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Neural Rainfall-Runoff Modeling
Hydrological modeling traditionally relies on mechanistic models that, while physically interpretable, struggle to capture the nonlinear dynamics of environmental systems. Data-driven approaches such as Artificial Neural Networks (ANNs) can automatically discover patterns in observational data, often achieving superior performance, but typically ...openaire +1 more source
Computational Intelligence in Rainfall-Runoff Modeling.
2009Civil Engineering and ...
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Application of Rainfall-Runoff Modeling With Neuro-Fuzzy.
2007The use of fuzzy approach is becoming increasingly common in the analysis of hydrology and water resources problems. In this research, a fuzzy inference system was developed and used to model the rainfall-runoff relationship on a semi-arid catchment, namely the Kurukavak catchment, which is a sub-basin of the Sakarya basin in Turkey.
Yürekli, B., Oysal, Yusuf, Tombul, M.
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RR-Former: Rainfall-runoff modeling based on Transformer
Journal of Hydrology, 2022Hanlin Yin +2 more
exaly
An assessment of rainfall-runoff modeling methodology
2010This study reports model performance calculations for three event-based rainfall-runoff models on both real and synthetic data sets. The models include a regression model, a unit hydrograph model and a quasi-physically based model. The real data sets are for small upland catchments from the Washita River Experimental Watershed, Oklahoma; the Mahantango
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Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model
Journal of Hydrology, 2021Hanlin Yin +2 more
exaly
Rainfall-runoff modeling using long short-term memory based step-sequence framework
Journal of Hydrology, 2022Hanlin Yin +2 more
exaly