Evaluating Urban Stream Flooding with Machine Learning, LiDAR, and 3D Modeling

نویسندگان

چکیده

Flooding in urban streams can occur suddenly and cause major environmental infrastructure destruction. Due to the high amounts of impervious surfaces watersheds, runoff from precipitation events a rapid increase stream water levels, leading flooding. With increasing urbanization, it is critical understand how channels will respond prevent catastrophic This study uses Prophet time series machine learning algorithm forecast hourly changes level an stream, Hunnicutt Creek, Clemson, South Carolina (SC), USA. Machine was highly accurate predicting for five locations along with R2 values greater than 0.9. Yet, be challenging these prediction translate volume channel. Therefore, this collected terrestrial Light Detection Ranging (LiDAR) data Creek model areas 3D illustrate predicted levels correspond The were also used calculate upstream flood volumes provide further context small Overall, methodology determined that more impacts experience larger rises during storm events. Together, innovative combining learning, LiDAR, modeling, calculations provides new techniques flood-prone environments.

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ژورنال

عنوان ژورنال: Water

سال: 2023

ISSN: ['2073-4441']

DOI: https://doi.org/10.3390/w15142581