In our latest Performance Report, we analyze BWI’s discharge forecast model’s performance for the L’Ouine – Le Moulin des Alleuds catchment.
As climate change amplifies hydrological extremes, such as droughts and floods, regular monitoring of water resources is becoming increasingly important for optimal water resource management.
BWI employs a semi-distributed hydrological model. With the integration of machine learning techniques, it monitors and forecasts river discharges. This blog post delves into a detailed performance evaluation of BWI’s discharge prediction model, specifically focusing on the ‘L’Ouine’ river, with its outlet located near Le Moulin des Alleuds in Nouvelle – Aquitaine.
L’Ouine is one of Sèvre Nantaise’s main tributaries. The Sèvre Nantaise is 141.8 km long, and is frequented by fishermen, boaters and local wildlife. It is home to several water mills which have benefited because of it’s waters. The Sèvre Nantaise mainly flows in the two regions of Pays de la Loire and Nouvelle -Aquitaine of France.
The river flows through the beautiful landscapes of the Vendée and Loire-Atlantique regions in western France. It originates from various tributaries, including the Sèvre Nantaise Supérieure and the Maine. The river makes it way through the rustic countryside, picturesque villages, before joining the Loire River near the city of Nantes.
BWI utilizes a robust model performance evaluation framework that considers a range of metrics. Some of these include Nash-Sutcliffe model efficiency scores (NSE), normalized NSE (NNSE), Kling-Gupta Efficiency (KGE), and their respective modified and normalized versions. The preferred metric within this framework is NNSE, which ranges between 0 and 1. A higher NNSE score indicates superior model performance. This metric is particularly useful for tracking the model’s performance over time and effectively communicating the results.
At the Le Moulin des Alleuds outlet, the model achieves an NNSE of 0.82. This is a score that scientific publications classify as indicative of good model performance (NNSE > 0.65). The optimal parameter sets and the final state of the basin at the end of the training are recorded and utilized to initialize the subsequent training batch.
