Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction
This study utilized a range of machine learning algorithms to predict the hourly streamflow in the Ikpoba River. Data gathering relied on a Hydromet System installed along the river, collecting hourly measurements of gage height, ambient temperature, and atmospheric pressure. To convert the gage he...
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| Language: | English |
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Can Tho University Publisher
2024-07-01
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| Series: | CTU Journal of Innovation and Sustainable Development |
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| Online Access: | https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/827 |
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| author | Osahon Idemudia Jacob Odeh Ehiorobo Christopher Osadolor Izinyon Idowu Ilaboya |
| author_facet | Osahon Idemudia Jacob Odeh Ehiorobo Christopher Osadolor Izinyon Idowu Ilaboya |
| author_sort | Osahon Idemudia |
| collection | DOAJ |
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This study utilized a range of machine learning algorithms to predict the hourly streamflow in the Ikpoba River. Data gathering relied on a Hydromet System installed along the river, collecting hourly measurements of gage height, ambient temperature, and atmospheric pressure. To convert the gage height to streamflow data, historical gage and streamflow data covering the period from 2015 to 2020 were extracted from the Ikpoba River rating curve and were analyzed using curve fitting techniques to establish the precise relationship between streamflow and gage height. Various goodness-of-fit measures, such as adjusted R-squared value, standard error of estimate, and coefficient of determination, were utilized to identify the best-fit relationship. The estimated streamflow data were subsequently validated using the Soil and Water Assessment Tool, incorporating the digital elevation model of the study area, along with other input parameters like soil, slope, daily maximum precipitation, and daily maximum temperature. Validation results were illustrated using regression plots generated in Microsoft Excel. From the machine learning results, random forest algorithm outperformed other methods in predicting streamflow, with a mean square error of 0.02 and a coefficient of determination of 0.98. Conversely, decision trees showed superior accuracy in predicting individual data points, with the lowest root-mean-square error of 0.02.
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| format | Article |
| id | doaj-art-9c2fb5c450ea448cb05fb6ec16a72268 |
| institution | DOAJ |
| issn | 2588-1418 2815-6412 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Can Tho University Publisher |
| record_format | Article |
| series | CTU Journal of Innovation and Sustainable Development |
| spelling | doaj-art-9c2fb5c450ea448cb05fb6ec16a722682025-08-20T03:12:50ZengCan Tho University PublisherCTU Journal of Innovation and Sustainable Development2588-14182815-64122024-07-0116210.22144/ctujoisd.2024.297Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow predictionOsahon Idemudia0Jacob Odeh Ehiorobo1Christopher Osadolor Izinyon2Idowu Ilaboya3University of BeninUniversity of BeninUniversity of Benina:1:{s:5:"en_US";s:19:"University of Benin";} This study utilized a range of machine learning algorithms to predict the hourly streamflow in the Ikpoba River. Data gathering relied on a Hydromet System installed along the river, collecting hourly measurements of gage height, ambient temperature, and atmospheric pressure. To convert the gage height to streamflow data, historical gage and streamflow data covering the period from 2015 to 2020 were extracted from the Ikpoba River rating curve and were analyzed using curve fitting techniques to establish the precise relationship between streamflow and gage height. Various goodness-of-fit measures, such as adjusted R-squared value, standard error of estimate, and coefficient of determination, were utilized to identify the best-fit relationship. The estimated streamflow data were subsequently validated using the Soil and Water Assessment Tool, incorporating the digital elevation model of the study area, along with other input parameters like soil, slope, daily maximum precipitation, and daily maximum temperature. Validation results were illustrated using regression plots generated in Microsoft Excel. From the machine learning results, random forest algorithm outperformed other methods in predicting streamflow, with a mean square error of 0.02 and a coefficient of determination of 0.98. Conversely, decision trees showed superior accuracy in predicting individual data points, with the lowest root-mean-square error of 0.02. https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/827Machine Learning, Random Forest (RF), Decision Tress (DT), Support Vector Regression (SVR), and Gradient Boosting (GB) |
| spellingShingle | Osahon Idemudia Jacob Odeh Ehiorobo Christopher Osadolor Izinyon Idowu Ilaboya Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction CTU Journal of Innovation and Sustainable Development Machine Learning, Random Forest (RF), Decision Tress (DT), Support Vector Regression (SVR), and Gradient Boosting (GB) |
| title | Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction |
| title_full | Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction |
| title_fullStr | Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction |
| title_full_unstemmed | Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction |
| title_short | Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction |
| title_sort | evaluating the performance of random forest decision tree support vector regression and gradient boosting for streamflow prediction |
| topic | Machine Learning, Random Forest (RF), Decision Tress (DT), Support Vector Regression (SVR), and Gradient Boosting (GB) |
| url | https://ctujs.ctu.edu.vn/index.php/ctujs/article/view/827 |
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