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).
翻译:野火是一种极为普遍的多因果环境现象。 这种现象的影响包括人类损失、环境破坏和经济成本高昂。 为了减轻这些影响,已经开发了数个计算机模拟系统,以便根据一系列输入参数预测火灾行为,这些输入参数也称为情景(风速和方向;温度等)。 然而,由于某些变量的价值不确定,或由于这些变量的测量可能不准确、错误或无法实时进行,模拟的结果通常有高度的误差。 先前的工作提出了多种结果的组合,以减少这种不确定性。 最新设计的方法以平行优化战略为基础,使用一种健身功能来指导所有可能的情景的搜索。 虽然这些方法显示了预测质量的改进,但它们与选择情景所使用的算法有一定的局限性。 为了克服这些局限性,我们提议在这项工作中应用Novvelty搜索范式,用所发现的新颖的解决办法来取代目标功能,从而使得能够进行搜索,从而能够产生出一个连续的优化战略,使用一种健康功能来指导所有可能的情景的搜索。 这种方法在预测质量上也有一定的局限性。 为了从另一种方法中找到一种不同的选择, 。