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Climate Gambit: Chinese team develops ‘super brain’ to guide flood precautions using weather, hydraulic and terrain data_我的网站

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    Students from Xi'an University of Technology test a virtual reality-enabled emergency evacuation simulation system tailored for flood disasters on January 12, 2024. Photos: Courtesy of Xi'an University of TechnologyEditor's Note:
Extreme weather is increasingly a global challenge, and the key to addressing climate risks lies in earlier prediction, more precise action and smarter preparedness, with emerging technologies playing a vital role. The Global Times launches the "Climate Gambit" series, exploring how research teams are leveraging cutting-edge technologies, including artificial intelligence, high-performance computing and smart observation systems, to anticipate weather changes, enhance disaster early-warning and strengthen resilience against climate risks.
Inside a state key laboratory at Xi'an University of Technology, Northwest China's Shaanxi Province, there is a miniature but complete "water world" which simulated water channels, inland lakes and main rivers to recreate real flood scenarios and test their newly developed GPU Accelerated Surface Water Flow and Transport Model (GAST).
Known as a "super brain" for flood control, GAST can complete flood simulations involving more than 3 million computational units within 30 seconds, helping transform flood management from a reaction to emergency into active precautions since "flooding impacts can be predicted even before rainfall arrives."
At a time when extreme rainfall and summer flooding have become increasingly frequent, questions such as when the flooding will arrive, which roads may be submerged and when residents should evacuate have become increasingly important.
In an exclusive interview with the Global Times, Hou Jingming, a professor at Xi'an University of Technology and the leader of the research team, explained how the GAST model seeks to answer these questions by accurately predicting flood development and identifying vulnerable areas before disasters occur, and how the model helps authorities take preventive measures to reduce casualties and economic losses.
AI empowering 'flood drill'  
The water tank system in the lab was designed to create a controllable, repeatable and observable environment to simulate complex hydrological processes, including river flooding, urban water level changes, lake regulation, drainage pump operations and coordinated flood-control measures.
By adjusting variations such as upstream water inflow, rainfall intensity, downstream water levels and drainage conditions, scientists can recreate different flood scenarios. Meanwhile, water levels, flow speeds and other data are collected in real time and displayed on a digital twin platform.
"If a rainstorm and corresponding floods are an exam, GAST is like a 'drill,'" Hou said. "It can simulate how floods develop, where water will flow, which areas may be inundated and when river levels may rise, ensuring authorities are well but not overly prepared."
To answer the public's concern about "whether my neighborhood will be flooded when heavy rain arrives," the team developed new algorithms for urban surface water flow, including improvements in terrain slope and friction calculations.
These breakthroughs have improved simulation accuracy in complex urban environments. Compared with extensive monitoring data, GAST can keep simulation errors of key hydrodynamic factors within 15 percent. This means the model can provide not only general flood trends, but also quantitative information such as water depth, flow speed and inundation areas.
Combined with AI technologies, it can identify complex relationships between rainfall, water conditions, flood depth, flow velocity and affected areas, cutting simulations from hours in traditional methods to minutes or even seconds.
The faster calculation capability means that once meteorological authorities update forecasts, the model can quickly estimate flood risks in different parts of a city. 
"The earlier rainfall warnings are issued, the earlier we can identify potential flooding hotspots and high-risk areas," Hou said. "This saves valuable time for evacuation, traffic management and emergency deployment."
For smarter disaster response

Building an accurate flood prediction model also requires integrating large amounts of urban data other than weather forecasts, including urban terrain, drainage networks and infrastructure information.
For example, a model developed for Xi'an incorporates geographic data and drainage system information collected from relevant authorities and field surveys. After receiving rainfall forecasts, the system can quickly calculate possible flooding scenarios, showing when and where waterlogging may occur and highlighting vulnerable roads and areas through visual maps.
To demonstrate how the super brain works in case of possible flooding, the laboratory has set a virtual reality area where visitors can experience a simulated urban flooding evacuation in the Xiaozhai area of Xi'an. Wearing VR headsets, participants can see water levels gradually rising and follow emergency instructions to move toward higher ground.
The entire technological package has already been applied in real-world flood prevention.
    A 3D live-scene display lab in Xi'an that oversees stormwater drainage performance in Hengshui, North China's Hebei Province Photos: Courtesy of Xi'an University of Technology
During Typhoon Muifa in 2022, Haishu district in Ningbo, East China's Zhejiang Province, recorded a regional rainfall of 367 millimeters. Using GAST as its core technology, the local flood forecasting platform integrated weather forecasts, AI algorithms and real-time monitoring data to provide rolling three-hour flood risk predictions.
Post-event assessments showed that predicted risks at most locations matched actual flooding conditions. The average relative error between predicted and observed maximum water depths was 13 percent.
The GAST model was also integrated into a smart rain and flood management platform in Qinhan new city area in Xianyang of Shaanxi, and during a rainstorm warning in July 2022, the platform provided continuous monitoring and forecasts. Based on the results, local authorities shifted from routine inspections to targeted monitoring of flood-prone areas and optimized emergency drainage operations.
The model is also being applied to mountain torrent prevention, as it can simulate rapidly changing flows in complex terrain and, combined with machine learning, complete forecasts within seconds. For reservoirs and rivers, it supports sudden and gradual dam-break simulations.
In June 2026, the model was presented at a national symposium on flood risk mapping achievements. The technology has since been applied by water resources, emergency management and urban development authorities, expanding from Shaanxi to multiple provinces and regions across China.
Looking ahead, the research team is developing a framework that further keeps up with the pace focusing on AI technologies. "Currently, the system operates based on weather forecast, therefore, AI will increase efficiency by using historical cases and real-time monitoring data to correct errors and update forecasts dynamically," Hou said.
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曼联再次与荷兰前锋哈维·西蒙斯（Xavi Simons）联系在一起，西班牙「Fichajes转会网」报道称，红魔欲以1.35亿欧元向其效力的德甲莱比锡红牛求购，这一数字将远远超过俱乐部队史1亿欧元的最高引援纪录。

如果莱比锡接受曼联的报价，他们将获得5500万欧元的利润。本赛季，他们用5000万欧元从巴黎圣日耳曼买断了西蒙斯，加上浮动条款最高可达8000万欧元。

当然，此事目前还纯属传闻，曼联确实需要西蒙斯这样的球员，荷兰国脚本赛季参与了16个进球（10球6助攻），能够胜任多个进攻位置，阿莫林想要这样充满活力的全面前锋。

21岁的西蒙斯也可能愿意踢英超，但他的最大梦想还是重返巴萨。

二 | 「我还是个年轻球员，我有很多梦想，俱乐部也知道。」西蒙斯说，「但现在对我来说，最重要的是在接下来的比赛中打好比赛。然后我们还要参加国际比赛，之后的问题我们再观望一下。」

据西班牙《世界体育报》报道，西蒙斯的内心似乎仍然眷恋着青训时期效力的巴萨。

三 | 他在2010年至2019年期间，曾在著名的拉玛西亚青训学院效力近10年，他从未掩饰过自己对巴萨的热爱。

不过，巴萨没钱，意大利转会专家法布里齐奥·罗马诺表示，西蒙斯转会英超联赛的可能性更大，因为那里对他感兴趣的球队不只是曼联。这名球员也与利物浦联系在一起，阿森纳和切尔西本赛季也对其进行了考察。

只不过，1.35亿欧元的报价传闻完全令人无法相信。这一说法恐怕就像上周奥斯梅恩同意64万欧元周薪加盟曼联一样，更像是球员经纪人的「抬价」手段。

利物浦更可能得到西蒙斯，如果他们真正采取行动的话。红牛全球足球主管恰是前利物浦主帅克洛普，后者希望削减莱比锡1700万英镑的工资预算。罗马诺认为，6400万英镑足以签下他。

罗马诺解释道：「正如几周前报道的那样，哈维·西蒙斯计划在夏季转会。

四 | 英超俱乐部被告知，他们是西蒙斯最有可能的目的地，大约7500万欧元的转会费足以说服莱比锡。西蒙斯梦想有一天能为巴萨效力，但这个夏天不太可能。

五 | 」

曼联夏窗确实想要引进一名中锋和一名10号位的球员，除了西蒙斯，还有许多球员也是阿莫林的潜在引进目标，例如狼队10号库尼亚和南安普顿边锋泰勒·迪布林（Tyler Dibling）。其中，19岁的迪布林传闻被标价1亿英镑！

对于这一传闻，迪布林周末1-1战平西汉姆联后公开进行了回应：「兰博（拉姆斯代尔）很喜欢这些传闻，每次我在训练中射门没打正，他都会高喊：『9000万英镑！8000万英镑！』他把我的身价一千万一千万调低，这很有趣。

六 | 还有人给我起了一些绰号，都是开玩笑而已，没人当真。这只是传闻中的数字，看着玩就好。」

提及转会曼联的传闻，迪布林表示：「我不会去想这些，只是每天努力训练，努力付出，尽我所能。总而言之，我爱南安普顿，我从8岁起就在这里。这是一支非常棒的球队，我正在努力和他们一起卷土重来。没有人想成为联赛历史上最差的球队，我不想，队里也没人想，所以我们现在的目标就是拿一分是一分，再多拿几分，这样我们就不会被冠以最差英超球队的头衔。

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Published on:14:36:49