您现在的位置: 纽约时报中英文网 >> 纽约时报中英文版 >> 科技 >> 正文

科技防灾:猜测自然灾害的人工智能新技术_纽约时报中英文版_纽约时报中英文网

更新时间:2019/3/23 9:39:25 来源: 作者:佚名

How to bring wildfires back under control
科技防灾:猜测自然灾害的人工智能新技术_纽约时报中英文版_纽约时报中英文网

It’s the dead of the night in California, but the skies are alight。 A crackling, searing heat spreads through the forest, destroying everything that crosses its path。 With the lack of humidity, soaring winds, and an uninterrupted source of fuel, the fire spreads much faster than the authorities can handle。

这是加州的死亡之夜,天空被山林野火照亮。噼里啪啦的燃烧声伴着阵阵热浪穿过森林,所来之处寸草不留。由于空气干燥、风力强劲、燃烧物未被阻断,绵延不绝,火势不受控制,蔓延得极快。

This horror story unfolded last year when California saw its deadliest wildfires on record. A total of 8,527 fires burned in an area spanning 1,893,913 acres. In Paradise, California, 86 lives were lost when fire swept through their town in November last year.

这个恐惧故事发生在去年,加州经受了有史以来最致命的山林野火。共有8,527场大火,烧过了189万多英亩的土地。大火去年11月席卷加州天堂市(Paradise),造成86人丧生。

But this was by no means limited to one state, or even one country. In 2018, fires broke out on the Greek coast, in the Australian bush, in the UK – and even in the Arctic Circle. It’s a worldwide problem.

但这种情况并不只局限于一个州或是一个国家。2018年遭遇野火的还有希腊的海滩、澳洲的灌木丛、英国,甚至北极地区。这是一个全球性问题。

These fires are linked to symptoms of climate change: droughts, increasing temperatures, shifting wind patterns and low humidity。 With this in mind, we can expect more wildfires in the future。

这些火灾与气候变化的征兆相关:土壤干旱、气温升高、风场类型的变化、大气湿度低。有了这些信息,我们估量未来会发生更多山林野火。

But help might be coming from an unusual place.

但一个特别领域的进展,有助于解决这个问题。

Controlling the spread of forest fires requires a detailed understanding of the forests and how to manage them – something that is beyond all but a handful of specialised experts. This is where artificial intelligence steps in.

控制森林火灾,人们需对森林有详细了解、晓道如何治理它们,这原本只有个别专家可以做来,而如今人工智能大有助益。

SilviaTerra is a company based in San Francisco using artificial intelligence to map forests, providing resources to help planners reduce the risk of fires.

SilviaTerra是一家位于旧金山的公司。该公司运用人工智能进行森林测绘,为计划人员提供资源,帮助降低火灾风险。

“We combine on-the-ground measurements made in the forest with a large stack of remote sensing information and topographical data,” says Nan Pond, SilviaTerra’s lead biometrician. Her team uses satellite data along with aerial photographs and laser scanning techniques to measure the spread of vegetation. “[We] use our algorithmic process to produce high-resolution estimates of the sizes and species of trees present across large areas.”

SilviaTerra的首席生物统计学家庞德(Nan Pond)的团队将卫星数据和空中影像、激光扫描技术结合,用于测量植被的生长情况。她说:“我们将在森林中完成的地面测绘与大量的遥感信息和地势数据相结合。我们的算法程序可在很大范畴内对树木的大小和种类进行精确估量。”

Currently the team is mapping every forest in the US, 305。5 million hectares, to a resolution of 15 square metres。 Once the maps are complete, machine learning algorithms will help authorities to identify the areas most at risk of fire。

目前,该团队正在测绘美国的每一片森林,合计3.055亿英亩,最小辨别率为15平方米。一旦地图绘制完成,机器学习算法就会帮助当局找出火灾风险最高的区域。

The risk is evaluated by taking into account the concentration, species and the size of the trees in each area. Some trees, like pines, are much more flammable than others, such as maple. The difference comes down to many factors including how much oil they hold in their bark, the shape of the foliage and density of the leaves.

风险评估会考虑每个地区树木的密度、种类和大小。松树类树木就比其他树木(如枫木)更易燃。其中的差别由多种因素构成,包括树皮内油脂含量、叶子的形状以及叶子的密度。

Previously, forest inventories involved taking random samples of a forest and mapping them on the ground, then scaling this up to the whole forest. Now, with SilviaTerra’s work, forest inventories are becoming much more accurate.

此前,森林资源清查包括对森林随机取样,将它们反映在地面测绘中,然后按比例估算整个森林的资源。现在有了SilviaTerra的成果后,森林资源清查变得更加精确。

The firm has analysed the area around Paradise in California, for example, to create a map which shows areas of higher and lower wildfire risk. The most at-risk areas, identified in red on the map, are the places where authorities should be intervening in advance by putting in physical fire barriers or cutting trees down.

该公司分析了加州天堂市周围的地区,绘制出不同火灾风险等级的地图。风险最高的区域在地图中用红色标出。在那里,当局需要预先摘取干预措施,设置防火屏障或砍伐树木。

But it’s a complicated task. The algorithms have to juggle many conflicting priorities.

这是一项复杂的工作。这些算法必须要平稳许多看似矛盾的优先事项。

“Forest managers are working to mitigate the impacts of forest fire while balancing many other resources we get from forests,” says Pond. Forests are home to a wide variety of animal species; they’re also sources of clean water, used for recreational activities, help reduce erosion and suck up carbon from the atmosphere.

庞德表示:“森林治理人员正努力应对火灾带来的影响,同时也在平稳许多其他森林资源。”森林是许多动物的家园,也是清洁的水源,人们休闲的好去处;它还可以减少土壤流失,汲取大气中的二氧化碳。

Fire is not the only natural disaster that could be tackled with this sort of approach. Flooding is also influenced by an unlucky confluence of extremely heavy rainfall, land use, drainage and the capacity of existing water courses. While most flood prediction models attempt to capture all these influences to provide flood warnings, they often give a fairly crude picture. Michael Souffront at Utah’s Brigham Young University is working to improve the Global Flood Awareness System by using AI to introduce smaller rivers and tributaries, which are currently excluded, into the models.

此方法并非只可用于应对火灾。极强降雨、土地开发、排水,以及现有河湖水系容量等多种因素也共同决定水灾发生的概率。虽然大多数洪水猜测模型试图捕捉所有变量、提供洪水预警,但它们往往比较粗略。美国犹他州(Utah)杨百翰大学(Brigham Young University)的苏弗朗特(Michael Souffront)正在改进全球洪灾预警系统(Global Flood Awareness System),运用人工智能将尚未收录的小型河流、支流信息囊括来模型内。

Smaller streams and waterways are vital for preparing for floods, as they are often where overflowing begins。 Souffront also has created a web-based application that shows animated flood warnings on individual streams over time。 This provides a level of detail not previously available to local governments, in close to real time。 This far more detailed picture can then be put to use by those seeking to defend towns and cities from the risk of flooding。

较小的河流和水道通常是溢流开始之处,因此是防洪关键。苏弗朗特创建了一个在线应用程序,显示不同时间各河流的洪水预警情况,为当地政府提供了前所未有的详细信息,而且近乎实时。这些更为详细的信息可保护城镇免受洪灾。

In December 2015, the city of Leeds in the north of England was hit by some of the largest flood levels ever recorded. In response, the construction company Bam Nuttall was contracted to build flood defences. They employed technology start-up SenSat to create a virtual replica of the flood corridor, along a 12km length of the river Aire.

2015年12月,英格兰北部的利兹市(Leeds)遭受了有史以来最大的洪灾。为了应对洪水,政府与建筑公司Bam Nuttall签订合同,由后者建造防洪体。该公司聘请了科技初创公司SenSat用虚拟方式复制了12公里长艾尔河(Aire)的洪道。

“The virtual flood corridor had one objective, to allow computers to analyse the river, topography and flood risks across vastly more variables than humans alone could do,” says James Dean, chief executive of SenSat。

SenSat首席执行官迪恩(James Dean)表示:“虚拟洪道可让运算机运用大量的变量分析河流、地势、洪灾风险,远远超出人类能力所及。”

Drones were flown along the river valley 80 metres above the ground, gathering pictures and taking a measurement every 2。5cm。 In total it collected more than 600 million data points that were used to digitally reconstruct the river and its flood plain。 Then AI was used to interpret the data。

无人机在距离地面80米空中沿着河谷飞行,拍照照片,每2.5厘米就进行一次测量,总共收集了超过6亿数据点,用于对河流及洪泛平原进行数字复构。随后,人工智能算法被用于解读数据。

“In order to do this, we built something called an elastic spatial indexing algorithm that allows an AI to identify objects as individual items within a 3D reconstructed environment,” says Dean.  “This produces better decision making and more efficient use of public funds to protect vulnerable areas and alleviate the misery of flooding.”

迪恩说:“为了做来这件事,我们建了一个所谓的弹性空间指数算法,让人工智能在三维复构的环境中将物体识别为单独的项目。这可以让决策更好,更高效地利用公共资金保护脆弱的地区、减轻洪水造成的伤害。”

The resulting map helped inform what construction works Bam Nuttall needed to undertake to reduce flooding in the area. The building work is due to be completed by summer this year.

这样制出的地图帮助Bam Nuttall公司了解需要做什么样的建筑工程来减少地区的洪涝灾害。建筑工作将在今年夏天完工。

But there are some natural events that cannot be avoided. Earthquakes, volcanic eruptions and tsunamis are notoriously unpredictable and impossible to control. While these disasters often claim many lives immediately, what happens in the aftermath and how rescue teams react has a large impact on how many people survive.

但有些自然灾害是无法避免的。众所周晓,地震、火山爆发、海啸都无法猜测或控制。这些灾难常常即刻就夺走许多生命,灾后余波和救援队的应对措施对受灾者的存活率有着复大的影响。

A team at Tohoku University in Sendai, Japan is attempting to increase the chances of finding people and pulling them alive from the chaos that follows tsunamis, volcanoes and earthquakes。

日本仙台(Sendai)东北大学(Tohoku University)的一个团队正在研究如何在海啸、火山、地震后更有效地觅找并救出幸存者。

In March 2011, Japan was hit by one of the most powerful earthquakes ever recorded in the region. It was the fourth most powerful earthquake on record, moving the entire island of Honshu by 2.4 metres and shifting the whole planet’s axis by around 17cm. The earthquake, which had its epicentre 130km off shore, triggered a tsunami that devastated a large part of the country’s east coast and triggered the now infamous emergency at the Fukushima Daiichi nuclear power plant. The Tohoku region was one of the most badly damaged. In total, the tsunami claimed nearly 20,000 lives. To this day 2,000 people are missing.

2011年3月,日本发生强烈地震。这是史上第四大的地震,整个本州岛(Honshu)移动了2.4米,地球轴心移动了约17厘米。此次地震震中距离日本海岸130公里,引发的海啸摧残了日本东部沿海的大部分地区,并引发令人担忧的福岛核电站泄露事故。东北地区(Tohoku)是受灾最严复的地区之一。这次海啸中,总计近2万人丧生,至今仍有2000人下落不明。

Seven years earlier, a tsunami hit 14 Asian and African countries after an earthquake in the Indian Ocean. An estimated 230,000 people died, and the damage was thought to be made worse by a lack of communication. The tsunami hit Indonesia first, then Thailand, Myanmar and Sri Lanka. Hours later, the tsunami hit the east African coast. Those in its path had little to no notice of what was coming because of a lack of early warning systems at the time. Rescue workers also struggled to find the places that needed their help most as communication networks had been decimated and whole regions were cut off.

7年前,印度洋一场地震引发的海啸席卷了14个亚非国家。据估量,共有23万人丧生,而且由于缺乏沟通,实际受灾情况可能更加严复。这场海啸先突击了印度尼西亚,随后是泰国、缅甸和斯里兰卡,几个小时后,来达非洲东海岸。由于当时缺乏预警系统,那些海啸影响地区的人们几乎不晓道将要发生的灾难。搜救人员也很难确定最需要救援的区域,因为通信网络遭来破坏,整个地区失联。

Bai Yanbing at Tohoku University hopes his work can avoid these problems in the future. He is developing a tool that uses AI to define areas affected by natural disasters, classify the damage on the ground and alert governments and rescue teams to where they are most needed.

日本东北大学的白琰冰(Yanbing Bai)表示,他的研究有望在未来解决这些问题。他在开发一款使用人工智能确定受灾区域、将地面受灾程度分类、向政府和救援人员发出警报告晓最需要救援的地区的工具。

They do this by running satellite imagery captured in the immediate aftermath of a natural disaster through a machine learning algorithm that’s been trained to classify buildings into different categories of destruction and stability。 It can identify buildings that have been totally destroyed, half damaged but reparable, slightly damaged or undamaged。

他们用机器学习算法处理灾区的卫星图像。这些机器学习算法经过训练、可以将建筑按照损毁程度和稳固性进行分类。该算法可以辨别不同类型的建筑:完全损毁的,损毁一半但可以修复的,以及微微受损或未受损的。

“This information can then be sent to first responders and give them the real-time information they need to save lives, and be safe themselves, when entering a post-disaster area” says Lucas Joppa, chief environmental officer at Microsoft, which has provided funding for Yanbing’s project under its AI for Earth programme。 The system can tell rescuers where the worst affected areas are and where they should focus their efforts to find survivors。

微软地球人工智能项目(AI for Earth)为白琰冰的研究提供资金,其首席环境官乔帕(Lucas Joppa)表示:“这些信息发送给第一批救援者,让他们进入灾区后获得挽救生命、保证自身安全的实时信息。”这一系统可以告诉搜救人员受灾最严复的地区是哪里,应该专注搜救哪一区域的幸存人员。

Machine learning technology could not just help to coordinate the response to disasters, but also help in the rescue efforts themselves.

机器学习技术不但有助和谐救灾工作,而且能参与来搜救工作中去。

In these cases, robots may be the answer. Katia Sycara, director of the advanced-agent robotics laboratory at Carnegie Mellon University in Pittsburgh, is developing swarms of robots that will be able to go into disaster zones to autonomously search for survivors. The robots would use AI-powered machine vision to help them interpret what they are seeing and make their own decisions.

在这些情况下,搜救方案可能由机器人执行。美国匹兹堡卡耐基梅隆大学(Carnegie Mellon University)高级代理机器人实验室主管塞卡拉(Katia Sycara)正在开发大批可以进入灾区、自主搜觅幸存者的机器人。这些机器人将使用人工智能的机器视觉分析所见场景、作出决策。

“This enables robots to go to areas that may be inaccessible and dangerous for humans and search for victims” says Sycara. The meltdown at the Fukushima nuclear power plant is one example where rapidly rising radiation levels made it unsafe for human disaster workers to go into the area.

“可以让机器人前往人们无法来达、或者对人类而言危险的区域,觅找受灾人员。”塞卡拉说道。福岛核电站的融毁就是一个例子。快速上升的辐射水平使得这一区域变得危险,不适合救灾人员进入。

The biggest challenge with these kinds of robots, says Sycara, is enabling them to move around in environments where the terrain is unknown and unexpected obstacles littering their path.

塞卡拉表示,要应对无法预晓掉落的路障,而且要在未晓的环境里自如移动,这是机器人面对的最大挑战。

There are some researchers, however, who believe that artificial intelligence could also help provide an early warning of natural disasters themselves.

有一些研究人员相信,人工智能本身也可以提供自然灾害的早期预警。

It is notoriously difficult to predict disasters like earthquakes and volcanoes。 Researchers are edging closer to being able to predict aftershocks, however。 Aftershocks can be just as devastating as the initial earthquake。 In Christchurch, New Zealand, a magnitude 7。1 earthquake in September 2010 caused widespread damage but did not cause any deaths, yet a smaller aftershock that occurred five months later killed 185 people。

众所周晓,要预报地震、火山爆发之类的灾害极其困难,但研究人员正在竭尽所能地猜测余震。余震的后果一样严复。2010年9月,新西兰基督城发生了7.1级大地震,造成大范畴的损毁,但没有造成任何伤亡。然而5个月后的小型余震造成185人丧生。

In a study last year, a team from Harvard University fed data from hundreds of thousands of earthquakes, including Japan’s 2011 disaster, into a neural network. The AI predicted the likelihood an aftershock would hit the surrounding area, which was divided up into 5km by 5km squares. By taking each square as its own problem, the AI predicted aftershocks more successfully than previous methods.

去年哈佛大学团队开展一项研究,将包括日本2011年大地震在内的数十万地震数据输入一个神经网络。地震灾区按照5乘5公里的区块划分,每一区块作为一个独立的区域,猜测余震的可能性,这比此前的任何方法都要成功。

But when it comes to prediction of and preparation for natural disasters, one of the biggest barriers is having enough accurate data。 These events are still extremely rare, so the quantities of data needed to train machine learning algorithms is thin on the ground。 Satellite and aerial surveys are often not a priority when governments are responding to a crisis。

要猜测自然灾害并做好应对准备,最大的困难是缺乏足够的准确数据。案例不足,因此训练机器学习算法所需的实际数据仍旧非常匮乏。这是因为,政府应对危机,通常并不把卫星和航空勘察当作首要事项。

Natural disasters also involve large numbers of variables. A wide range of events can turn a small tornado into one with a magnitude of EF5 – where winds can exceed 200mph and are powerful enough to lift cars off the ground – or lead a volcanic eruption to trigger a tsunami, for example.

自然灾害还有大量的变量。举个例子,许多因素可将小型龙卷风的强度增至EF5级(最高级别)——风速可达每小时200英里,足以将车辆从地面卷起,也可将简单的火山喷发变为海啸。

“The good news, though, and the reason we’re focused on this work, is that even incremental progress can [have a] exponential impact,” says Joppa. “A few more hours to evacuate an area, to adjust where resources are deployed to prevent flooding – those hours and minutes are often the difference between life and death.”

乔帕表示:“但好消息是,渐进式的成果也能产生级数增长的影响力,这是为什么我们专注这项研究。多几个小时疏散一片区域、调配资源预防洪灾,每分每秒都意味着生存机率的增加。”

“全文请访问,本文发表于纽约时报中文网(http://cn.nytimes.com),版权回纽约时报公司所有。任何单位及个人未经许可,不得擅自转载或翻译。订阅纽约时报中文网新闻电邮:http://nytcn.me/subscription/”

pk10投注技巧相关的文章列表
pk10投注技巧论坛 pk10开奖记录 pk10下注技巧 pk10走势图 pk10登录地址

pk10投注技巧免责声明: 本站资料及图片来源互联网文章-|,本网不承担任何由内容信息所引起的争议和法律责任。所有作品版权归原创作者所有,与本站立场无关-|,如用户分享不慎侵犯了您的权益,请联系我们告知,-|我们将做删除处理!