论文标题
基于ML的洪水预测：规模，准确性和覆盖范围的进步
ML-based Flood Forecasting: Advances in Scale, Accuracy and Reach
论文作者
论文摘要
洪水是世界上最常见和致命的自然灾害之一，洪水警告系统已被证明有效减少伤害。然而，由于稳定性，计算成本和数据可用性的核心挑战，世界上大多数人的脆弱人群无法获得可靠和可行的警告系统。在本文中，我们介绍了过去一年中开发的两个洪水预测系统的组成部分，为7500万人以前没有这种访问的人提供了对这些关键系统的访问。
Floods are among the most common and deadly natural disasters in the world, and flood warning systems have been shown to be effective in reducing harm. Yet the majority of the world's vulnerable population does not have access to reliable and actionable warning systems, due to core challenges in scalability, computational costs, and data availability. In this paper we present two components of flood forecasting systems which were developed over the past year, providing access to these critical systems to 75 million people who didn't have this access before.
