Unleashing the power of AI: revolutionizing runoff prediction beyond NRCS-CN method
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12517-024-12031-1.pdf
Reference46 articles.
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2. Adnan R, Petroselli A, Heddam S et al (2021) Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach. Nat Hazards 105:2987–3011. https://doi.org/10.1007/s11069-020-04438-2
3. Akbari A, Daryabor F, Abu Samah A, Shirmohammadi AZ (2021) Improving runoff estimation by raster-based natural resources conservation service-curve number adjustment for a new initial abstraction ratio in semi-arid climates. River Res Appl 37(9):1333–1342. https://doi.org/10.1002/rra.3840
4. Aoulmi Y, Marouf N, Amireche M (2021) The assessment of artificial neural network rainfall-runoff models under different input meteorological parameters case study: seybouse basin, Northeast Algeria. J Water Land Dev 50:38–47
5. Asadi H, Shahedi K, Jarihani B, Sidle RC (2019) Rainfall-runoff modelling using hydrological connectivity index and artificial neural network approach. Water 11(2):212. https://doi.org/10.3390/w11020212
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