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

被引:124
|
作者
Shabani, Sevda [1 ]
Samadianfard, Saeed [1 ]
Sattari, Mohammad Taghi [1 ,2 ]
Mosavi, Amir [3 ,4 ,5 ,6 ]
Shamshirband, Shahaboddin [7 ,8 ]
Kmet, Tibor [9 ]
Varkonyi-Koczy, Annamaria R. [3 ,9 ]
机构
[1] Univ Tabriz, Fac Agr, Dept Water Engn, Tabriz 51666, Iran
[2] Ankara Univ, Fac Agr, Dept Farm Struct & Irrigat, Ankara, Turkey
[3] Obuda Univ, Kalman Kando Fac Elect Engn, Inst Automat, H-1034 Budapest, Hungary
[4] Bauhaus Univ Weimar, Inst Struct Mech, D-99423 Weimar, Germany
[5] Queensland Univ Technol, Fac Hlth, Brisbane, Qld 4059, Australia
[6] Oxford Brookes Univ, Sch Built Environm, Oxford OX3 0BP, England
[7] Ton Duc Thang Univ, Dept Management Sci & Technol Dev, Ho Chi Minh City, Vietnam
[8] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam
[9] J Selye Univ, Dept Math & Informat, Komarno 94501, Slovakia
关键词
machine learning; meteorological parameters; pan evaporation; advanced statistical analysis; hydrological cycle; big data; hydroinformatics; random forest (RF); support vector regression (SVR); GLOBAL SOLAR-RADIATION; CROP EVAPOTRANSPIRATION; PREDICTION; REQUIREMENTS; REGIONS;
D O I
10.3390/atmos11010066
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:17
相关论文
共 32 条
  • [1] Prediction of Beef Production Using Linear Regression, Random Forest and k-Nearest Neighbors Algorithms
    Yildiz, Berkant Ismail
    Karabag, Kemal
    KSU TARIM VE DOGA DERGISI-KSU JOURNAL OF AGRICULTURE AND NATURE, 2025, 28 (01): : 247 - 255
  • [2] ANALYSIS OF CUSTOMER CHURN PREDICTION USING LOGISTIC REGRESSION, k-NEAREST NEIGHBORS, DECISION TREE AND RANDOM FOREST ALGORITHMS
    Atay, Mehmet Tarik
    Turanli, Munevver
    ADVANCES AND APPLICATIONS IN STATISTICS, 2025, 92 (02) : 147 - 169
  • [3] Prediction of Breast Cancer Using Support Vector Machine and K-Nearest Neighbors
    Islam, Md. Milon
    Iqbal, Hasib
    Haque, Md. Rezwanul
    Hasan, Md. Kamrul
    2017 IEEE REGION 10 HUMANITARIAN TECHNOLOGY CONFERENCE (R10-HTC), 2017, : 226 - 229
  • [4] Concrete Strength Prediction Using Machine Learning Methods CatBoost, k-Nearest Neighbors, Support Vector Regression
    Beskopylny, Alexey N.
    Stel'makh, Sergey A.
    Shcherban', Evgenii M.
    Mailyan, Levon R.
    Meskhi, Besarion
    Razveeva, Irina
    Chernil'nik, Andrei
    Beskopylny, Nikita
    APPLIED SCIENCES-BASEL, 2022, 12 (21):
  • [5] Modeling High Pan Evaporation Losses Using Support Vector Machine, Gaussian Processes, and Regression Tree Models
    Alsumaiei, Abdullah A.
    JOURNAL OF HYDROLOGIC ENGINEERING, 2024, 29 (05)
  • [6] Diabetes Prediction using Decision Tree, Random Forest, Support Vector Machine, K- Nearest Neighbors, Logistic Regression Classifiers
    Peerbasha, S.
    Raja, A. Saleem
    Praveen, K. P.
    Iqbal, Y. Mohammed
    Surputheen, Mohamed
    JOURNAL OF ADVANCED APPLIED SCIENTIFIC RESEARCH, 2023, 5 (04): : 42 - 54
  • [7] Comparative Analysis of Support Vector Machine, Random Forest and k-Nearest Neighbor Classifiers for Predicting Remaining Usage Life of Roller Bearings
    Palaniappan R.
    Informatica (Slovenia), 2024, 48 (07): : 39 - 52
  • [8] Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features
    Saberioon, Mohammadmehdi
    Cisar, Petr
    Labbe, Laurent
    Soucek, Pavel
    Pelissier, Pablo
    Kerneis, Thierry
    SENSORS, 2018, 18 (04)
  • [9] Classifying Crowdsourced Citizen Complaints through Data Mining: Accuracy Testing of k-Nearest Neighbors, Random Forest, Support Vector Machine, and AdaBoost
    Madyatmadja, Evaristus D.
    Sianipar, Corinthias P. M.
    Wijaya, Cristofer
    Sembiring, David J. M.
    INFORMATICS-BASEL, 2023, 10 (04):
  • [10] Heart Plaque Detection with Improved Accuracy using K-Nearest Neighbors classifier Algorithm in comparison with Least Squares Support Vector Machine
    Kumar, Vankamaddi Sunil
    Vidhya, K.
    CARDIOMETRY, 2022, (25): : 1590 - 1594