Virtual Fire: A web-based GIS platform for forest fire control

Kalabokidis, Kostas; Athanasis, Nikolaos; Gagliardi, Fabrizio; Karayiannis, Fotis; Palaiologou, Palaiologos; Parastatidis, Savas; Vasilakos, Christos

doi:10.1016/j.ecoinf.2013.04.007

Cited by 62 publications

(42 citation statements)

References 21 publications

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“…Similar techniques have also been employed by Asgary et al (2010) and Corcoran et al (2011a). Further, coupled with various data collection sensors, web-based GIS platforms can support locating fire service resources and identifying high-risk areas in a real-time manner (Kalabokidis et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

J.¹,

Zhang²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Fire and rescue service is one of the fundamental public services provided by government in order to protect people, properties and environment from fires and other disasters, and thus promote a safer living environment. Well understanding spatial-temporal dynamics of fire incidents can offer insights for potential determinants of various fire events and enable better fire risk estimation, assisting future allocation of prevention resources and strategic planning of mitigation programs. Using a 12-year (2002-2013) dataset containing the urban fire events in Nanjing, China, this research explores the spatial-temporal dynamics of urban fire incidents. A range of exploratory spatial data analysis (ESDA) approaches and tools, such as spatial kernel density and co-maps, are employed to examine the spatial, temporal and spatial-temporal variations of the fire events. Particular attention has been paid to two types of fire incidents: residential properties and local facilities, due to their relatively higher occurrence frequencies. The results demonstrated that the amount of urban fire has greatly increased in the last decade and spatial-temporal distribution of fire events vary among different incident types, which implies varying impact of potential influencing factors for further investigation.

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Section: Related Workmentioning

confidence: 99%

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

J.¹,

Zhang²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Valuable assistance and decision support tools can be then provided to local authorities responsible for wildfire management to extract useful information as part of an operational wildfire prevention and management plan. The comparison of AEGIS with previously developed wildfire management systems (Kalabokidis et al, 2013a(Kalabokidis et al, , 2014b reveals that AEGIS covers the full range of forest fire management and prevention, except for damage assessment (Table 5). …”

Section: Discussionmentioning

confidence: 99%

“…On the practical side, applications developed by Caballero (1998), Vakalis et al (2004) and Kalabokidis et al (2012) resulted in stand-alone systems that were difficult to use due to requirements for local installations of specific software components. The first attempt to develop a fully integrated and distributed system for Greece was Virtual Fire (Kalabokidis et al, 2013a), a web-based GIS platform designed to deliver a wide range of forest fire related information. It provided spatial fire ignition probability estimation (Vasilakos et al, 2007(Vasilakos et al, , 2009), visualization of weather forecast maps, real-time weather data from Remote Automatic Weather Stations (RAWS) and fire management tools (e.g., fleet tracking, routing, closest facilities and RSS feeds).…”

Section: Introductionmentioning

confidence: 99%

AEGIS: a wildfire prevention and management information system

Kalabokidis

Ager

Finney

et al. 2016

Nat. Hazards Earth Syst. Sci.

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Abstract. We describe a Web-GIS wildfire prevention and management platform (AEGIS) developed as an integrated and easy-to-use decision support tool to manage wildland fire hazards in Greece (http://aegis.aegean.gr). The AEGIS platform assists with early fire warning, fire planning, fire control and coordination of firefighting forces by providing online access to information that is essential for wildfire management. The system uses a number of spatial and non-spatial data sources to support key system functionalities. Land use/land cover maps were produced by combining field inventory data with high-resolution multispectral satellite images (RapidEye). These data support wildfire simulation tools that allow the users to examine potential fire behavior and hazard with the Minimum Travel Time fire spread algorithm. End-users provide a minimum number of inputs such as fire duration, ignition point and weather information to conduct a fire simulation. AEGIS offers three types of simulations, i.e., single-fire propagation, point-scale calculation of potential fire behavior, and burn probability analysis, similar to the FlamMap fire behavior modeling software. Artificial neural networks (ANNs) were utilized for wildfire ignition risk assessment based on various parameters, training methods, activation functions, pre-processing methods and network structures. The combination of ANNs and expected burned area maps are used to generate integrated output map of fire hazard prediction. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps. The system and associated computation algorithms leverage parallel processing techniques (i.e., High Performance Computing and Cloud Computing) that ensure computational power required for real-time application. All AEGIS functionalities are accessible to authorized end-users through a web-based graphical user interface. An innovative smartphone application, AEGIS App, also provides mobile access to the webbased version of the system.

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“…Decision support systems have the ability to integrate diverse aspects of the planning process, and may take into account the spatial dimension. As such, they have been broadly used in the management of natural resources, including water [44,45], fire [46,47], or wind [48]. Decision support systems may be used to produce scenarios or forecasts [49] and may encompass a large number of qualitative or quantitative variables.…”

Section: Methodological Precedents Research Objectives and Noveltiesmentioning

confidence: 99%

Mapping the Viability, Time, and Cost of Manual Borehole Drilling in Developing Regions

Martínez‐Santos

Martín-Loeches

Solera

et al. 2017

Water

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While access to water remains an issue in arid and semiarid regions across the world, aquifers have the potential to help millions of people out of poverty by providing a reliable source of drinking and irrigation water. Manual boreholes are increasingly advocated as a safe and cost-effective substitute to mechanized drilling, as well as to traditional excavation methods. This research banks on the assumption that field and remote sensing data can be integrated within a geospatial database in order to map the viability of manual boreholes based on factors such as rock type, water table depth, landforms, or water quality. The approach presents three main novelties in relation to methodological precedents: (1) outcomes are not only expressed in terms of technical feasibility, but also as a function of drilling time and cost; (2) maps refer to a specific drilling technique; and (3) results take into account borehole diameter, as this constrains both drilling time and cost. The method provides univocal outcomes that can be immediately useful for non-experts, donors, planners, or practitioners and that can be readily exported to other catchment-scale settings. Results were validated against geophysical data.

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Virtual Fire: A web-based GIS platform for forest fire control

Cited by 62 publications

References 21 publications

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

Spatial-Temporal Dynamics of Urban Fire Incidents: A Case Study of Nanjing, China

AEGIS: a wildfire prevention and management information system

Mapping the Viability, Time, and Cost of Manual Borehole Drilling in Developing Regions

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