Acta Scientiarum Polonorum
serie Formatio Circumiectus – Environmental Processes
RESEARCH PAPER
Machine Learning-Based Evaporation Prediction and Kernel Function Analysis: A Case Study of the Boukourdane Dam, Algeria
1
University of Jijel, Algeria
2
Badji-Mokhtar Annaba University, Algeria
3
Chandigarh University, Punjab, India
4
University of Tusca, Viterbo, Italy
Submission date: 2025-08-09
Final revision date: 2025-09-06
Acceptance date: 2025-09-16
Publication date: 2026-03-19
Acta Sci. Pol. Formatio Circumiectus 2026;25(1):3-16
HIGHLIGHTS
- Evaporation at Boukourdane Dam was estimated for better water use
- Three machine learning models were tested with the Pearson VII kernel
- Random Forest performed best; humidity was most influential
KEYWORDS
evaporation forecastinggaussian processrandom forest regressionsupport vector machinessensitivity analysis
TOPICS
ABSTRACT
Aim of the study:
The objective of this study is to evaluate the impact of three kernel functions − Pearson VII, radial basis function (RBF), and polynomial − on the predictive performance of Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models.
Material and methods:
Three machine learning models − Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) − were applied to estimate monthly evaporation at Boukourdane Dam, Algeria. The dataset included 240 observations over 20 years, with the following inputs: max./min. air temperature, relative humidity, wind speed, and water temperature; the output being: evaporation.
Results and conclusions:
Model performance was evaluated via Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). RF outperformed GPR and SVR across kernels, achieving MAE = 1.01 mm, RMSE = 1.29 mm, and CC = 0.81 in testing. Moreover, the Pearson VII kernel delivered the highest accuracy within both the GP and SVM frameworks. Sensitivity analysis highlighted relative humidity as the most influential factor in evaporation forecasting.
The objective of this study is to evaluate the impact of three kernel functions − Pearson VII, radial basis function (RBF), and polynomial − on the predictive performance of Support Vector Regression (SVR) and Gaussian Process Regression (GPR) models.
Material and methods:
Three machine learning models − Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) − were applied to estimate monthly evaporation at Boukourdane Dam, Algeria. The dataset included 240 observations over 20 years, with the following inputs: max./min. air temperature, relative humidity, wind speed, and water temperature; the output being: evaporation.
Results and conclusions:
Model performance was evaluated via Correlation Coefficient (CC), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). RF outperformed GPR and SVR across kernels, achieving MAE = 1.01 mm, RMSE = 1.29 mm, and CC = 0.81 in testing. Moreover, the Pearson VII kernel delivered the highest accuracy within both the GP and SVM frameworks. Sensitivity analysis highlighted relative humidity as the most influential factor in evaporation forecasting.
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| ISSN: | 1644-0765 |
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