![]() Limited quality and floodplains are often heavily populated (Alfieri et al.,Ģ018). Vulnerability is especially significant in low-income and middle-incomeĬountries where adequate flood mitigation measures are often lacking or of – a trend that is likely to continue (Blöschl et al., 2019). Population growth, urbanization, and theĬhanging climate have led to an increase in these numbers in recent decades Reported flooding to be the most frequent weather-related natural disaster,Īffecting the largest number of people globally and with annual economicĭamage of more than USD 30 billion. Furthermore, United Nations office for disaster risk reduction (UNISDR) (2015) Underestimation due to unreported events. That over a 27-year period, more than 175 000 people were killed and close toĢ.2 billion were affected by floods worldwide. Jonkman (2005) analyzed a natural disaster database (EM-DAT, 2021) and found Thousands of fatalities and resulting in large economic damages annually. ![]() Current and future work on the system includes extending coverage to additional flood-prone locations and improving modeling capabilities and accuracy.įloods are a major natural threat to populations worldwide causing More than 100 000 000 flood alerts were sent to affected populations, to relevant authorities, and to emergency organizations. Warning system was operational in India and Bangladesh, covering flood-prone regions around rivers with a total area close to 470 000 km 2, home to more than 350 000 000 people. During the 2021 monsoon season, the flood The thresholding and manifold models achieved similar performance metricsįor modeling inundation extent. ![]() The LSTM showed higher skills than the linear model, while When evaluated on historicalĭata, all models achieve sufficiently high-performance metrics for To hydraulic modeling of flood inundation. Presented here for the first time, provides a machine-learning alternative Latter computes both inundation extent and depth. Flood inundation is computed with the thresholding and the manifold models, where the former computes inundation extent and the Stage forecasting is modeled with the long short-term memory (LSTM) networks and the linear models. Machine learning is used for two of the subsystems. This forecasting system consists of four subsystems: data validation, stage forecasting, inundation modeling, and alert distribution. It became operational in 2018 and has since expanded geographically. Google's operational flood forecasting system was developed to provide accurate real-time flood warnings to agencies and the public with a focus on riverine floods in large, gauged rivers.
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