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7. Towards Knowledge-Driven Automatic Service Composition for Wildfire Prediction

Author

Chirine Ghedira Guegan, Michael Mrissa, Khouloud Boukadi, Faiez Gargouri, Hela Taktak, Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, and InnoRenew CoE

Subjects
fire prediction, Earth observation, Computer science, 020207 software engineering, 02 engineering and technology, Service composition, Data science, Intervention (law), machine learning, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, service composition, [INFO]Computer Science [cs], Weather satellite, ComputingMilieux_MISCELLANEOUS
Abstract

Wildfire prediction from Earth Observation (EO) data has gained much attention in the past years, through the development of connected sensors and weather satellites. Nowadays, it is possible to extract knowledge from collected EO data and to learn from this knowledge without human intervention to trigger wildfire alerts. However, exploiting knowledge extracted from multiple EO data sources at run-time and predicting wildfire raise multiple challenges. One major challenge is to provide dynamic construction of service composition plans, according to the data obtained from sensors. In this paper, we present a knowledge-driven Machine Learning approach that relies on historical data related to wildfire observations to guide the collection of EO data and to automatically and dynamically compose services for triggering wildfire alerts.

Published
2021
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8. PREDICAT: A Semantic Service-Oriented Platform for Data Interoperability and Linking in Earth Observation and Disaster Prediction

Author

Bernard Archimède, Hela Taktak, Hajer Baazaoui Zghal, Sana Ben Abdallah Ben Lamine, Chirine Ghedira Guegan, Khouloud Boukadi, Maroua Masmoudi, Mohamed Hedi Karray, Michael Mrissa, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), Université de la Manouba - UMA (TUNISIA), InnoRenew CoE (SLOVENIA), Université de Lyon - UDL (FRANCE), Université Jean Moulin Lyon 3 (FRANCE), Université de Sfax (TUNISIA), Laboratoire d'InfoRmatique en Image et Systèmes d'information - LIRIS (Villeurbanne, France), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Multimedia, InfoRmation systems and Advanced Computing Laboratory (MIRACL), Faculté des Sciences Economiques et de Gestion de Sfax (FSEG Sfax), Université de Sfax - University of Sfax-Université de Sfax - University of Sfax, Service Oriented Computing (SOC), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Franche-Comté Électronique Mécanique, Thermique et Optique - Sciences et Technologies (UMR 6174) (FEMTO-ST), Université de Technologie de Belfort-Montbeliard (UTBM)-Ecole Nationale Supérieure de Mécanique et des Microtechniques (ENSMM)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de recherche en Génie Logiciel, Applications distribuées, Systèmes décisionnels et Imagerie intelligente [Manouba] (RIADI), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA)-Université de la Manouba [Tunisie] (UMA), Laboratoire Génie de Production (LGP), Ecole Nationale d'Ingénieurs de Tarbes, and InnoRenew CoE

Subjects
[SPI.OTHER]Engineering Sciences [physics]/Other, Earth observation, Computer science, [SDE.MCG]Environmental Sciences/Global Changes, Ocean temperature, Big data, Interoperability, 0211 other engineering and technologies, Services computing, 02 engineering and technology, Ontology (information science), computer.software_genre, Meteorology, Autre, 0202 electrical engineering, electronic engineering, information engineering, Milieux et Changements globaux, 021101 geological & geomatics engineering, business.industry, Earth, Data science, Semantics, Data access, Data integration, 020201 artificial intelligence & image processing, Web service, business, computer
Abstract

International audience; The increasing volume of data generated by earth observation programs such as Copernicus, NOAA, and NASA Earth Data, is overwhelming. Although these programs are very costly, data usage remains limited due to lack of interoperability and data linking. In fact, multi-source and heterogeneous data exploitation could be significantly improved in different domains especially in the natural disaster prediction one. To deal with this issue, we introduce the PREDICAT project that aims at providing a semantic service-oriented platform to PREDIct natural CATastrophes. The PREDICAT platform considers (1) data access based on web service technology; (2) ontology-based interoperability for the environmental monitoring domain; (3) data integration and linking via big data techniques; (4) a prediction approach based on semantic machine learning mechanisms. The focus in this paper is to provide an overview of the PREDICAT platform architecture. A scenario explaining the operation of the platform is presented based on data provided by our collaborators, including the international intergovernmental Sahara and Sahel Observatory (OSS).

Published
2018
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