Evaluating the performance of Random Forest, Decision Tree, Support Vector Regression and Gradient Boosting for streamflow prediction
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Abstract
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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