Abstract
ABSTRACTAccurate simulation of evaporation plays an important role in the efficient management of water Resources. Generally, evaporation is measured using the direct method where Class A pan-evaporimeter is used, and an indirect method that includes empirical equations. However, despite its widespread usage, Class A pan-evaporimeter method can be affected by human and instrumentation errors. Empirical equations, on the other hand, are generally linked to the different climatic factors that should provide initial or boundary conditions in the mathematical equations that affect the rate of evaporation. Considering these challenging, heuristic soft computing approaches that do not need key information about the physics of evaporation. In this study, a Quantum-behaved Particle Swarm Optimization algorithm, embedded into a multi-layer perceptron technique, is developed to estimate the evaporation rates over a daily forecast horizon. The measured evaporation data from 2012–2014 for Talesh meteorological station located in Northern Iran are employed. The predictive accuracy of the MLP-QPSO model is evaluated with existing methods: i.e. a hybrid MLP-PSO and a standalone MLP model. The results are evaluated in respect to statistical performance criterion: the mean absolute error, root mean square error (RMSE), Willmott's Index and the Nash–Sutcliffe coefficient. In conjunction with these metrics, Taylor diagrams are also utilized to assess the level of agreement between the forecasted and observed evaporation data. Evidently, the hybrid MLP-QPSO model is confirmed to be an optimal forecasting tool applied for estimating daily pan evaporation, outperforming both the hybrid MLP-PSO and the standalone model.In light of these results, the present study justifies the potential utility of the hybrid MLP-QPSO model to be applied for estimating daily evaporation rates in North of Iran.
Highlights
Evaporation is an integral component of the global hydrological cycle
Referring to the results of the testing period, it was evident that the multi-layer perceptron and Particle Swarm Optimization (MLP-Particle Swarm Optimization (PSO)) method was capable to estimate daily evaporation amounts more sufficient than the standalone multi-layer perceptron (MLP) method
In the face of the similar tendency for the standalone MLP and the hybrid MLP-PSO models, the efficiency of the MLP-PSO hybrid model was much better than the standalone MLP method
Summary
Evaporation is an integral component of the global hydrological cycle. the accurate estimation of evaporation rates using novel learning algorithms is a vital task for hydrologic engineering, water resources management and agriculture, in arid and semi-arid regions (Deo & Samui, 2017; Deo, Samui, & Kim, 2016; Goyal, Bharti, Quilty, Adamowski, & Pandey, 2014; Kim, Shiri, Singh, Kisi, & Landeras, 2015; Kisi, 2015). It is important to note that the physical processes related to evaporation rates are highly non-linear (Kisi, 2007), consistent and powerful forecasting methods should be able to analyze the non-linear trends related with the predictor variables for the evaporation rate, Over the last several decades, the evolution of soft computing, data-driven learning algorithms coupled with new artificial intelligence (AI) tools (Deo et al, 2016; Garousi-Nejad, Bozorg-Haddad, & Loáiciga, 2016) based on artificial neural network-multi-layer perceptron and Particle Swarm Optimization (MLP-PSO) have successfully led to an extensive number of investigations that attempted to model the evaporation rates As these models are completely non-parametric (and assumptionfree), they provide considerable advantage compared to the physically-based models. As a result of this advantage, the AI-models have a greater applicability for practical implementations that require estimated evaporation rates
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