Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops
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 p...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-09-01
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| Series: | Ecological Processes |
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| Online Access: | https://doi.org/10.1186/s13717-022-00400-1 |
| _version_ | 1811211768994725888 |
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| author | Terenzio Zenone Luca Vitale Daniela Famulari Vincenzo Magliulo |
| author_facet | Terenzio Zenone Luca Vitale Daniela Famulari Vincenzo Magliulo |
| author_sort | Terenzio Zenone |
| collection | DOAJ |
| description | 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 R 2 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 R 2 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. |
| first_indexed | 2024-04-12T05:18:06Z |
| format | Article |
| id | doaj.art-5f9f18465e5149b1b8547c5ed33f18df |
| institution | Directory Open Access Journal |
| issn | 2192-1709 |
| language | English |
| last_indexed | 2024-04-12T05:18:06Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Ecological Processes |
| spelling | doaj.art-5f9f18465e5149b1b8547c5ed33f18df2022-12-22T03:46:34ZengSpringerOpenEcological Processes2192-17092022-09-0111111410.1186/s13717-022-00400-1Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn cropsTerenzio Zenone0Luca Vitale1Daniela Famulari2Vincenzo Magliulo3National Research Council, Institute for Agricultural and Forestry Systems in the MediterraneanNational Research Council, Institute for Agricultural and Forestry Systems in the MediterraneanNational Research Council CNR, Institute for BioeconomyNational Research Council, Institute for Agricultural and Forestry Systems in the MediterraneanAbstract 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 R 2 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 R 2 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.https://doi.org/10.1186/s13717-022-00400-1Energy fluxEvapotranspirationEddy covarianceArtificial intelligence |
| spellingShingle | Terenzio Zenone Luca Vitale Daniela Famulari Vincenzo Magliulo Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops Ecological Processes Energy flux Evapotranspiration Eddy covariance Artificial intelligence |
| title | Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops |
| title_full | Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops |
| title_fullStr | Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops |
| title_full_unstemmed | Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops |
| title_short | Application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops |
| title_sort | application of machine learning techniques to simulate the evaporative fraction and its relationship with environmental variables in corn crops |
| topic | Energy flux Evapotranspiration Eddy covariance Artificial intelligence |
| url | https://doi.org/10.1186/s13717-022-00400-1 |
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