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熊猫Climate Gambit: Chinese team develops ‘super brain’ to guide flood precautions using weather, hydraulic and terrain data_我的网站
A | 新加坡的医疗成本不断上涨，引发了广泛的社会关注。  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.  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. 。而背后潜藏的主要问题——健康保险中的“按收费”模式，正逐渐成为国家金融灾难的潜在威胁。
B | 1. “按收费”计划的起源与问题2005年，新加坡健康保险领域的新秀Aviva引入了“按收费”计划，打破了健康保险市场的格局。该计划允许保险公司为医生和医院批准的任何治疗买单，导致医疗成本迅速上涨。这种模式让医生和医院可以选择最昂贵的治疗方案，而患者则因相信“最好的治疗”而积极参与。结果，医疗费用激增，公共医疗支出大幅攀升，最终负担转嫁给了消费者。数据显示，自2009年以来，政府在医疗保健方面的支出已增加了四倍，这种不可持续的增长趋势引发了社会的广泛担忧。来源：clema risk solutions2. 成本上升的背后：谁在为“按收费”买单随着“按收费”计划的普及，私立医院的医疗费用远远超过了公立医院，导致整体医疗成本的大幅上涨。研究表明，有“按收费”保险的患者，其医疗支出比没有此类保险的患者高出25%。这一问题引发了政府的关注。李显龙资政呼吁遏制过度治疗和过度开药，以控制医疗成本的攀升。然而，政治意愿的不足使得问题迟迟未得到根本解决。在这一背景下，政府近年来尝试了一些零碎的措施，例如限制医生选择，但这些措施效果有限，未能真正解决成本上升的核心问题。来源：联合早报3. 变革迫在眉睫：如何解决“按收费”问题要解决医疗成本上升的问题，必须从根本上改革“按收费”保险计划。我的建议是，取消在综合盾计划中使用MediSave资金的“按收费”覆盖范围，转而采用更具成本效益的计划。此外，保险计划的可移植性问题也需要得到解决。新加坡精算协会的研究表明，提升保险的可移植性将有助于增强市场竞争，允许新加坡人选择更适合的保险产品。总之，取消“按收费”计划和提升保险的可移植性，将是解决新加坡医疗成本问题的关键举措。这是一场必须进行的改革，为了全社会的利益，迫切需要采取行动。来源：联合早报面对日益严峻的医疗成本问题，新加坡亟需对现有的健康保险体系进行全面改革。只有通过各方的共同努力，才能为全体居民提供可持续的、负担得起的医疗保障。参考资料：1. Commentary: To contain spiralling healthcare costs in Singapore, we must remove the original sin，channelnewsasia。 Current article:http://6h4n.foucaorengtunzhuaikuowang.cyou/news/20260826_16722.html Published on:14:21:33 |
