4.5 Article

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

期刊

ATMOSPHERE
卷 12, 期 6, 页码 -

出版社

MDPI
DOI: 10.3390/atmos12060701

关键词

pan evaporation; ANN; WANN; SVM-RF; SVM-LF; Pusa station

向作者/读者索取更多资源

This study evaluated the estimation of pan evaporation using different input parameters and various models. The SVM-RF model outperformed others in all scenarios, providing better decision-making support for water managers and planners.
In the present study, estimating pan evaporation (E-pan) was evaluated based on different input parameters: maximum and minimum temperatures, relative humidity, wind speed, and bright sunshine hours. The techniques used for estimating E-pan 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据