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Effectiveness of Integrating Ensemble-Based Feature Selection and Novel Gradient Boosted Trees in Runoff Prediction: A Case Study in Vu Gia Thu Bon River Basin, Vietnam.
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- Author(s): Aiyelokun, Oluwatobi; Pham, Quoc Bao; Aiyelokun, Oluwafunbi; Linh, Nguyen Thi Thuy; Roy, Tirthankar; Anh, Duong Tran; Łupikasza, Ewa
- Source:
Pure & Applied Geophysics; May2024, Vol. 181 Issue 5, p1725-1744, 20p- Subject Terms:
- Source:
- Additional Information
- Subject Terms:
- Abstract: Traditional rainfall-runoff modeling techniques require large datasets and often an exhaustive calibration process, which is challenging, especially in poorly-gauged basins and resource-limited settings. Therefore, it is necessary to examine new ways of constructing predictive models for runoff that can achieve satisfactory results, while also minimizing the data requirement and model construction time. In this study, the effectiveness of integrating the Random Forest (RF) as an important feature identifier with novel gradient boosted trees to achieve satisfactory results was examined for two adjacent catchments in Vietnam. Antecedent daily runoff in combination with daily and one-day antecedent rainfall was found to significantly influence the runoff at the outlet of the catchments. Categorical Boosting (CatBoost) and Extreme Gradient Boosting (XGBoost) were effective in predicting day-ahead runoff. For instance, CatBoost with NSE, d, r, and R2 values of 0.92, 0.98, 0.96, and 0.92, respectively, and XGBoost with NSE, d, r, and R2 values of 0.91, 0.98, 0.96, and 0.92, respectively, are well suited for predicting runoff. A comparative analysis of their results with previous studies revealed that the models were very effective since they were able to better reduce generalization errors at different calibration and validation phases. This study presents the integration of RF and gradient boosted trees as a simplified alternative to computationally expensive and data-intensive physically-based rainfall-runoff models. The practitioners can build upon the experimentation presented in this study to minimize the computational time requirement, construction process complexity, and data requirement, which are often serious constraints in physically-based rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Pure & Applied Geophysics is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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