A Parallel Novelty Search Metaheuristic Applied to a Wildfire Prediction System

TL;DR

This paper introduces a parallel novelty search metaheuristic for wildfire prediction systems, aiming to improve scenario diversity and reduce local optima issues in simulation-based predictions.

Contribution

It proposes applying the Novelty Search paradigm to wildfire prediction, replacing traditional fitness functions to enhance scenario exploration and prediction accuracy.

Findings

Improved diversity of solutions in wildfire scenario prediction

Reduced likelihood of local optima in search process

Potential adaptability to other environmental propagation models

Abstract

Wildfires are a highly prevalent multi-causal environmental phenomenon. The impact of this phenomenon includes human losses, environmental damage and high economic costs. To mitigate these effects, several computer simulation systems have been developed in order to predict fire behavior based on a set of input parameters, also called a scenario (wind speed and direction; temperature; etc.). However, the results of a simulation usually have a high degree of error due to the uncertainty in the values of some variables, because they are not known, or because their measurement may be imprecise, erroneous, or impossible to perform in real time. Previous works have proposed the combination of multiple results in order to reduce this uncertainty. State-of-the-art methods are based on parallel optimization strategies that use a fitness function to guide the search among all possible scenarios. Although these methods have shown improvements in the quality of predictions, they have some limitations related to the algorithms used for the selection of scenarios. To overcome these limitations, in this work we propose to apply the Novelty Search paradigm, which replaces the objective function by a measure of the novelty of the solutions found, which allows the search to continuously generate solutions with behaviors that differ from one another. This approach avoids local optima and may be able to find useful solutions that would be difficult or impossible to find by other algorithms. As with existing methods, this proposal may also be adapted to other propagation models (floods, avalanches or landslides).