ORIGINAL RESEARCH
Flood Routing Calculation with ANN, SVM, GPR,
and RTE Methods
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1
Erzincan Üzümlü Vocational School, Erzincan Binali Yıldırım University, Üzümlü, Erzincan, Turkey
2
Department of Civil Engineering, Erzincan Binali Yıldırım University, Erzincan, Turkey
Submission date: 2022-04-30
Final revision date: 2022-06-21
Acceptance date: 2022-06-22
Online publication date: 2022-09-12
Publication date: 2022-11-03
Corresponding author
Metin Sarıgöl
Erzincan Binali Yildirim University,Uzumlu Vocational School, Turkey
Pol. J. Environ. Stud. 2022;31(6):5221-5228
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ABSTRACT
Flood routing analysis is of great importance in predicting floods and taking all necessary
precautions in the area where the flood occurred. By applying different machine learning algorithms
to historical flood data, the success of the established models in flood routing calculations has been
measured. In this study, flood hydrograph data on 05.05.2014 was passed through the training and testing
stages using the Support Vector Machine (SVM), Gaussian Process Regression (GPR), Regression Tree
Ensembles (RTE), and Artificial Neural Networks (ANN) methods, then routing calculations were made
by applying the flood data on 03.06.2015 to these models. The results were compared with either ANN,
SVM, GPR, RTE, or measured values. Root Mean Square Errors (RMSE) and Correlation Coefficient
(R) values were calculated at the end of the comparative analysis. In conclusion, it has been determined
that verifying the flood routing results performed by the SVM is the best result.