Advancing Resource Forecasting: An Evaluation of Hybrid Predictive Models on River Flow Rates

Reliable forecasting of resource availability and demand evolution is paramount for effective community planning. The forecast horizon, which can range from a few days to several years, plays a crucial role in this process. Short-term forecasts allow for the optimization of withdrawals according to demand, while medium-term forecasts enable anticipation of resource scarcity risks, planning operations on sensitive structures, or anticipating demand peaks. Long-term forecasts, on the other hand, help anticipate and analyze development strategies and usage scenarios.
 
This study, a component of the Water Resources Forecast (WRF) SUEZ’s project, partially funded by the French Ecological Transition Agency (ADEME’s innov’eau initiative), introduces an innovative approach to predicting river flow rates. We assess the performance of three distinct model types: traditional lumped rainfall–runoff conceptual models with two reservoirs, classic AI models (Random Forest, LSTM, etc.), and hybrid models that synergize AI and conceptual models for enhanced predictive accuracy.
 
Preliminary findings suggest LSTM and that hybrid models utilizing LSTM demonstrate superior short-term performance, reducing error rates by 41% compared to standalone conceptual models. These results indicate the potential of AI and hybrid models in improving the accuracy of resource availability forecasts. The analysis of the medium and long-term performances of the forecast models is currently underway and the findings will be presented at the conference.
 
This ongoing research contributes to the development of Aquadvanced® Water Resources, a comprehensive platform aimed at monitoring and forecasting various resource types, both underground and surface. By aligning resource availability with water demand forecasts, this tool will facilitate resource management strategies.