http://repositorio.lnec.pt:8080/jspui/handle/123456789/1007780 2026-04-04T21:10:31Z 2026-04-04T21:10:31Z Martins, R. Azevedo, A. Jesus, G. Oliveira, A. Fortunato, A. B. Oliveira , F. Nahon, A. Freire, P. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1016705 2023-11-21T11:03:10Z 2022-06-01T00:00:00Z

Automatic identification of the wave runup line from camera images Martins, R.; Azevedo, A.; Jesus, G.; Oliveira, A.; Fortunato, A. B.; Oliveira , F.; Nahon, A.; Freire, P. We propose a novel methodology for an automated coastline runup detection from high-resolution remote camera images. As part of a multi-source integrated flood risk assessment platform, this methodology will further improve the characterization of the beach hydrodynamics and define automated procedures for the surveillance of coastal overtopping and overwash.

2022-06-01T00:00:00Z Korani, Z. Moin, A. Silva, A. Ferreira, J. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1016000 2023-02-28T12:26:20Z 2023-01-01T00:00:00Z

Model-driven engineering techniques and tools for machine learning-enabled IoT applications: a scoping review Korani, Z.; Moin, A.; Silva, A.; Ferreira, J. This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.

2023-01-01T00:00:00Z Barros, J. Tavares, A. Santos, P. Freire, P. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1015612 2023-02-28T10:53:09Z 2022-08-01T00:00:00Z

Enhancing a coastal territorial vulnerability index: anticipating the impacts of coastal flooding with a local scale approach Barros, J.; Tavares, A.; Santos, P.; Freire, P. The coastal zone of mainland Portugal is characterized by its morpho-sedimentary diversity such as estuaries, lagoons, barrier islands, beaches, dunes and cliffs. The high population density and the multiplicity of land use, occupation and activities, makes it an area of great national strategic value. This transforms the coastal zone into a multi-hazard zone, where the occurrences related to coastal flooding and overtopping stand out. In the present work, a multidi mensional methodology called Coastal Territorial Vulnerability Index (CTVI) was developed and applied in three selected areas with a historical record of coastal impacts, to analyze, evaluate and interpret the local vulnerability. The methodology considers four components of coastal territorial vulnerability: morphology, land value, buildings and public areas characteristics. These four components are combined to calculate the CTVI. The results highlight the differences for the analyzed areas, allowing the differentiation of natural and artificial areas. In the natural areas a moderate CTVI predominates, while in the latter, a high and very high CTVI stands out. The results contribute to the development of a comprehensive coastal flood risk assessment and forecasting the impacts

2022-08-01T00:00:00Z Jesus, G. Oliveira, A. Rogeiro, J. Rodrigues, R. Fernandes, J. N. http://repositorio.lnec.pt:8080/jspui/handle/123456789/1015363 2022-11-04T11:27:11Z 2022-07-01T00:00:00Z

A reliable monitoring approach of floods in small and high slope watersheds Jesus, G.; Oliveira, A.; Rogeiro, J.; Rodrigues, R.; Fernandes, J. N. An implementation to instantiate a dependable data quality-oriented methodology in the Vinhas Creek monitoring network is presented herein. Redundancy was taken as a core aspect of network reliability. In this instantiation, we implement several machine learning mechanisms to process measurements from the multiple sensors while correlating them according to their geographical position, monitoring timing and the relevant physical processes involved. As an output, we are able to predict the sensor measurements and compare them with the actual sensing value obtained in the monitoring network station. Moreover, in case of any sensor failure, one or more replacement values can be issued. These are important for the correct simulation of the hydrologic and hydraulic processes of the dendritic watershed systems and to predict the inundation characteristics such as levels and flow velocities.

2022-07-01T00:00:00Z