Comparative analysis of data-driven and conceptual streamflow forecasting models with uncertainty assessment in a major basin in Iran
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42108-023-00276-7.pdf
Reference43 articles.
1. Abbaspour, K. C., Faramarzi, M., Ghasemi, S. S., & Yang, H. (2009). Assessing the impact of climate change on water resources in Iran. Water Resources Research, 45(10), 1–16. https://doi.org/10.1029/2008WR007615
2. Abbaspour, K. C., Rouholahnejad, E., Vaghefi, S., Srinivasan, R., Yang, H., & Kløve, B. (2015). A continental-scale hydrology and water quality model for Europe: Calibration and uncertainty of a high-resolution large-scale SWAT model. Journal of Hydrology, 524, 733–752. https://doi.org/10.1016/j.jhydrol.2015.03.027
3. Abbaspour, K. C., Yang, J., Maximov, I., Siber, R., Bogner, K., Mieleitner, J., Zobrist, J., & Srinivasan, R. (2007). Modelling hydrology and water quality in the pre-alpine/alpine thur watershed using SWAT. Journal of Hydrology, 333(2–4), 413–430. https://doi.org/10.1016/j.jhydrol.2006.09.014
4. Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Williams, J. R. (1998). Large area hydrologic modeling and assessment part I: Model development. Journal of the American Water Resources Association, 34(1), 73–89. https://doi.org/10.1111/J.1752-1688.1998.TB05961.X
5. Bai, T., & Tahmasebi, P. (2023). Graph neural network for groundwater level forecasting. Journal of Hydrology, 616, 128792. https://doi.org/10.1016/j.jhydrol.2022.128792
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