How AI Is Being Used to Respond to Natural Disasters in Cities
人工智能在城市应对自然灾害中的应用
The number of people living in urban areas has tripled in the last 50 years, meaning when a major natural disaster such as an earthquake strikes a city, more lives are in danger. Meanwhile, the strength and frequency of extreme weather events has increased—a trend set to continue as the climate warms. That is spurring efforts around the world to develop a new generation of earthquake monitoring and climate forecasting systems to make detecting and responding to disasters quicker, cheaper, and more accurate than ever.
在过去的 50 年里，生活在城市地区的人口数量已经翻了三倍，这意味着当像地震这样的重大自然灾害袭击城市时，会有更多生命处于危险之中。同时，极端天气事件的强度和频率也在增加——这一趋势随着气候变暖将继续下去。这正在促使全球各地努力开发新一代的地震监测和气候预报系统，以使检测和应对灾害的速度更快、成本更低、准确性更高。
On Nov. 6, at the Barcelona Supercomputing Center in Spain, the Global Initiative on Resilience to Natural Hazards through AI Solutions will meet for the first time. The new United Nations initiative aims to guide governments, organizations, and communities in using AI for disaster management.
11 月 6 日，在西班牙巴塞罗那超级计算中心，全球灾害韧性倡议通过人工智能解决方案首次会议将举行。这一新的联合国倡议旨在指导政府、组织和社区利用人工智能进行灾害管理。
The initiative builds on nearly four years of groundwork laid by the International Telecommunications Union, the World Meteorological Organization (WMO) and the U.N. Environment Programme, which in early 2021 collectively convened a focus group to begin developing best practices for AI use in disaster management. These include enhancing data collection, improving forecasting, and streamlining communications.
该倡议基于国际电信联盟、世界气象组织（WMO）和联合国环境规划署在 2017 年至 2020 年间所做的近四年的工作基础。2021 年初，这三个组织共同召集了一个焦点小组，开始制定在灾害管理中使用人工智能的最佳实践。这些实践包括增强数据收集、提高预测能力和优化通讯流程。
“What I find exciting is, for one type of hazard, there are so many different ways that AI can be applied and this creates a lot of opportunities,” says Monique Kuglitsch, who chaired the focus group. Take hurricanes for example: In 2023, researchers showed AI could help policymakers identify the best places to put traffic sensors to detect road blockages after tropical storms in Tallahassee, Fla. And in October, meteorologists used AI weather forecasting models to accurately predict that Hurricane Milton would land near Siesta Key, Florida. AI is also being used to alert members of the public more efficiently. Last year, The National Weather Service announced a partnership with AI translation company Lilt to help deliver forecasts in Spanish and simplified Chinese, which it says can reduce the time to translate a hurricane warning from an hour to 10 minutes.
“我感到兴奋的是，对于一种类型的灾害，人工智能可以以如此多不同的方式应用，这创造了许多机会，”蒙蒂克小组主席莫妮卡·库格利奇说。以飓风为例：在 2023 年，研究人员展示了人工智能如何帮助政策制定者识别佛罗里达州塔拉哈西热带风暴后最佳的交通传感器位置，以检测道路堵塞。而在 10 月，气象学家使用人工智能天气预报模型准确预测飓风米尔顿将登陆佛罗里达州的西塔岛。人工智能也被用于更有效地向公众发出警报。去年，国家气象局宣布与人工智能翻译公司 Lilt 合作，以提供西班牙语和简化中文的预报，它说这可以将翻译飓风警告的时间从一小时缩短到 10 分钟。
Besides helping communities prepare for disasters, AI is also being used to coordinate response efforts. Following both Hurricane Milton and Hurricane Ian, non-profit GiveDirectly used Google’s machine learning models to analyze pre- and post-satellite images to identify the worst affected areas, and prioritize cash grants accordingly. Last year AI analysis of aerial images was deployed in cities like Quelimane, Mozambique, after Cyclone Freddy and Adıyaman, Turkey, after a 7.8 magnitude earthquake, to aid response efforts.
除了帮助社区为灾难做准备，人工智能还在协调响应工作。在飓风米尔顿和飓风伊恩之后，非营利组织 GiveDirectly 使用了谷歌的机器学习模型，分析了预先和事后的卫星图像，以识别受影响最严重的地区，并相应地优先考虑现金补助。去年，在莫桑比克的奎利马纳和土耳其的阿迪亚曼，分别在飓风弗雷迪和 7.8 级地震之后，部署了对空中图像的人工智能分析，以协助响应工作。
Operating early warning systems is primarily a governmental responsibility, but AI climate modeling—and, to a lesser extent, earthquake detection—has become a burgeoning private industry. Start-up SeismicAI says it’s working with the civil protection agencies in the Mexican states of Guerrero and Jalisco to deploy an AI-enhanced network of sensors, which would detect earthquakes in real-time. Tech giants Google, Nvidia, and Huawei are partnering with European forecasters and say their AI-driven models can generate accurate medium-term forecasts thousands of times more quickly than traditional models, while being less computationally intensive. And in September, IBM partnered with NASA to release a general-purpose open-source model that can be used for various climate-modeling cases, and which runs on a desktop.
运行早期预警系统主要是一项政府职责，但人工智能气候建模——以及在较小程度上地震探测——已经发展成为蓬勃发展的私营行业。初创公司 SeismicAI 表示，它正在与墨西哥格雷罗州和贾拉尔州的民防机构合作部署一个增强有人工智能的传感器网络，该网络可以实时检测地震。科技巨头谷歌、英伟达和华为与欧洲预报员合作，并表示他们的人工智能驱动的模型可以生成准确的中长期预测，速度比传统模型快数千倍，同时计算量更小。而在 9 月，IBM 与 NASA 合作发布了可用于各种气候建模案例的通用开源模型，并可以在桌面设备上运行。
AI advances 人工智能的进步
While machine learning techniques have been incorporated into weather forecasting models for many years, recent advances have allowed many new models to be built using AI from the ground-up, improving the accuracy and speed of forecasting. Traditional models, which rely on complex physics-based equations to simulate interactions between water and air in the atmosphere and require supercomputers to run, can take hours to generate a single forecast. In contrast, AI weather models learn to spot patterns by training on decades of climate data, most of which was collected via satellites and ground-based sensors and shared through intergovernmental collaboration.
多年来，机器学习技术已被整合到天气预报模型中，最近的进展使得能够从头开始使用人工智能构建许多新的模型，从而提高了预报的准确性和速度。传统的模型依赖于复杂的基于物理的方程式来模拟大气中水和空气之间的相互作用，并需要超级计算机运行，生成单个预报可能需要数小时。相比之下，人工智能天气模型通过训练数十载的气候数据来学习识别模式，其中大部分数据通过卫星和地面传感器收集，并通过政府间合作共享。
Both AI and physics-based forecasts work by dividing the world into a three-dimensional grid of boxes and then determining variables like temperature and wind speed. But because AI models are more computationally efficient, they can create much finer-grained grids. For example, the the European Centre for Medium-Range Weather Forecasts’ highest resolution model breaks the world into 5.5 mile boxes, whereas forecasting startup Atmo offers models finer than one square mile. This bump in resolution can allow for more efficient allocation of resources during extreme weather events, which is particularly important for cities, says Johan Mathe, co-founder and CTO of the company, which earlier this year inked deals with the Philippines and the island nation of Tuvalu.
人工智能和基于物理的预测方法都是通过将世界划分为三维网格的盒子来工作的，然后确定变量，如温度和风速。但因为人工智能模型在计算效率上更高，所以它们可以创建更精细的网格。例如，欧洲中期天气预报中心的最高分辨率模型将世界划分为 5.5 英里的盒子，而预测初创公司 Atmo 提供的模型则比一平方英里还要精细。这种分辨率的提升可以允许在极端天气事件期间更有效地分配资源，这对城市尤为重要，公司联合创始人兼 CTO 约翰·马瑟说，该公司今年早些时候与菲律宾和岛国图瓦卢签订了协议。
Limitations 限制
AI-driven models are typically only as good as the data they are trained on, which can be a limiting factor in some places. “When you’re in a really high stakes situation, like a disaster, you need to be able to rely on the model output,” says Kuglitsch. Poorer regions—often on the frontlines of climate-related disasters—typically have fewer and worse-maintained weather sensors, for example, creating gaps in meteorological data. AI systems trained on this skewed data can be less accurate in the places most vulnerable to disasters. And unlike physics-based models, which follow set rules, as AI models become more complex, they increasingly operate as sophisticated ‘black boxes,’ where the path from input to output becomes less transparent. The U.N. initiative’s focus is on developing guidelines for using AI responsibly. Kuglitsch says standards could, for example, encourage developers to disclose a model’s limitations or ensure systems work across regional boundaries.
AI 驱动的模型通常只与它们所接受的训练数据一样好，这在某些地方可能是一个限制因素。库格利奇说：“当你处于像灾难这样的高风险情况时，你需要能够依赖模型的输出。”较贫穷的地区——往往是气候相关灾害的前线——通常拥有较少且维护状况较差的气象传感器，例如，这会导致气象数据出现缺口。使用这种偏斜数据训练的 AI 系统在最容易遭受灾害的地方准确性可能较低。与遵循固定规则的物理模型不同，随着 AI 模型变得更加复杂，它们越来越多地作为复杂的‘黑盒’运行，从输入到输出的路径变得不那么透明。联合国倡议的重点是制定负责任使用 AI 的指导原则。库格利奇表示，标准可以鼓励开发人员披露模型的局限性，或者确保系统在区域边界上工作。
The initiative will test its recommendations in the field by collaborating with the Mediterranean and pan-European forecast and Early Warning System Against natural hazards (MedEWSa), a project that spun out of the focus group. “We’re going to be applying the best practices from the focus group and getting a feedback loop going, to figure out which of the best practices are easiest to follow,” Kuglitsch says. One MedEWSa pilot project will explore machine learning to predict the occurrence of wildfires an area around Athens, Greece. Another will use AI to improve flooding and landslide warnings in the area surrounding Tbilisi city, Georgia.
这项倡议将通过与地中海和泛欧洲自然灾害预报与早期预警系统（MedEWSa）合作，在实践中测试其建议，MedEWSa 是从焦点小组中衍生出来的项目。“我们将应用焦点小组的最佳实践，并建立一个反馈循环，以确定哪些最佳实践最容易遵循，”Kuglitsch 说。MedEWSa 的一个试点项目将探索使用机器学习来预测希腊雅典周围地区的野火发生。另一个项目将使用人工智能来改善格鲁吉亚第比利斯市周围地区的洪水和山体滑坡预警。
Meanwhile, private companies like Tomorrow.io are seeking to plug these gaps by collecting their own data. The AI weather forecasting start-up has launched satellites with radar and other meteorological sensors to collect data from regions that lack ground-based sensors, which it combines with historical data to train its models. Tomorrow.io’s technology is being used by New England cities including Boston, to help city officials decide when to salt the roads ahead of snowfall. It’s also used by Uber and Delta Airlines.
与此同时，私营公司如 Tomorrow.io 正在通过收集自己的数据来填补这些空白。这家人工智能天气预报初创公司推出了搭载雷达和其他气象传感器的卫星，从缺乏地面传感器的地区收集数据，然后将其与历史数据结合起来，用于训练其模型。Tomorrow.io 的技术被新英格兰地区的城市，包括波士顿，用于帮助城市官员决定在降雪前何时撒盐。它也被优步和达美航空使用。
Another U.N. initiative, the Systematic Observations Financing Facility (SOFF), also aims to close the weather data gap by providing financing and technical assistance in poorer countries. Johan Stander, director of services for the WMO, one of SOFF’s partners, says the WMO is working with private AI developers including Google and Microsoft, but stresses the importance of not handing off too much responsibility to AI systems.
另一个联合国倡议，系统观测融资设施（SOFF），也旨在通过为较贫穷国家提供融资和技术援助来缩小气象数据缺口。WMO 的服务主任，SOFF 的合作伙伴之一，Johan Stander 表示，WMO 正在与包括谷歌和微软在内的私营 AI 开发者合作。但他强调，不要将过多的责任交给 AI 系统的重要性。