http://repositorio.lnec.pt:8080/jspui/handle/123456789/51 Mon, 26 Oct 2026 10:06:36 GMT 2026-10-26T10:06:36Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019413 Convergir fluxos para reforçar a resiliência Alegre, H. Mon, 01 Dec 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019413 2025-12-01T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019034 Shaping 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 Tue, 04 Nov 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1019034 2025-11-04T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018713 Deep 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. Fri, 28 Mar 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018713 2025-03-28T00:00:00Z http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018608 Perdas de água e reabilitação dos sistemas de abastecimento de água Alegre, H.; Loureiro, D. Sat, 01 Mar 2025 00:00:00 GMT http://repositorio.lnec.pt:8080/jspui/handle/123456789/1018608 2025-03-01T00:00:00Z