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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

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.

Details

Title
Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India
Author
Nand Lal Kushwaha 1   VIAFID ORCID Logo  ; Rajput, Jitendra 1 ; Elbeltagi, Ahmed 2   VIAFID ORCID Logo  ; Elnaggar, Ashraf Y 3   VIAFID ORCID Logo  ; Sena, Dipaka Ranjan 1 ; Vishwakarma, Dinesh Kumar 4   VIAFID ORCID Logo  ; Mani, Indra 1 ; Hussein, Enas E 5   VIAFID ORCID Logo 

 Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi 110012, India; [email protected] (N.L.K.); [email protected] (D.R.S.); [email protected] (I.M.) 
 Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, Egypt; [email protected] 
 Department of Food Nutrition Science (Previously Chemistry), College of Science, Taif University, Taif 21944, Saudi Arabia; [email protected] 
 Department of Irrigation and Drainage Engineering, G.B. Pant University of Agriculture and Technology, Pantnagar 263145, India; [email protected] 
 National Water Research Center, P.O. Box 74, Shubra El-Kheima 13411, Egypt 
First page
1654
Publication year
2021
Publication date
2021
Publisher
MDPI AG
e-ISSN
20734433
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2612751836
Copyright
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.