Advancing River Water Level Prediction: A Comparative Machine Learning and Deep Learning Approach

Abstract

River water level prediction plays an important role in effective planning and flood risk mitigation. In this study, four standalone machine learning (ML) models, M5Rules, Random Forest (RF), Sequential Minimal Optimization (SMO), and Long Short-Term Memory (LSTM), as well as a hybrid LSTM-RF model, were developed to predict weekly water levels of the Rhine River. The models were trained and tested using data collected between 2006 and 2024. Different scenarios with different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed scenarios and models. The results showed that the performance of the model increased in Scenario 4 with all input variables. Among the standalone models the M5Rule and SMO algorithms perform better with Nash-Sutcliffe Efficiency (NSE) of 0.78, in validation phase, followed by RF (NSE = 0.76) and LSTM (NSE = 0.72). In order to increase the model predictive power, the hybrid model LSTM-RF applied to the input variables of the best scenario and this hybrid model achieved a remarkable accuracy of NSE = 0.98 significantly outperforming standalone models. The findings of this research demonstrated the efficacy of the hybrid LSTM-RF model in capturing the changes in the water level in Rhine River.