS model are more accurate as compared to those obtained from the ANN models. Lastly, a sensitivity analysis was performed to assess the reliability and resilience of both the neural network models.

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Section: Introductionmentioning

confidence: 99%

Reservoir Outflow Prediction Using Adaptive Neuro-Fuzzy Interface System

Tatavarthi,

Vemuri,

Singh

2024

Preprint

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“…ML models can quickly capture the complexity of dam inflow time-series data without extensive prior knowledge [19]. In addition, many studies have successfully utilized ensemble methods to improve the accuracy of dam inflow predictions [20][21][22][23]. Ensemble modeling is a process in which an ML model combines the predictive capabilities of individual base models unique to the model to generate generalized predictions, allowing for capturing various aspects of the data and providing high prediction performance.…”

Section: Introductionmentioning

confidence: 99%

Development of Multi-Inflow Prediction Ensemble Model Based on Auto-Sklearn Using Combined Approach: Case Study of Soyang River Dam

et al. 2023

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Accurate prediction of dam inflows is essential for effective water resource management and dam operation. In this study, we developed a multi-inflow prediction ensemble (MPE) model for dam inflow prediction using auto-sklearn (AS). The MPE model is designed to combine ensemble models for high and low inflow prediction and improve dam inflow prediction accuracy. We investigated the impact of datasets assigned to flow regimes on the ensemble composition and compared the performance of the MPE model to an AS-based ensemble model developed using a conventional approach. Our findings showed that the MPE model outperformed the conventional model in predicting dam inflows during flood and nonflood periods, reducing the root mean square error (RMSE) and mean absolute error (MAE) by 22.1% and 24.9% for low inflows, and increasing the coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) by 21.9% and 35.8%, respectively. These results suggest that the MPE model has the potential to improve water resource management and dam operation, benefiting both the environment and society. Overall, the methodology of this study is expected to contribute to the development of a robust ensemble model for dam inflow prediction in regions with high climate variability.

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