Abstract
Reliable prediction of evaporative losses from reservoirs is an essential component of reservoir management and operation. Conventional models generally used for evaporation prediction have a number of drawbacks as they are based on several assumptions. A novel approach called the co-active neuro-fuzzy inference system (CANFIS) is proposed in this study for the modeling of evaporation from meteorological variables. CANFIS provides a center-weighted set rather than global weight sets for predictor–predictand relationship mapping and thus it can provide a higher prediction accuracy. In the present study, adjustments are made in the back-propagation algorithm of CANFIS for automatic updating of membership rules and further enhancement of its prediction accuracy. The predictive ability of the CANFIS model is validated with three well-established artificial intelligence (AI) models. Different statistical metrics are computed to investigate the prediction efficacy. The results reveal higher accuracy of the CANFIS model in predicting evaporation compared to the other AI models. CANFIS is found to be capable of modeling evaporation from mean temperature and relative humidity only, with a Nash–Sutcliffe efficiency of 0.93, which is much higher than that of the other models. Furthermore, CANFIS improves the prediction accuracy by 9.2–55.4% compared to the other AI models.
Highlights
Egypt is facing the challenge of growing water stress owing to limited water resources
The ability of a new artificial intelligence (AI) model known as co-active neuro-fuzzy inference system (CANFIS) to predict lake evaporation is investigated in this study
The results reveal that the support vector regression (SVR) and radial basis function neural network (RBF-NN) models need at least four meteorological variables to provide acceptable prediction accuracy
Summary
Egypt is facing the challenge of growing water stress owing to limited water resources. Nourani and Sayyah Fard (2012) used a number of commonly used AI models such as artificial neural networks (ANNs) for the modeling of daily evaporative losses They compared the results with those obtained using a classical regression model and confirmed the better performance of the ANN model in predicting evaporation from meteorological variables such as air temperature and solar radiation.
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