Evaluating the Accuracy of Machine Learning, Deep Learning and Hybrid Algorithms for Flood Routing Calculations

The increase in average temperatures in the last century has caused global warming, which has increased the frequency of natural disasters. Floods are one of the most important natural disasters and harm the environment and especially human life. Flood routing techniques also play an important role in predicting floods. For this reason, the accuracy and precision of flood routing calculations are of vital importance in taking all necessary precautions before the floods reach the region and in preventing loss of life. This study aims to compare the performance of machine learning, deep learning and hybrid algorithms for flood routing prediction models in the B & uuml;y & uuml;k Menderes River. In this research deep learning model Long-Short Term Memory (LSTM), machine learning model Artificial Neural Network (ANN), and hybrid machine learning models empirical mode decomposition (EMD)-ANN, and particle swarm optimization (PSO)-ANN algorithms were compared to forecast the flood routing results in two discharge observation stations in the B & uuml;y & uuml;k Menderes river. The analysis results of the established ML algorithms were compared with statistical criteria such as mean error, mean absolute error, root mean square error and coefficient of determination. Additionally, Taylor diagrams, box plots, and beeswarm plot visual graphs were also used in this comparison analysis. At the end of the research, it was determined that the hybrid algorithm PSO-ANN was the most successful algorithm in forecasting flood routing results among other models according to the error values of MAE: 0.2514, MSE: 0.4613, RMSE: 0.6791, NSE: 0.941 and MBE: 0.047. Moreover, the LSTM algorithm was the approach with second estimation accuracy. The findings are vital in terms of taking necessary precautions and gaining time before floods reach any region.