Comparative Analysis of a LSTM and a Rainfall-Runoff Model for Catchment Runoff Simulation: Advancing Hydrological Modelling and Forecasting

Hydrological modelling is an important tool for understanding and predicting runoff behaviour in catchments. It is essential for flood risk management and flood forecasting. This study conducts a comparative analysis of two modelling approaches: a Long Short-Term Memory (LSTM) neural network model and a conventional rainfall-runoff model. Both models are used to simulate runoff dynamics in a catchment in Bavaria, Germany.

LSTM models are known for their ability to capture temporal dependencies and nonlinear relationships in sequential data. This research aims to comprehensively evaluate and compare the performance, accuracy, and predictive capabilities of the physical rainfall-runoff model widely used in hydrology against the LSTM model. The objective is to replicate the intricate processes governing rainfall-induced runoff. This study analyses the ability of the LSTM model to predict runoff patterns by leveraging historical hydrological data and meteorological inputs. The model learns from temporal sequences of precipitation and other relevant factors. The traditional rainfall-runoff model, which operates on established hydrological principles and parameterizations, is also assessed for its accuracy in simulating runoff within the same catchment. The comparison includes assessments of prediction accuracy, model robustness under varying conditions, computational efficiency, and the ability to capture the complex non-linear relationships inherent in hydrological processes.

The results of this study have important implications for the further development of hydrological modelling techniques. Understanding the comparative strengths and limitations of the LSTM model against the conventional rainfall-runoff model provides valuable insights for improving the accuracy and reliability of runoff predictions. Such information can improve decision making in flood risk management, assist in more accurate flood forecasting and help reduce the loss of human life. By identifying the comparative effectiveness of these modelling approaches in reproducing the complex dynamics of runoff, this research aims to advance the field of hydrological modelling and pave the way for more robust and accurate prediction tools.