Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods

Compound channels play an important role in hydraulic and hydrological engineering. Many rivers and streams have naturally compound channels, which are important for both flood control and river ecosystems. When predicting discharge, interval predictions are usually more realistic and reliable than traditional point predictions made using ML models. In this study, the authors used three well-known machine learning techniques (Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Artificial Neural Networks (ANN)) to predict compound channel discharge. To accurately assess uncertainty, two resampling techniques were employed: Bootstrap (B) and Jackknife after Bootstrap (JB). The proposed approach reflects the impacts of statistical uncertainties using prediction confidence intervals and the reliability of point prediction. The JB resampling technique has proven to offer superior accuracy in terms of point prediction and prediction intervals when compared to Bootstrap. Bootstrapping did not yield superior outcomes compared with the standalone ML models. The JB-ANN and JB-ANFIS models outperformed the JB-SVM and Bootstrap ML models in the testing phase, as shown by various statistical measures. This study suggests that the JB-ANN approach is a reliable and robust tool for accurately predicting compound discharges. The findings reveal that when the models have low accuracy in point prediction, the level of uncertainty in predicting the interval also increases. The innovation of this research is enhancing the examination of uncertainties in compound channel discharge by incorporating two resampling techniques to analyze three different ML models.