With the rise in frequency of catastrophic events, enviromental protection and risk management have become critical challenges for assuring both the safety of human pop-ulations and the sustainability of ecosystems. In this direction, the ARIES project has deveoped a suite of functionalities and software solution to support emergency operators in all phases of emergency management, from risk estimation to enviromental monitoring, early detection and response. The present paper provides an overview of the main functionalities that consistute the buidling blocks of the enviromental monitoring system developed by the project through a combination of dynamical systems, digital twins and convolutional neural networks, also reporting on its early validation activities.

ARIES: An Intelligent System for Landslide and Wildfire Risk Management / Giuseppi, A.; Di Paola, A.; Santopaolo, A.; Saif, S. S.; Fiorini, F.; Pietrabissa, A.. - (2024), pp. 561-566. ( 32nd Mediterranean Conference on Control and Automation (MED) Chania - Crete, Greece ) [10.1109/MED61351.2024.10566274].

ARIES: An Intelligent System for Landslide and Wildfire Risk Management

Giuseppi A.
;
Di Paola A.;Santopaolo A.;Pietrabissa A.
2024

Abstract

With the rise in frequency of catastrophic events, enviromental protection and risk management have become critical challenges for assuring both the safety of human pop-ulations and the sustainability of ecosystems. In this direction, the ARIES project has deveoped a suite of functionalities and software solution to support emergency operators in all phases of emergency management, from risk estimation to enviromental monitoring, early detection and response. The present paper provides an overview of the main functionalities that consistute the buidling blocks of the enviromental monitoring system developed by the project through a combination of dynamical systems, digital twins and convolutional neural networks, also reporting on its early validation activities.
2024
32nd Mediterranean Conference on Control and Automation (MED)
landslide; forest fires; deep learning; intelligent control; decision support system;
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
ARIES: An Intelligent System for Landslide and Wildfire Risk Management / Giuseppi, A.; Di Paola, A.; Santopaolo, A.; Saif, S. S.; Fiorini, F.; Pietrabissa, A.. - (2024), pp. 561-566. ( 32nd Mediterranean Conference on Control and Automation (MED) Chania - Crete, Greece ) [10.1109/MED61351.2024.10566274].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1746648
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