Version 1
: Received: 30 July 2019 / Approved: 31 July 2019 / Online: 31 July 2019 (10:58:29 CEST)
How to cite:
Shabani, S.; Samadianfard, S.; Taghi Sattari, M.; Shamshirband, S.; Mosavi, A.; Kmet, T.; R. Várkonyi-Kóczy, A. Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression. Preprints2019, 2019070351. https://doi.org/10.20944/preprints201907.0351.v1
Shabani, S.; Samadianfard, S.; Taghi Sattari, M.; Shamshirband, S.; Mosavi, A.; Kmet, T.; R. Várkonyi-Kóczy, A. Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression. Preprints 2019, 2019070351. https://doi.org/10.20944/preprints201907.0351.v1
Shabani, S.; Samadianfard, S.; Taghi Sattari, M.; Shamshirband, S.; Mosavi, A.; Kmet, T.; R. Várkonyi-Kóczy, A. Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression. Preprints2019, 2019070351. https://doi.org/10.20944/preprints201907.0351.v1
APA Style
Shabani, S., Samadianfard, S., Taghi Sattari, M., Shamshirband, S., Mosavi, A., Kmet, T., & R. Várkonyi-Kóczy, A. (2019). Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression. Preprints. https://doi.org/10.20944/preprints201907.0351.v1
Chicago/Turabian Style
Shabani, S., Tibor Kmet and Annamária R. Várkonyi-Kóczy. 2019 "Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression" Preprints. https://doi.org/10.20944/preprints201907.0351.v1
Abstract
Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations 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 outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It 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 performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.
Keywords
evaporation; meteorological parameters; Gaussian process regression; support vector regression; machine learning modeling; hydrology; prediction; data science; hydroinformatics
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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