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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.
。     8 月 26 日消息，“绿色地狱”纽博格林赛道是车手证明实力、刷新圈速纪录的圣地。

二 | 不过，越来越多的汽车制造商开始将纽博格林视为自动驾驶技术的试验场。

三 | 小米在今年夏天率先展开尝试，一辆搭载自动驾驶技术的小米 YU7 GT 用 10 分 29.483 秒完成了一圈，达成全球首个纽北自动驾驶圈速纪录。这个成绩比人工驾驶的同款车型慢了 3 分多钟，但对于一辆自动驾驶汽车来说，这样的表现已经相当不错。

四 | 如今，梅赛德斯-AMG 似乎也准备加入这一行列。近日，谍照摄影师拍到一辆 GT 63 原型车正在进行测试。乍看之下，这辆车与普通车型并没有太大区别，但车顶上安装了三个传感器。此外还可以看到，车内“驾驶员”特意将双手放在方向盘之外。据谍照摄影师透露，这辆原型车首先以非常缓慢的速度沿着赛道最右侧行驶，用于完成赛道的圈速测试和地图采集。整个过程大约耗时 25 分钟，随后车辆又切换到赛道最左侧，重复进行圈速测试和地图采集。在确定赛道两侧边缘的位置后，这辆原型车终于做好了完全脱手驾驶的准备。第一次自动驾驶正式跑圈时，车辆完成了一个“非常缓慢且谨慎的圈”。据报道，车辆整体驾驶风格十分保守，加速受到严格控制，并且会提前进行制动。这一圈耗时不到 20 分钟，车内“驾驶员”全程只是为了在出现问题时进行干预。成功完成测试后，车辆返回维修区，让工程师分析采集到的数据，并进一步调整车辆的相关设置。随后，这辆原型车再次驶上赛道进行第二圈测试，完成时间比第一圈“快了几分钟”。不过，据报道，这一成绩“距离小米创下的纪录还差得很远”。据了解，梅赛德斯-AMG 似乎在完成第二次测试后暂时结束了此次测试。

五 | 不过毫无疑问，该公司将仔细分析这次测试获得的数据，并从中总结经验。预计这辆原型车未来会再次出现在纽博格林赛道，并尝试创造属于梅赛德斯-AMG 自己的自动驾驶圈速纪录。

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