Towards a Dynamic Data Driven Wildfire Behavior Prediction System at European Level

Artés, Tomàs; Cencerrado, Andrés; Cortés, Ana; Margalef, Tomás; Rodriguez-Aseretto, Dario; Thomas, P.; San-Miguel-Ayanz, Jesús

doi:10.1016/j.procs.2014.05.109

Cited by 15 publications

(8 citation statements)

References 13 publications

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“…In addition, they do not track the local evolution characteristics of different parts of the continuous object and thus cannot provide decision support using predictive modeling. Dynamic Data Driven Application Systems (DDDAS) [16]- [19] try to address some of these severe limitations. Their main objective is to continuously improve the quality of predictions produced by hazard-specific models (mathematical, semi-empirical, etc.)…”

Section: Introductionmentioning

confidence: 99%

Predictive Tracking of Continuous Object Boundaries Using Sparse Local Estimates

Manatakis

Μανωλάκος

2020

IEEE Access

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Environmental hazards (wildfires, floods, oil spills) are often modeled as ''continuous objects'' which evolve in space and time taking irregular shapes. Tracking their boundaries and accurately predicting their spatiotemporal spreading patterns is of paramount importance to combat their often catastrophic consequences. Wireless Sensor Networks (WSN) have been very instrumental for this purpose. However, current WSN-based methods require a prohibitively large density of deployed sensors to achieve a reasonable boundary reconstruction accuracy because they are based on the explicit identification of nodes close to the boundary. We present a novel approach that can track and predict the global boundary using only a relatively small number of distributed local front estimates. Our approach first filters and fuses the available sparse set of local front estimates (e.g. vectors of local orientation, direction and speed) and then uses the resulting information to reconstruct a smooth curve prediction of the evolving object's boundary at a future time. Moreover, since the uncertainty of the local front parameters is modeled, it can provide a heatmap representation of the evolving object, indicating the probability for each point in the area of interest to be reached at a future time by the spreading hazard. These predictive modeling capabilities when combined enable effective decision support for crisis management. We demonstrate that different types of continuous objects can be tracked with accuracy, even when only a relatively small number of noisy local front estimates is available. Our approach is practical since it can be applied in many situations where global boundary prediction updates are important to obtain by new sparsely distributed noisy local front estimates as soon as they reach a control center while the hazard is still progressing.INDEX TERMS Continuous object tracking, predictive modeling, hazards tracking, boundary reconstruction algorithm, environmental monitoring.

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Section: Introductionmentioning

confidence: 99%

Predictive Tracking of Continuous Object Boundaries Using Sparse Local Estimates

Manatakis

Μανωλάκος

2020

IEEE Access

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“…Prior DDDAS approaches have demonstrated improvements in simulation accuracy under controlled conditions, especially when infield sensors offer high-quality measurements of a wildfire's front-line [21]- [24]. However, the limited efforts to validate wildfire DDDAS approaches using real-time, noisy measurements from real wildfires have not proved as successful [25]. This is why in [26] we have introduced a flexible data assimilation approach, where we first calibrate the mechanism producing the wildfire simulation model's output before attempting to adjust its input parameters when significant simulation drift is observed.…”

Section: Introductionmentioning

confidence: 99%

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Bogdos

Μανωλάκος

2019

IEEE Access

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Wildfires are natural hazards with severe consequences worryingly worsening for many climate-change affected regions of our planet. Unfortunately, technologies that can provide real-time fireline information, such as satellites, in-field sensors, and social media texts, exhibit low spatial/temporal resolution or cannot be deployed cost-effectively in widespread geographical areas. We present the design, development, and implementation of a novel software service, called CITISENS, which by exploiting commodity smartphone sensors allows ordinary citizens to easily georeference a fire-line in real-time and report its coordinates as they are photographing a wildfire. The location/orientation sensors and the camera are used to compute the view-ray of the smartphone, and a digital elevation model is employed to estimate the ray's intersection with the topography. We have tested the georeferencing accuracy obtained and it is to be on par with, or even better, than that of existing satellite wildfire hotspot services. When combined with FLogA, a flexible wildfire spread simulator we have also developed, CITISENS offers the following unique advantages: real-time prediction of burn probabilities, dynamic assimilation of citizen-reported hotspots into ongoing simulations for improved predictive accuracy, and decision support to issue citizen alarms based on the estimated time-dependent risk at their location due to an approaching wildfire.

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“…This avoids specific treatment of the front markers [52]. This also avoids the use of morphing that involves a complex registration mapping [7] and provides valuable spatial information compared to usual similarity scores [6,23]. To this end, our goal is to rely on a front shape similarity measure introduced in the context of data assimilation for cardiac electrophysiology [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Front Shape Similarity Measure for Shape-Oriented Sensitivity Analysis and Data Assimilation for Eikonal Equation

et al. 2018

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We present a shape-oriented data assimilation strategy suitable for front-tracking problems through the example of wildfire. The concept of “front” is used to model, at regional scales, the burning area delimitation that moves, undergoes shape and topological changes under heterogeneous orography, biomass fuel and micrometeorology. The simulation-observation discrepancies are represented using a front shape similarity measure deriving from image processing and based on the Chan-Vese contour fitting functional. We show that consistent corrections of the front location and uncertain physical parameters can be obtained using this measure applied on a level-set fire growth model solving for an eikonal equation. This study involves a Luenberger observer for state estimation, including a topological gradient term to track multiple fronts, and of a reduced-order Kalman filter for joint parameter estimation. We also highlight the need – prior to parameter estimation – for sensitivity analysis based on the same discrepancy measure, and for instance using polynomial chaos metamodels, to ensure a meaningful inverse solution is achieved. The performance of the shape-oriented data assimilation strategy is assessed on a synthetic configuration subject to uncertainties in front initial position, near-surface wind magnitude and direction. The use of a robust front shape similarity measure paves the way toward the direct assimilation of infrared images and is a valuable asset in the perspective of data-driven wildfire modeling.

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Towards a Dynamic Data Driven Wildfire Behavior Prediction System at European Level

Cited by 15 publications

References 13 publications

Predictive Tracking of Continuous Object Boundaries Using Sparse Local Estimates

Predictive Tracking of Continuous Object Boundaries Using Sparse Local Estimates

Crowd-Sourced Wildfire Spread Prediction With Remote Georeferencing Using Smartphones

Front Shape Similarity Measure for Shape-Oriented Sensitivity Analysis and Data Assimilation for Eikonal Equation

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