DHA/Chefiahttp://repositorio.lnec.pt:8080/jspui/handle/123456789/512026-10-26T10:08:15Z2026-10-26T10:08:15ZConvergir fluxos para reforçar a resiliênciaAlegre, H.http://repositorio.lnec.pt:8080/jspui/handle/123456789/10194132026-05-05T16:28:03Z2025-12-01T00:00:00ZConvergir fluxos para reforçar a resiliência
Alegre, H.
2025-12-01T00:00:00ZShaping water infrastructure futures in the european union contextAlegre, H.http://repositorio.lnec.pt:8080/jspui/handle/123456789/10190342026-05-04T15:34:25Z2025-11-04T00:00:00ZShaping water infrastructure futures in the european union context
Alegre, H.
This entry explores how foresight approaches can guide the future of water infrastructures.
It highlights key long-term disruptive drivers of change—such as climate change, digital
transformation, and geopolitical tensions—that infrastructures must withstand and adapt
to. It also emphasizes the role of collective choices and innovation alliances, including
Water-Oriented Living Labs, in shaping resilient and sustainable water systems. The focus
is on transforming today’s infrastructures into adaptive systems that ensure water security
and ecosystem integrity for future generations. Although many of the drivers of change
are global, this entry emphasizes the European context, where policy frameworks and
innovation agendas are currently shaping infrastructure transitions
2025-11-04T00:00:00ZDeep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego RiversJesus, G.Korani, Z.Alves, E.Oliveira, A.http://repositorio.lnec.pt:8080/jspui/handle/123456789/10187132025-07-21T13:14:45Z2025-03-28T00:00:00ZDeep Learning-Based River Flow Forecasting with MLPs: Comparative Exploratory Analysis Applied to the Tejo and the Mondego Rivers
Jesus, G.; Korani, Z.; Alves, E.; Oliveira, A.
Yuh-Shyan Chen; Wei Yi
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.
2025-03-28T00:00:00ZPerdas de água e reabilitação dos sistemas de abastecimento de águaAlegre, H.Loureiro, D.http://repositorio.lnec.pt:8080/jspui/handle/123456789/10186082025-07-21T12:49:20Z2025-03-01T00:00:00ZPerdas de água e reabilitação dos sistemas de abastecimento de água
Alegre, H.; Loureiro, D.
2025-03-01T00:00:00Z