Wildfire risk prediction in Southeastern Mississippi using population interaction

Sadasivuni, Ravi R.; Cooke, William H.; Bhushan, Shanti

doi:10.1016/j.ecolmodel.2012.12.024

Cited by 10 publications

(15 citation statements)

References 12 publications

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“…The trend in much of the world is towards landscapes with more ignitions [44], and these ignitions are often associated with increased land-use intensity [45,46]. This is because in most areas humans are responsible for starting most fires.…”

Section: Land Use Change and Ignition Frequencymentioning

confidence: 99%

Land Use and Wildfire: A Review of Local Interactions and Teleconnections

Butsic

Kelly

Moritz

2015

Land

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Abstract:Fire is a naturally occurring process of most terrestrial ecosystems as well as a tool for changing land use. Since the beginning of history humans have used fire as a mechanism for creating areas suitable for agriculture and settlement. As fires threaten human dominated landscapes, fire risk itself has become a driver of landscape change, impacting landscapes through land use regulations and fire management. Land use changes also influence fire ignition frequency and fuel loads and hence alters fire regimes. The impact of these changes is often exacerbated as new land users demand alternative fire management strategies, which can impact land cover and management far from where land use change has actually occurred. This creates nuanced land use teleconnections between source areas for fires and economic cores, which demand and fund fire protection. Here we will review the role of fire and fire risk as a driver of land use change, the ways land use changes impact drivers of fire, and suggest that the integration of land use teleconnections into the fire/land use discussion can help us better understand and manage the complex interactions between fire and land use.

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Section: Land Use Change and Ignition Frequencymentioning

confidence: 99%

Land Use and Wildfire: A Review of Local Interactions and Teleconnections

Butsic

Kelly

Moritz

2015

Land

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“…The overarching goal of this project is to develop a geographic information system (GIS) tool to obtain the ignition potential map of an area, which can be combined with meteorology and fuel type and distribution maps to assist in wildfire management and prevention. This research builds on the previous wildfire ignition prediction modeling efforts by the authors (Gilreath 2006;Grala and Cooke 2010;Sadasivuni et al 2013) to develop a state-ofthe-art model that can depict a range of human activity patterns.…”

Section: Objective Modeling Philosophy and Approachmentioning

confidence: 99%

“…Sadasivuni et al (2013) developed such a model, wherein a gravity model was used to measure the interaction among cities. The city interactions provided a measure of movement of people, which was used as a model for wildfire ignition.…”

Section: Review Of Wildfire Hazard Prediction and Spatial-interactionmentioning

confidence: 99%

“…1, the factors influencing wildfire ignition potential are grouped as ''global'' (population interaction) and ''local'' (parameters of transportation corridors) variables. The global variables, or global human interaction patterns, can be best related to a pressure gradient term, and are modeled using a gravity term following Sadasivuni et al (2013). The local variables are modeled as convective/diffusive fluxes across the local boundaries.…”

Section: Objective Modeling Philosophy and Approachmentioning

confidence: 99%

“…For the city interaction analysis, road density including both designated and county roads, traffic volume, and city population are used. A fuel layer based on the vegetation age-species combination derived from Landsat Thematic Mapper image mosaics and scenes from the Spatial Information Technology Laboratory at Mississippi State University Department of Forestry was analyzed previously (Openshaw 1998;Sadasivuni et al 2013), and made available to the authors. The historical wildfire data were analyzed for better understanding of the distribution pattern of anthropogenically-initiated wildfire events in the southeast Mississippi region.…”

Section: Observational Datamentioning

confidence: 99%

See 2 more Smart Citations

Convection–Diffusion Model for the Prediction of Anthropogenically-Initiated Wildfire Ignition

Sadasivuni

Bhushan

Cooke

2014

Int J Disaster Risk Sci

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A spatial interaction model to predict anthropogenically-initiated accidental and incendiary wildfire ignition probability is developed using fluid flow analogies for human movement patterns. Urban areas with large populations are identified as the sites of global influencing factors, and are modeled as the gravity term. The transportation corridors are identified as local influencing factors, and are modeled using fluid flow analogy as diffusion and convection terms. The model is implemented in Arc-GIS, and applied for the prediction of wildfire hazard distribution in southeastern Mississippi. The model shows 87 % correlation with historic data in the winter season, whereas the previously developed gravity model shows only 75 % correlation. The normalized error for convection-diffusion model predictions is about 5 % in the winter season, whereas the gravity model shows an error of 7 %. The proposed model is robust as it couples a multi-criteria behavioral pattern within a single dynamic equation to enhance predictive capability. At the same time, the proposed model is more costly than the gravity model as it requires evaluation of distance from intermodal transportation corridors, transportation corridor density, and traffic volume maps. Nonetheless, the model is developed in a modular fashion, such that either global or local terms can be neglected if required.

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Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning

P¹,

Sadasivuni²,

Nóbrega

et al. 2021

Multi Criteria Decision Anal

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Modelling infectious diseases is a complex and multi‐disciplinary problem that necessitates the combined use of multicriteria decision analysis (MCDA) and machine learning (ML) in a spatial framework. This research attempts to demonstrate the extensive applications of MCDA in the field of public health and to illustrate its utility with the combined use of spatial models and machine learning. The study investigates the risk factors for communicable diseases with a focus on vector‐borne infectious diseases, such as West Nile Virus (WNV), malaria, dengue, etc. It aims to quantify vector‐borne disease risk by examining the geographic contextual effects of socio‐economic, climatic, and environmental factors using the objective‐weighting technique adopted from MCDA and machine learning in a geographic information systems (GIS) framework. The authors attempted to minimize subjective bias from the decision space by utilizing an objective‐weighted technique to quantify the risk. The study adopted Shannon's entropy to derive weights for each factor and its classes. The derived weighted layers are fed to an artificial neural network to obtain a final map of risk susceptibility. This final risk map allows policymakers to examine vulnerable areas and identify the factors pivotal to the contribution of risk. Findings show the traffic volume as the most influential variable, and terrain slope as the least one in the disease spread for the study area. The risk appears to be concentrated and distributed along vegetation, wetlands, and around water bodies. The results produced by ensemble learning show great promise with more than 94% accuracy. The accuracy of the results was determined by the confusion matrix and the kappa index of agreement (KIA). The vector control programmes need to adapt to better manage the dynamic changes in patterns involving vector‐borne infectious diseases.

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Wildfire risk prediction in Southeastern Mississippi using population interaction

Cited by 10 publications

References 12 publications

Land Use and Wildfire: A Review of Local Interactions and Teleconnections

Land Use and Wildfire: A Review of Local Interactions and Teleconnections

Convection–Diffusion Model for the Prediction of Anthropogenically-Initiated Wildfire Ignition

Application of spatial multicriteria decision analysis in healthcare: Identifying drivers and triggers of infectious disease outbreaks using ensemble learning

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