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提前48小时预警,谷歌利用AI技术预测东南亚洪水

提前48小时预警,谷歌利用AI技术预测东南亚洪水

Jeremy Kahn 2020年09月04日
谷歌通过修改该系统采用的技术,可以在洪水爆发之前最多48小时提供详细预警。

谷歌(Google)在9月1日发布的一篇博文中表示,公司完善并扩大了利用人工智能软件为南亚地区预测洪水的项目,该项目能够帮助南亚国家政府更早、更准确地提供预警,从而拯救更多生命。

目前,该项目覆盖了印度面临洪水风险的超过2亿人口及其邻国孟加拉国的大量人口。孟加拉国每年平均有5,000人在洪水中丧生。

谷歌通过修改该系统采用的技术,将目前的预警时间提前一倍,可以在洪水爆发之前最多48小时提供详细预警。

洪水每年在全世界范围内会影响大约9,500万至2.4亿人,造成6,000人至8,000人死亡,经济损失高达330亿美元。气候变化产生更猛烈的暴风雨,导致冰川融化,这将使洪水频发,并且变得更加严重,预计这些数据也会随之继续上升。

谷歌在2017年启动“洪水预报倡议”(Flood Forecasting Initiative),涵盖了印度比哈尔邦首府巴特那的周边地区,这里一直是印度最容易洪水泛滥的地区。2019年,比哈尔邦遭遇了25年来最严重的洪水,造成130多人死亡。

作为来自硅谷的科技行业巨头,谷歌一直在与印度政府下属的中央水务委员会(Central Water Commission)合作改善洪水预报。另外,谷歌还与该委员会合作,改进了向民众发送预警信息的方式。

谷歌一直在逐步扩大该项目的范围,该公司表示,到目前为止,已经在印度帮助发送了超过2,700万条洪水预警。

谷歌的高级软件工程师塞拉•尼瓦是洪水预报项目的负责人。他表示,改进印度的洪水预报,需要与印度政府合作完善水位数据的收集方式。这样便可以减少过去妨碍洪水预报的水位信息读取错误和延误等问题。

虽然某些问题是印度及其他发展中国家在洪水监测方面存在的特殊问题,但谷歌在印度率先尝试的一些技术可能会改变全世界的洪水预报。

尼瓦表示,以前最先进的洪水预报所采用的水文模型,基本上都是基于当地的地形图和从物理学中得出的概念原则。他解释说,每一个流域都有各自的特点,因此几乎不可能创建一个在不同江河流域都适用的模型。

但谷歌采用的方法主要基于人工智能,由软件对世界不同地区的多个不同江河流域的历史洪水数据进行分析,然后通过自我学习对几乎所有江河流域做出准确预测。

尼瓦说:“在水文学中有一种观点认为你不可能对不同江河流域一概而论,这种观点被人们奉为圭臬。但事实证明,这种观点并不正确。”他说谷歌基于人工智能的预报模型,在接受训练之前从未遇到过的流域预测洪水的效果,胜过了专为该流域设计的传统水文模型。

当然,得出预测结果是一回事,要解决如何根据预测结果向人们提供预警又是另一回事。人们对于政府发布的洪水预警会作何反应?哪种预警的效果最佳?到目前为止,对于这些问题的研究一直有所欠缺。

谷歌表示,其目前正在与耶鲁大学(Yale University)的研究人员合作,尝试找到这些问题的答案。耶鲁大学和谷歌在印度的初步研究显示,民众收到预警后采取自我保护措施的几率增加了一倍,收到洪水预警的民众有65%采取了一定的保护措施。

但谷歌仍然在努力提高这些数据。谷歌表示,公司今年全面改进了其洪水预警,将以9种本地语言和可视化的形式提供信息,帮助人们更直观地理解预警内容。

另外,谷歌将根据其预测向人们提供更多信息,比如洪水在某个特定时间抵达某个村庄或区域时可能达到的水位高度。(财富中文网)

本文经过更新,以准确描述谷歌为改善洪水预测所用的数据质量采用的方法。谷歌在其洪水预报倡议中没有使用电子传感器,而是依靠其他方法提高数据的及时性和准确性。

译者:Biz

谷歌(Google)在9月1日发布的一篇博文中表示,公司完善并扩大了利用人工智能软件为南亚地区预测洪水的项目,该项目能够帮助南亚国家政府更早、更准确地提供预警,从而拯救更多生命。

目前,该项目覆盖了印度面临洪水风险的超过2亿人口及其邻国孟加拉国的大量人口。孟加拉国每年平均有5,000人在洪水中丧生。

谷歌通过修改该系统采用的技术,将目前的预警时间提前一倍,可以在洪水爆发之前最多48小时提供详细预警。

洪水每年在全世界范围内会影响大约9,500万至2.4亿人,造成6,000人至8,000人死亡,经济损失高达330亿美元。气候变化产生更猛烈的暴风雨,导致冰川融化,这将使洪水频发,并且变得更加严重,预计这些数据也会随之继续上升。

谷歌在2017年启动“洪水预报倡议”(Flood Forecasting Initiative),涵盖了印度比哈尔邦首府巴特那的周边地区,这里一直是印度最容易洪水泛滥的地区。2019年,比哈尔邦遭遇了25年来最严重的洪水,造成130多人死亡。

作为来自硅谷的科技行业巨头,谷歌一直在与印度政府下属的中央水务委员会(Central Water Commission)合作改善洪水预报。另外,谷歌还与该委员会合作,改进了向民众发送预警信息的方式。

谷歌一直在逐步扩大该项目的范围,该公司表示,到目前为止,已经在印度帮助发送了超过2,700万条洪水预警。

谷歌的高级软件工程师塞拉•尼瓦是洪水预报项目的负责人。他表示,改进印度的洪水预报,需要与印度政府合作完善水位数据的收集方式。这样便可以减少过去妨碍洪水预报的水位信息读取错误和延误等问题。

虽然某些问题是印度及其他发展中国家在洪水监测方面存在的特殊问题,但谷歌在印度率先尝试的一些技术可能会改变全世界的洪水预报。

尼瓦表示,以前最先进的洪水预报所采用的水文模型,基本上都是基于当地的地形图和从物理学中得出的概念原则。他解释说,每一个流域都有各自的特点,因此几乎不可能创建一个在不同江河流域都适用的模型。

但谷歌采用的方法主要基于人工智能,由软件对世界不同地区的多个不同江河流域的历史洪水数据进行分析,然后通过自我学习对几乎所有江河流域做出准确预测。

尼瓦说:“在水文学中有一种观点认为你不可能对不同江河流域一概而论,这种观点被人们奉为圭臬。但事实证明,这种观点并不正确。”他说谷歌基于人工智能的预报模型,在接受训练之前从未遇到过的流域预测洪水的效果,胜过了专为该流域设计的传统水文模型。

当然,得出预测结果是一回事,要解决如何根据预测结果向人们提供预警又是另一回事。人们对于政府发布的洪水预警会作何反应?哪种预警的效果最佳?到目前为止,对于这些问题的研究一直有所欠缺。

谷歌表示,其目前正在与耶鲁大学(Yale University)的研究人员合作,尝试找到这些问题的答案。耶鲁大学和谷歌在印度的初步研究显示,民众收到预警后采取自我保护措施的几率增加了一倍,收到洪水预警的民众有65%采取了一定的保护措施。

但谷歌仍然在努力提高这些数据。谷歌表示,公司今年全面改进了其洪水预警,将以9种本地语言和可视化的形式提供信息,帮助人们更直观地理解预警内容。

另外,谷歌将根据其预测向人们提供更多信息,比如洪水在某个特定时间抵达某个村庄或区域时可能达到的水位高度。(财富中文网)

本文经过更新,以准确描述谷歌为改善洪水预测所用的数据质量采用的方法。谷歌在其洪水预报倡议中没有使用电子传感器,而是依靠其他方法提高数据的及时性和准确性。

译者:Biz

Google has improved and expanded a program that uses artificial intelligence software to forecast floods in South Asia, enabling governments to issue earlier and more accurate warnings that can potentially save lives, the company said in a blog post on September 1.

The system now covers more than 200 million people at risk for flooding across India as well as large portions of neighboring Bangladesh, a country where an average of 5,000 people each year are killed in floods.

Changes in the technology underpinning the system have allowed Google to double the warning time it is now providing, giving people detailed alerts up to 48 hours before flooding occurs.

Floods affect an estimated 95 million to 240 million people worldwide annually, killing between 6,000 and 8,000 of them and causing up to $33 billion in economic damage. Those figures are expected to rise as climate change makes flooding, owing to stronger rainstorms and glacial melting, more frequent and severe.

Google began its Flood Forecasting Initiative in 2017, covering the area around Patna, the capital of the Indian state of Bihar, historically the country’s most flood-prone region. In 2019, Bihar experienced some of the worst floods in a quarter-century, which killed more than 130 people.

The Silicon Valley technology giant has worked with the Indian government’s Central Water Commission to improve the forecasts it relies on. It has also worked with the agency to improve the way it sends alerts to citizens warning them of danger.

Since then Google has steadily expanded the program, and the company says it has helped send more than 27 million flood alerts in India to date.

Sella Nevo, a senior software engineer at Google who leads the flood forecasting project, said part of its improvement in forecasting in India has involved working with the Indian government to improve how it collects data on water levels. This has reduced both erroneous water-level readings and delays that hampered forecasting in the past.

While some of these problems are specific to flood monitoring in India and other developing nations, some of the techniques Google has pioneered in India could change flood forecasting worldwide.

Nevo said even state-of-the-art flood forecasting had previously relied on hydrologic models that were based largely on maps of local topography and conceptual principles derived from physics. Each watershed was thought to be unique—leaving little ability to create a model that would work equally well across different river basins, Nevo explained.

Google, instead, took an approach largely based on A.I., in which software analyzes historical flood data taken from several different river basins in different parts of the world and trains itself to make accurate predictions for almost any river basin.

“One assumption that was presumed to be true in hydrology is that you cannot generalize across water basins,” Nevo said. “Well, it’s not true, as it turns out.” He said Google’s A.I.-based forecasting model has performed better on watersheds it has never encountered before in training than classical hydrologic models that were designed specifically for that river basin.

Of course, issuing these forecasts is one thing. Figuring out how to alert people based on them is another. And so far, exactly how people react to government-issued flood warnings and what kinds of alerts work best are topics that have been understudied.

Google said it is currently working with researchers from Yale University to try to answer some of these questions. Preliminary work by Yale and Google in India has shown that receiving an alert doubles the chance that someone will take action to protect themselves, with about 65% of all people who receive flood warnings taking some protective steps.

But the company has been working to improve these figures. This year, it said it overhauled its alerts to provide information in nine different local languages as well as in a visual formats, which can help people intuitively grasp the warning.

It is also providing people with more information about exactly how far the water is likely to rise in their specific village or area at specific times, based on the Google forecast.

This story has been updated to correctly describe the method Google has used to improve the quality of data fed into its flood forecasts. It has not used electronic sensors as part of the initiative and instead has relied on other ways to improve the timeliness and accuracy of the data.

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