情牵两世

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

A |     

    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.
。          　　机动车驾驶证考试是法律规定的国家考试，事关道路交通安全与公共出行安全。部分人员受利益驱使，打出“包过”噱头，在驾考中组织作弊，不仅严重破坏考试公平秩序，更给道路交通安全埋下重大隐患，自己也触碰法律红线，最终受到法律严惩。     　　记者从济南市槐荫区人民法院获悉，日前该院审结一起组织驾考作弊刑事案件，驾校从业人员赵某伙同钱某等4人共谋驾考作弊牟利，由赵某提供微型视听作弊设备，团伙成员远程看题传答案，以每人4000元至7500元的价格招揽考生作弊。经查，该团伙累计组织16人驾考作弊，非法获利近7万元。

B | 法院审理认定，5人在法定国家考试中分工协作、多次组织作弊，属情节严重，均构成组织考试作弊罪。最终依法判处5人一年至三年四个月不等有期徒刑并处罚金，全额追缴违法所得。法官提醒，驾考无捷径，切勿轻信“包过”骗局，从业者需严守法律底线，考试作弊、组织助考均需承担刑事责任。     　　据了解，赵某从事驾驶员培训工作。

C | 2023年11月，赵某与钱某等5人共谋组织驾驶员理论考试作弊牟利，赵某负责给考生提供微型视听设备进入考场，钱某等人通过微型摄像头远程看题后，再通过隐形耳机告知考生答案，每次向每名考生收取4000元至7500元不等的费用。

D | 后来，赵某等人被公安机关抓获，经查，他们先后共组织16人作弊，违法所得近7万元。公诉机关依法将赵某等人起诉至法院。     　　法院审理后认为，被告人赵某等5人在法律规定的国家考试中，利用作弊设备多次组织考试作弊，他们的行为均已构成组织考试作弊罪，且情节严重，应予依法惩处。最终，法院依法判决，赵某等人犯组织考试作弊罪，分别被判处有期徒刑三年四个月至一年不等的刑罚并处罚金，责令被告人退缴违法所得。

E |      　　法官表示，根据《中华人民共和国道路交通安全法》规定，驾驶机动车应当依法取得机动车驾驶证，机动车驾驶资格考试属于法律规定的国家考试范畴。

F | 根据《中华人民共和国刑法》规定，在法律规定的国家考试中组织作弊的，构成组织考试作弊罪，处三年以下有期徒刑或者拘役，并处或者单处罚金;情节严重的，处三年以上七年以下有期徒刑，并处罚金。依据《最高人民法院、最高人民检察院关于办理组织考试作弊等刑事案件适用法律若干问题的解释》，多次组织考试作弊的，应当认定为“情节严重”。本案中，赵某等5人分工明确，利用微型摄像头、隐形耳机等设备远程传递试题答案，严重扰乱国家考试管理秩序，已达到“情节严重”的认定标准。法院结合各被告人在共同犯罪中的作用、自愿认罪认罚等量刑情节，依法作出相应判决。     　　法官提醒广大考生，应摒弃“包过”“走捷径”的侥幸心理，诚信备考、依规应考，切勿轻信所谓“考试保过”的虚假宣传，否则不仅考试成绩会被作废、被限制报考资格，还可能承担相应不利后果。相关从业人员应恪守职业底线与法律红线，依法合规开展培训业务，切勿为牟取不法利益参与、介绍考试作弊，以身试法终将付出沉重的代价。

G |

Current article:http://www.bainangmieruopanglaomen.sbs/news/20260826_7122058.doc

Published on:12:46:27