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Modeling river flow for flood forecasting: A case study on the Ter river

Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when floods are imminent. In the last decade, machine learning models have been used to anticipate these hazards. In this work, we model the Ter river (NE Spain), which has historically suffered from floods, using traditional ML models such as K-nearest neighbors, Random forests, XGBoost and Linear regressors. Publicly available river flow and precipitation data was collected from year 2009 to 2021. Our analysis measures the time elapsed between observing a flow rise event at different stations (or heavy rain, for rainfall stations), and use the measured time lags to align the data from the different stations. This information provides increased interpretability to our river flow models and flood forecasters. A thorough evaluation reveals that ML techniques can be used to make short-term predictions of the river flow, even during heavy rain and large flow rise events. Moreover, our flood forecasting system provides valuable interpretable information for setting up timely preparatory actions.

Ketersediaan
200551.136Perpustakaan BIG (Eksternal Harddisk)Tersedia
Informasi Detail
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136
Penerbit
Amsterdam : Elsevier.,
Deskripsi Fisik
12 hlm PDF, 2.425 KB
Bahasa
Inggris
ISBN/ISSN
2590-1974
Klasifikasi
551.136
Tipe Isi
text
Tipe Media
-
Tipe Pembawa
-
Edisi
Vol.23, September 2024
Subjek
Machine Learning
Real-time flood forecasting
Spatio-temporal calibration
Ter river
Info Detail Spesifik
-
Pernyataan Tanggungjawab
Versi lain/terkait

Tidak tersedia versi lain

Lampiran Berkas
  • Modeling river flow for flood forecasting: A case study on the Ter river
    Floods affect chronically many communities around the world. Their socioeconomic impact increases year-by-year, boosted by global warming and climate change. Combined with long-term preemptive measures, preparatory actions are crucial when floods are imminent. In the last decade, machine learning models have been used to anticipate these hazards. In this work, we model the Ter river (NE Spain), which has historically suffered from floods, using traditional ML models such as K-nearest neighbors, Random forests, XGBoost and Linear regressors. Publicly available river flow and precipitation data was collected from year 2009 to 2021. Our analysis measures the time elapsed between observing a flow rise event at different stations (or heavy rain, for rainfall stations), and use the measured time lags to align the data from the different stations. This information provides increased interpretability to our river flow models and flood forecasters. A thorough evaluation reveals that ML techniques can be used to make short-term predictions of the river flow, even during heavy rain and large flow rise events. Moreover, our flood forecasting system provides valuable interpretable information for setting up timely preparatory actions.
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