The Superiority of Data-Driven Techniques for Estimation of Daily Pan Evaporation

Abstract

In the present study, estimating pan evaporation (Epan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating Epan were the artificial neural network (ANN), wavelet-based ANN (WANN), radial function-based support vector machine (SVM-RF), linear function-based SVM (SVM-LF), and multi-linear regression (MLR) models. The proposed models were trained and tested in three different scenarios (Scenario 1, Scenario 2, and Scenario 3) utilizing different percentages of data points. Scenario 1 includes 60%: 40%, Scenario 2 includes 70%: 30%, and Scenario 3 includes 80%: 20% accounting for the training and testing dataset, respectively. The various statistical tools such as Pearson’s correlation coefficient (PCC), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and Willmott Index (WI) were used to evaluate the performance of the models. The graphical representation, such as a line diagram, scatter plot, and the Taylor diagram, were also used to evaluate the proposed model’s performance. The model results showed that the SVM-RF model’s performance is superior to other proposed models in all three scenarios. The most accurate values of PCC, RMSE, NSE, and WI were found to be 0.607, 1.349, 0.183, and 0.749, respectively, for the SVM-RF model during Scenario 1 (60%: 40% training: testing) among all scenarios. This showed that with an increase in the sample set for training, the testing data would show a less accurate modeled result. Thus, the evolved models produce comparatively better outcomes and foster decision-making for water managers and planners.

Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India

“…Throughout the investigation, pan evaporation (EP d ) measurement and predicted values were compared. Statistical assessment was done to compare the accuracy of the applied algorithms (i.e., SVM, RT, REPTree, and RSS) using the root mean square error (RMSE) [8,10,44], mean absolute error (MAE) [7,45], Nash-Sutcliffe efficiency (NSE) [4,45], Willmott index (WI) [8], and correlation coefficient (r) [19]. In addition, qualitative performance was evaluated through graphical scrutiny.…”

“…Daily pan evaporation (EP d ) has been extensively used in irrigation scheduling, water balance studies, sustainable water resources management, and hydrological modelling, etc. [2,[6][7][8].…”

“…In the advent of soft computing tools, developments in data-driven models using metaheuristic algorithms have been incorporated to model various hydrological processes. The most common meta-heuristic algorithms are support vector machine (SVM) [2,8,10], random tree (RT), artificial neural networks (ANNs), M5 pruning tree (M5P) [2,16,17], reduced error pruning tree (REPTree), multivariate adaptive regression splines (MARS) [2,13,16], extreme learning machine (ELM), gene expression programming (GEP), and random subspace (RSS). Furthermore, their hybrids with a variety of algorithms have been efficiently used to estimate pan evaporation [14,18,19].…”

“…Other wavelet analysis techniques, such as the least-squares wavelet analysis were successfully applied to analyze and forecast climate and hydrologic data [22]. Several studies [1,2,5,8,10,21] applied various metaheuristic algorithms for estimation of pan evaporation in specific agro-climatic regions and endorsed the applicability of meta-heuristic algorithms.…”

Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test

“…The results of this study were compared with the previous studies conducted on modeling pan-evaporation using several artificial intelligence (AI) techniques optimized by bio-inspired algorithms (Kumar et al, 2021;Majhi et al, 2020;Qasem et al, 2019;Salih et al, 2019;Singh et al, 2021). The previous studies reported the effective utility of hybrid AI methods for pan-evaporation at different locations in varying climates through statistical metrics and visual investigation.…”