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

Abstract

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.

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

“…The distance to health care and the availability of physicians can affect the spatial distribution of risk. Spatial interaction models (SIM) (Sadasivuni et al, 2014; Sadasivuni et al, 2013) are widely used to characterize intangible interactions that are difficult to model like the flow of movement/spillovers (Haynes & Fotheringham, 1984). Then, the investigation employs the gravity model (Sadasivuni et al, 2013), a SIM model to evaluate the influence between health care facilities and the population.…”