在自然灾害中，洪水因其不可预测性和破坏性而成为全球性的重大挑战。近日，Google Research 团队的最新研究成果在 Nature 期刊上发表，这一研究成果展示了人工智能在洪水预测领域的巨大潜力。该论文题为“Global prediction of extreme floods in ungauged watersheds”，由 Grey Nearing 及其同事共同完成。
该研究团队开发的人工智能模型通过利用全球现有的 5680 个测量仪进行训练，成功地预测了未测量流域在 7 天预测期内的日径流。这一模型的预测准确率与目前全球领先的短期和长期洪水预测软件——全球洪水预警系统（GloFAS）相当甚至更高。
该模型的创新之处在于它能够处理复杂的地形和气候数据，从而对未被充分监测的流域进行可靠的洪水预报。这一突破对于提高全球洪水预警能力具有重要意义，特别是对于缺乏先进监测设备的地区。
Google Research 团队的这一成果不仅展示了人工智能在洪水预测领域的应用前景，也为全球范围内的洪水风险管理提供了新的工具和技术支持。预计这一模型将有助于提高灾害响应的时效性和准确性，从而减少洪水造成的损失和人员伤亡。
英文标题：Google AI model revolutionizes flood prediction, study published in Nature
英文关键词：Google Research, AI model, flood prediction
英文新闻内容：
In the realm of natural disasters, floods pose a significant challenge globally due to their unpredictability and destructive force. Recently, a groundbreaking study by the Google Research team was published in the prestigious journal Nature, showcasing the immense potential of artificial intelligence in flood prediction. The paper, titled “Global prediction of extreme floods in ungauged watersheds,” was co-authored by Grey Nearing and his colleagues.
The research team developed an AI model that leverages data from over 5,680 existing sensors worldwide to predict daily runoff in ungauged basins over a seven-day forecast period. The model’s prediction accuracy is on par with, and in some cases exceeds, that of the current global leader in short- and long-term flood forecasting software, the Global Flood Awareness System (GloFAS).
The innovation of this model lies in its ability to process complex terrain and climate data, enabling reliable flood forecasts for areas with insufficient monitoring. This breakthrough is significant for enhancing global flood early warning capabilities, particularly in regions lacking advanced monitoring infrastructure.
The Google Research team’s achievement not only demonstrates the application of AI in flood prediction but also provides new tools and technical support for flood risk management worldwide. It is expected that this model will contribute to improving the timeliness and accuracy of disaster responses, thus reducing the loss of life and property caused by floods.
【来源】https://mp.weixin.qq.com/s/GoOPqLtdYvPv3_no7GJUJQ
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