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Evapotranspiration is one of the most important components of the hydrological cycle as it accounts for more than two-thirds of the global precipitation losses. Indeed, the accurate prediction of reference evapotranspiration (ET o) is highly significant for many watershed activities, including agriculture, water management, crop production and several other applications. Therefore, reliable estimation of ET o is a major concern in hydrology. ET o can be estimated using different approaches, including field measurement, empirical formulation and mathematical equations. Most recently, advanced machine learning models have been developed for the estimation of ET o. Among several machine learning models, evolutionary computing (EC) has demonstrated a remarkable progression in the modeling of ET o. The current research is devoted to providing a new milestone in the implementation of the EC algorithm for the modeling of ET o. A comprehensive review is conducted to recognize the feasibility of EC models and their potential in simulating ET o in a wide range of environments. Evaluation and assessment of the models are also presented based on the review. Finally, several possible future research directions are proposed for the investigations of ET o using EC.

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.

Interest in quantum computing has increased significantly. Tensor network theory has become increasingly popular and widely used to simulate strongly entangled correlated systems. Matrix product state (MPS) is a well-designed class of tensor network states that plays an important role in processing quantum information. In this letter, we show that MPS, as a one-dimensional array of tensors, can be used to classify classical and quantum data. We have performed binary classification of the classical machine learning data set Iris encoded in a quantum state. We have also investigated its performance by considering different parameters on the ibmqx4 quantum computer and proved that MPS circuits can be used to attain better accuracy. Furthermore the learning ability of an MPS quantum classifier is tested to classify evapotranspiration (ET[Formula: see text]) for the Patiala meteorological station located in northern Punjab (India), using three years of a historical data set (Agri). We have used different performance metrics of classification to measure its capability. Finally, the results are plotted and the degree of correspondence among values of each sample is shown.