城市的 海堤能否抵御特大风暴? 地区的 电网能否抵御破纪录的高温? 城镇的 消防资源能否遏制重大山火?
Can a city的 seawall stand up to a blockbuster storm? Will a region的 power grid hold against record-breaking heat? And can a town的 fire-fighting resources contain a major wildfire?
要回答这些问题, 社区首先需要知道此类极端事件如何发生。野火可能蔓延多远? 风暴可能影响多少地区? 热浪会持续多长时间?
To answer these questions, communities will first need to know how such extreme events could unfold. How far is a wildfire likely to spread? How much of a region might a storm impact? How long could a heat wave last?
但众所周知，极端事件很难预测。就其本质而言, 他们是异常值。在历史记录保存, 极端事件是零星的和罕见的。然而，大多数评估区域的风险的方法都依赖于过去的极端事件来描述未来更极端的,最坏情况。
But extreme events are notoriously difficult to anticipate. By their nature, they are outliers. In the history of record keeping, extreme events are sporadic and rare. Yet most methods that assess a region的 risk depend on extreme events of the past to characterize even more extreme, worst-case scenarios in the future.
现在, 麻省理工学院的工程师开发了一种工具，可以生成可能的极端事件和最坏情况,，并绘制其特征,，例如极端风暴的 可能的持续时间, 强度, 和影响区域。他们的方法的关键在于，它不需要了解以前的极端事件就可以生成可能的未来极端事件。
Now, MIT engineers have developed a tool that generates plausible extreme events and worst-case scenarios, and maps their characteristics, such as an extreme storm的 likely duration, intensity, and area of impact. The key to their method is that it does not need to know about previous extreme events in order to generate plausible future extreme events.
相反,，方法,以机器学习算法,的形式从数据集,学习，例如区域’的每日天气记录和地图。该记录可能包含也可能不包含过去的极端偏差,，例如创纪录的高温或降雨。团队的 算法采用统计方法从可用数据, 中学习，以排除不可信的天气情况。然后，该方法生成可能在给定频率 (（例如每 100 年一次),）的区域发生的极端事件，并预测这些极端事件的规模, 强度, 和持续时间。
Instead, the method, in the form of a machine-learning algorithm, learns from a dataset, such as a region的 daily weather records and maps. This record may or may not contain past extreme deviations, such as record-setting heat or rain. The team的 algorithm takes a statistical approach to learn from the available data, to exclude implausible weather scenarios. The method then generates plausible extreme events that are likely to occur in a region with a given frequency (such as once every 100 years), and projects how those extreme events might look in terms of their size, intensity, and duration.
“我们正在尝试对从未见过的极端,史无前例的事件进行建模,不在数据集中,”，麻省理工学院机械工程研究生、麻省理工学院计算科学与工程中心附属机构的 Kai Chang, 说。
“We are trying to model extreme, unprecedented events that no one has seen before, that are not in the dataset,” says Kai Chang, an MIT graduate student in mechanical engineering and affiliate of the MIT Center for Computational Science and Engineering.
“像卡特里娜飓风这样的事件每 30 到 40 年就会发生一次,” 添加了 Themis Sapsis,，他是麻省理工学院, 机械和海洋工程的 William I. Koch 教授，计算科学与工程中心, 的核心成员，也是麻省理工学院数据, 系统, 和社会研究所的附属机构。 “每 100 年发生一次的卡特里娜飓风会怎样? 会有多糟糕? 那’正是我们’正在尝试量化,以帮助规划者为可能的极端情况做好准备。”
“An event like Hurricane Katrina is something that happens every 30 to 40 years,” adds Themis Sapsis, the William I. Koch Professor of Mechanical and Ocean Engineering at MIT, a core member of the Center for Computational Science and Engineering, and an affiliate of the MIT Institute for Data, Systems, and Society. “What will be the Katrina that happens every 100 years? How bad will it be? That的 exactly what we’re trying to quantify, to help planners prepare for plausible extreme scenarios.”
除了天气事件, 之外，该团队将其称为“极端事件感知,”或“η-学习,” 的方法, 还可以应用于其他领域,，例如机器人导航和金融市场。
Beyond weather events, the approach, which the team has dubbed Extreme Event Aware, or “η-learning,” can be applied to other fields, such as robotic navigation and financial markets.
“金融市场崩盘是极端事件，是多种事件的复杂组合,涉及许多不同部门,” Chang说。 “导致市场崩溃的相互作用是什么?这是该方法可以探索的内容。”
“Financial market crashes are extreme events that are a complicated combination of things, involving many different sectors,” Chang says. “What is the interaction that leads to a market crash? That is something that this method could explore.”