Abstract:
This paper presents an innovative service for river flow forecasting and its
demonstration in two dam-controlled rivers in Portugal, Tejo, and Mondego rivers, based
on using Multilayer Perceptron (MLP) models to predict and forecast river flow. The main
goal is to create and improve AI models that operate as remote services, providing precise
and timely river flow predictions for the next 3 days. This paper examines the use of
MLP architectures to predict river discharge using comprehensive hydrological data from
Portugal’s National Water Resources Information System (Sistema Nacional de Informação
de Recursos Hídricos, SNIRH), demonstrated for the Tejo and Mondego river basins.
The methodology is described in detail, including data preparation, model training, and
forecasting processes, and provides a comparative study of the MLP model’s performance
in both case studies. The analysis shows that MLP models attain acceptable accuracy in
short-term river flow forecasts for the selected scenarios and datasets, adeptly reflecting
discharge patterns and peak occurrences. These models seek to enhance water resources
management and decision-making by amalgamating modern data-driven methodologies
with established hydrological and meteorological data sources, facilitating better flood
mitigation and sustainable water resource planning as well as accurate boundary conditions
for downstream forecast systems.
