Abstract
Flood routing models are vital in predicting floods and taking all necessary precautions in the region where floods occur, preventing loss of life and property in the region and protecting agricultural areas. This study aims to compare the performance of various machine learning models such as Bagged Tree, Gradient-Boosted Machine, Random Forest, K-Nearest Neighbor, Support Vector Machine and Extreme Gradient Boosting for flood routing prediction models in Ankara, Eskişehir and Sivas. In addition, the predictive success of tree-based algorithms established according to the optimized and default parameters was compared. For this purpose, the flood data of 2013, 2014 and 2015 discharge observation stations located in Ankara D12A242-D12A126, D12A170-D12A172 in Eskişehir and D15A290-E15A035 in Sivas were used. While establishing the machine learning (ML) models, the data was selected as 80% training and 20% testing. Model performances were tested according to various statistical indicators such as root mean square error, mean absolute error and determination coefficient. As a result of the study, the Gradient-Boosted Machine was chosen as the most successful model in estimating flood routing. In addition, the K-nearest neighbor model with 3-nearest neighbor achieved high-level prediction success with the lowest error rates in Ankara. The findings are important in terms of flood management and taking necessary precautions before the flood occurs.
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Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
The data used in the study were obtained from the General Directorate of State Hydraulic Works Rasatlar Branch Office and DSI Regional Directorates. Figure 1 was obtained http://cografyaharita.com/haritalarim/2eturkiyenin-akarsular-gollar-haritasi3.png and the date of access is 27 July 2022.
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O. M. Katipoğlu contributed to the data analysis, findings, and conclusions. M. Sarıgöl contributed with data collection, literature review, and writing methods. All authors read and approved the final manuscript.
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Katipoğlu, O.M., Sarıgöl, M. Prediction of flood routing results in the Central Anatolian region of Türkiye with various machine learning models. Stoch Environ Res Risk Assess 37, 2205–2224 (2023). https://doi.org/10.1007/s00477-023-02389-1
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DOI: https://doi.org/10.1007/s00477-023-02389-1