Abstract
Abstract
Background
The evaporative fraction (EF) represents an important biophysical parameter reflecting the distribution of surface available energy. In this study, we investigated the daily and seasonal patterns of EF in a multi-year corn cultivation located in southern Italy and evaluated the performance of five machine learning (ML) classes of algorithms: the linear regression (LR), regression tree (RT), support vector machine (SVM), ensembles of tree (ETs) and Gaussian process regression (GPR) to predict the EF at daily time step. The adopted methodology consisted of three main steps that include: (i) selection of the EF predictors; (ii) comparison of the different classes of ML; (iii) application, cross-validation of the selected ML algorithms and comparison with the observed data.
Results
Our results indicate that SVM and GPR were the best classes of ML at predicting the EF, with a total of four different algorithms: cubic SVM, medium Gaussian SVM, the Matern 5/2 GPR, and the rational quadratic GPR. The comparison between observed and predicted EF in all four algorithms, during the training phase, were within the 95% confidence interval: the R2 value between observed and predicted EF was 0.76 (RMSE 0.05) for the medium Gaussian SVM, 0.99 (RMSE 0.01) for the rational quadratic GPR, 0.94 (RMSE 0.02) for the Matern 5/2 GPR, and 0.83 (RMSE 0.05) for the cubic SVM algorithms. Similar results were obtained during the testing phase. The results of the cross-validation analysis indicate that the R2 values obtained between all iterations for each of the four adopted ML algorithms were basically constant, confirming the ability of ML as a tool to predict EF.
Conclusion
ML algorithms represent a valid alternative able to predict the EF especially when remote sensing data are not available, or the sky conditions are not suitable. The application to different geographical areas, or crops, requires further development of the model based on different data sources of soils, climate, and cropping systems.
Publisher
Springer Science and Business Media LLC
Subject
Ecological Modeling,Ecology
Reference89 articles.
1. Alexandridis T, Cherif I, Chemin Y, Silleos G, Stavrinos E, Zalidis G (2009) Integrated methodology for estimating water use in mediterranean agricultural areas. Remote Sens 1:445–465
2. Allen RG, Tasumi M, Trezza R (2007) Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model. J Irrig Drain Eng 133:380–394
3. Anderson MC, Norman JM, Mecikalski JR, Otkin JA, Kustas WP (2007) A climatological study of evapotranspiration and moisture stress across the continental United States based on the thermal remote sensing: 1. Model formulation. J Geophys Res-Atmos 112:D10117
4. Anurag M, Yazid T, Nadhir AA, Shamsuddin ShH, Harkanwaljot S, Sekhon RKP, Priya Rai KP, Padam S, Ahmed E, Saad S (2021) Daily pan-evaporation estimation in different agro-climatic zones using novel hybrid support vector regression optimized by Salp swarm algorithm in conjunction with gamma test. Eng Appl Comput Fluid Mech 15(1):1075–1094
5. Aybar-Ruiz A, Jiménez-Fernández S, Cornejo-Bueno L, Casanova-Mateo C, Sanz-Justo J, Salvador-González P, Salcedo-Sanz S (2016) A novel grouping genetic algorithm - extreme learning machine approach for global solar radiation prediction from numerical weather models inputs. Sol Energy 132:129–142
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