Combining global precipitation data and machine learning to predict flood peaks in ungauged areas with similar climate

Author:

Rasheed Zimeena,Aravamudan Akshay,Zhang Xi,Anagnostopoulos Georgios C.,Nikolopoulos Efthymios I.ORCID

Funder

National Science Foundation

Publisher

Elsevier BV

Reference57 articles.

1. The CAMELS data set: catchment attributes and meteorology for large-sample studies;Addor;Hydrol. Earth Syst. Sci.,2017

2. The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset;Alvarez-Garreton;Hydrol. Earth Syst. Sci.,2018

3. Urbanizing the floodplain: global changes of imperviousness in flood-prone areas;Andreadis;Environ. Res. Lett.,2022

4. Austin, G., Rizzo K., Matte A., and Finnerty B. (1998). Service assessment: Ohio River Valley Flood of March 1997. National Oceanic and Atmospheric Administration, National Weather Service, Silver Spring, MD, 35 pp. Date Accessed: February 15, 2024. https://repository.library.noaa.gov/view/noaa/6398.

5. Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B., 2011. Algorithms for hyper-parameter optimization. In: Vol 24 of Neural Information Processing Systems Foundation.

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