Marc Demange;Alessia Di Fonso;Gabriele Di Stefano;Pierpaolo Vittorini
{"title":"Instantiating a Diffusion Network Model to Support Wildfire Management","authors":"Marc Demange;Alessia Di Fonso;Gabriele Di Stefano;Pierpaolo Vittorini","doi":"10.1109/TNSE.2025.3559681","DOIUrl":null,"url":null,"abstract":"Wildfires require effective responses considering multiple constraints and conflicting goals. We provide a methodology and a tool enabling stakeholders to compute risk maps and use them in practical and realistic scenarios. The territory is modeled as a network where nodes are land patches subject to fire and links model the probability of fire spread from one patch to another. We discuss a risk function and show how to compute it effectively. We show how to instantiate the model on a real landscape. The methodology describes how to compute each patch's borders and probabilities of ignition and how to estimate the probability of fire spreading from one patch to a neighboring one. We embed the methodology into an ad-hoc modular tool-chain using geographical data, a fire simulator and geospatial tools. As a proof-of-concept, the tool-chain is applied in three different experiments on a region of Corsica, France, aiming at simulating a realistic scenario and measuring the sensitivity of the methodology with increasing wind speed or variable wind directions. We finally introduce the web application that incorporates the tool-chain and enables users to manipulate the model intuitively through an interactive map, evaluate what-if scenarios, and simulate the effects of different fire preventive measures.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"3374-3388"},"PeriodicalIF":6.7000,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Network Science and Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10964192/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Wildfires require effective responses considering multiple constraints and conflicting goals. We provide a methodology and a tool enabling stakeholders to compute risk maps and use them in practical and realistic scenarios. The territory is modeled as a network where nodes are land patches subject to fire and links model the probability of fire spread from one patch to another. We discuss a risk function and show how to compute it effectively. We show how to instantiate the model on a real landscape. The methodology describes how to compute each patch's borders and probabilities of ignition and how to estimate the probability of fire spreading from one patch to a neighboring one. We embed the methodology into an ad-hoc modular tool-chain using geographical data, a fire simulator and geospatial tools. As a proof-of-concept, the tool-chain is applied in three different experiments on a region of Corsica, France, aiming at simulating a realistic scenario and measuring the sensitivity of the methodology with increasing wind speed or variable wind directions. We finally introduce the web application that incorporates the tool-chain and enables users to manipulate the model intuitively through an interactive map, evaluate what-if scenarios, and simulate the effects of different fire preventive measures.
期刊介绍:
The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.