Reservoir Inflow Forecasting Using Extreme Learning Machines

Abstract

Accurate and reliable forecasting of reservoir inflow is necessary for efficient and effective water resources' planning and management. In the present study, the capacity of recently developed extreme learning machines (ELMs) modeling approach in forecasting reservoir inflows is assessed and compared to that of equivalent traditional artificial neural network-based models. Performance of wavelet analysis technique is also explored by developing wavelet-based ELMs (WELMs) and wavelet-based ANNs (WANNs) models. Seven years of reservoir inflow data along with outflow data from two upstream reservoirs in the Damodar catchment along with rainfall data of 5 upstream rain gauge stations are considered in this study. Out of 7 years' daily data, 5 years' data are used for training the model, one-year data are used for cross-validation, and remaining one-year data are used to evaluate the performance of the developed models. Different performance indices indicated better performance of ELM and WELM models in comparison with MLR, ANN, WMLR, and WANN models. This study demonstrated the effectiveness of proper selection of wavelet functions and appropriate methodology for wavelet-based model development. ELM models were also computationally efficient as demonstrated by faster running time, and consequently, this study advocates the superiority of the WELM model and the significant role of wavelet transformation in order to improve the model's overall performance for reservoir inflow forecasting modeling.