Next Article in Journal
Development of a Building-Scale Meteorological Prediction System Including a Realistic Surface Heating
Previous Article in Journal
Fine-Scale Modeling of Individual Exposures to Ambient PM2.5, EC, NOx, and CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study
Previous Article in Special Issue
Study on Wind Simulations Using Deep Learning Techniques during Typhoons: A Case Study of Northern Taiwan
Open AccessArticle

Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis

1
Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz 51666, Iran
2
Department of Farm Structures and Irrigation, Faculty of Agriculture, Ankara University, Ankara, Turkey
3
Institute of Automation, Kalman Kando Faculty of Electrical Engineering, Obuda University, 1034 Budapest, Hungary
4
Institute of Structural Mechanics, Bauhaus University Weimar, D-99423 Weimar, Germany
5
Faculty of Health, Queensland University of Technology, Queensland 4059, Australia
6
School of the Built Environment, Oxford Brookes University, Oxford OX3 0BP, UK
7
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
8
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Viet Nam
9
Department of Mathematics and Informatics, J. Selye University, 94501 Komarno, Slovakia
*
Author to whom correspondence should be addressed.
Atmosphere 2020, 11(1), 66; https://doi.org/10.3390/atmos11010066
Received: 4 November 2019 / Revised: 27 December 2019 / Accepted: 31 December 2019 / Published: 4 January 2020
Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters. View Full-Text
Keywords: machine learning; meteorological parameters; pan evaporation; advanced statistical analysis; hydrological cycle; big data; hydroinformatics; random forest (RF); support vector regression (SVR) machine learning; meteorological parameters; pan evaporation; advanced statistical analysis; hydrological cycle; big data; hydroinformatics; random forest (RF); support vector regression (SVR)
Show Figures

Figure 1

MDPI and ACS Style

Shabani, S.; Samadianfard, S.; Sattari, M.T.; Mosavi, A.; Shamshirband, S.; Kmet, T.; Várkonyi-Kóczy, A.R. Modeling Pan Evaporation Using Gaussian Process Regression K-Nearest Neighbors Random Forest and Support Vector Machines; Comparative Analysis. Atmosphere 2020, 11, 66.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop