Abstract
Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.
Highlights
The results of regression analysis on all input parameters showed that Tmin, relative humidity (RH), sunshine hours (SSH), and wind speed (WS) by having absolute standard coefficients (0.404, −0.516, 0.132, and 0.336) were identified as the most influential input at two meteorological stations
The accuracy of five machine learning methods (i.e., multivariate adaptive regression splines (MARS), multi-model artificial neural network (MM-artificial neural networks (ANNs)), support vector machine (SVM), multi-gene genetic programming (MGGP), and M5Tree) to predict the monthly pan evaporation in India was evaluated by Malik et al [2] which revealed a similar outcome with SVM being more prolific
They observed that the MM-ANN and MGGP algorithms had superior prediction performance when compared with the MARS and SVM algorithms, as well as the M5Tree method, as shown by their high levels of root mean square error (RMSE)
Summary
The hydrological process by which the liquid water from water bodies and landmass is converted to vapor and transferred to the atmosphere is known as evaporation, which is a significant constituent member of the hydrological cycle. The driving factor for this process is the pressure gradient between the atmosphere–earth system [1,2]. Water scarcity has become a serious concern and evaporation losses have increased significantly during the last few decades; precise estimation of evaporation is crucial, in regions of limited water resources [3,4,5]. Pan evaporation (EPd ) has been extensively used in
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