Relieving the Effects of Uncertainty in Forest Fire Spread Prediction by Hybrid MPI-OpenMP Parallel Strategies

Artés, Tomàs; Cencerrado, Andrés; Margalef, Tomás

doi:10.1016/j.procs.2013.05.399

Cited by 25 publications

(15 citation statements)

References 13 publications

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“…In this respect, an obvious question arises as to whether or not other exogenous disturbances such as fire and wind have the same effect on growth forecasts as those we observed for SBW. Introducing exogenous disturbances is subject to high levels of uncertainty (Artés et al, 2013;Cencerrado et al, 2015). Relying on the spatial synchrony theory (Williams and Liebhold, 2000), it is reasonable to assume that large forest fires and severe windstorms would greatly impact some realizations, while others would remain untouched.…”

Section: Discussionmentioning

confidence: 99%

The effect of natural and anthropogenic disturbances on the uncertainty of large-area forest growth forecasts

Melo

Schneider

Fortin

2019

Forestry: An International Journal of Forest Research

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This study aimed to estimate the contribution of disturbances to the uncertainty of forest growth forecasts in the Bas-Saint-Laurent region in Quebec, Canada. We focused on two major disturbances affecting that region: spruce budworm (SBW) outbreaks and harvest activities. Growth forecasts were carried out for a period of 100 years (2003-2103) using ARTEMIS-2009, a stochastic individual-based model. Using the Monte Carlo technique, we simulated four scenarios: a baseline; a harvest scenario; a SBW scenario; and a scenario including both harvest and SBW. Uncertainty estimation was performed using a bootstrap variance estimator that applies to the context of hybrid inference. The results revealed that the total variances increased over time. For the scenarios including SBW, the variances were three to six times greater than those in the scenarios without outbreaks. Harvesting did not greatly contribute to the total variance. We conclude that to reduce the uncertainty of large-area growth forecasts in the Bas-Saint-Laurent, considering SBW dynamics is a crucial issue.

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Section: Discussionmentioning

confidence: 99%

The effect of natural and anthropogenic disturbances on the uncertainty of large-area forest growth forecasts

Melo

Schneider

Fortin

2019

Forestry: An International Journal of Forest Research

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“…For this reason, we rely on a methodology for classifying the scenarios according to how long their simulation will take before their execution, based on the use of Decision Trees [ 26 ] as classification strategy. This methodology is widely discussed in previous works, such as [ 27 , 28 ], and allows us to carry out a premature detection of lengthy simulations so that we can avoid their inclusion in the adjustment process. As it will be seen in Section 4 , this represents a very important advantage for the success of our proposed methods.…”

Section: Methodology For Time Response and Quality Assessment For mentioning

confidence: 99%

“…As stated in Section 2.1 , we rely on a Decision Tree-based method which allows us to rapidly assess the simulation time a certain configuration of the scenario will produce, without the necessity of executing it [ 27 , 28 ]. Therefore, by means of the premature detection and discard of lengthy simulations, we are able to limit the elapsed time in the adjustment stage.…”

Section: Experimental Study: Ga Characterization Based On mentioning

confidence: 99%

Applying Probability Theory for the Quality Assessment of a Wildfire Spread Prediction Framework Based on Genetic Algorithms

Cencerrado

Cortés

Margalef

2013

The Scientific World Journal

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This work presents a framework for assessing how the existing constraints at the time of attending an ongoing forest fire affect simulation results, both in terms of quality (accuracy) obtained and the time needed to make a decision. In the wildfire spread simulation and prediction area, it is essential to properly exploit the computational power offered by new computing advances. For this purpose, we rely on a two-stage prediction process to enhance the quality of traditional predictions, taking advantage of parallel computing. This strategy is based on an adjustment stage which is carried out by a well-known evolutionary technique: Genetic Algorithms. The core of this framework is evaluated according to the probability theory principles. Thus, a strong statistical study is presented and oriented towards the characterization of such an adjustment technique in order to help the operation managers deal with the two aspects previously mentioned: time and quality. The experimental work in this paper is based on a region in Spain which is one of the most prone to forest fires: El Cap de Creus.

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“…This map partitioning approach can easily be implemented in a Master/Worker MPI [7] [2] [1] application where the Master creates the map parts and distributes them to the workers, the workers calculate the wind field for each part and return the results to the Master that aggregates the wind fields in a complete wind field. Once the map partitioning scheme has been applied, the resulting wind field map has the same dimensions as the original one.…”

Section: Map Partitioningmentioning

confidence: 99%

Determining map partitioning to accelerate wind field calculation

Sanjuan

Brun

Margalef

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

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Wind speed and direction are parameters that affect forest fire propagation dramatically. So, an accurate estimation of such parameters is crucial to predict the fire propagation precisely. WindNInja is a wind field simulator that can easily be coupled to a forest fire propagation simulator such as FARSITE. However, wind field simulators present to main drawbacks: They take too much time to compute the wind field and they require a lot of memory. So, a map partitioning strategy has been developed to compute partial wind field maps that can be aggregated afterwards. Each map part can be computed in parallel and the amount of memory required is available in a single node. In this work a methodology to determine the most adequate map partitioning is presented. The map part shape, map part size, amount of overlapping and number of parts have been studied considering execution time and effects on wind field estimation. The results are based on a wide experimentation and are validated with real case scenarios.

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Relieving the Effects of Uncertainty in Forest Fire Spread Prediction by Hybrid MPI-OpenMP Parallel Strategies

Cited by 25 publications

References 13 publications

The effect of natural and anthropogenic disturbances on the uncertainty of large-area forest growth forecasts

The effect of natural and anthropogenic disturbances on the uncertainty of large-area forest growth forecasts

Applying Probability Theory for the Quality Assessment of a Wildfire Spread Prediction Framework Based on Genetic Algorithms

Determining map partitioning to accelerate wind field calculation

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