22 citations as recorded by crossref.

1. Estimation of parameters of the transient storage model by means of multi-layer perceptron neural networks / Estimation des paramètres du modèle de transport TSM au moyen de réseaux de neurones perceptrons multi-couches P. ROWIŃSKI & A. PIOTROWSKI https://doi.org/10.1623/hysj.53.1.165
2. Development of a short-term river flood forecasting method for snowmelt driven floods based on wavelet and cross-wavelet analysis J. Adamowski https://doi.org/10.1016/j.jhydrol.2008.02.013
3. Complexity selection of a neural network model for karst flood forecasting: The case of the Lez Basin (southern France) L. Siou et al. https://doi.org/10.1016/j.jhydrol.2011.04.015
4. A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling A. Piotrowski & J. Napiorkowski https://doi.org/10.1016/j.jhydrol.2012.10.019
5. Benefits and limitations of data assimilation for discharge forecasting using an event-based rainfall–runoff model M. Coustau et al. https://doi.org/10.5194/nhess-13-583-2013
6. River flow forecasting using wavelet and cross‐wavelet transform models J. Adamowski https://doi.org/10.1002/hyp.7107
7. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms J. Adamowski & C. Karapataki https://doi.org/10.1061/(ASCE)HE.1943-5584.0000245
8. A review of advances in flash flood forecasting H. Hapuarachchi et al. https://doi.org/10.1002/hyp.8040
9. The application of Dynamic Linear Bayesian Models in hydrological forecasting: Varying Coefficient Regression and Discount Weighted Regression M. Ciupak et al. https://doi.org/10.1016/j.jhydrol.2015.10.023
10. Copula-based geohazard assessment – case of flood-prone area in Poland A. Perz et al. https://doi.org/10.1016/j.ejrh.2022.101214
11. Optimizing neural networks for river flow forecasting – Evolutionary Computation methods versus the Levenberg–Marquardt approach A. Piotrowski & J. Napiorkowski https://doi.org/10.1016/j.jhydrol.2011.06.019
12. A computationally efficient flash flood early warning system for a mountainous and transboundary river basin in Bangladesh N. Biswas et al. https://doi.org/10.2166/hydro.2020.202
13. Multistep-ahead flood forecasting using wavelet and data-driven methods Y. Seo et al. https://doi.org/10.1007/s12205-015-1483-9
14. Modeling dimensionless longitudinal dispersion coefficient in natural streams using artificial intelligence methods Z. Toprak et al. https://doi.org/10.1007/s12205-014-0089-y
15. Longitudinal Dispersion Coefficient Modeling in Natural Channels using Fuzzy Logic Z. Fuat Toprak & M. Savci https://doi.org/10.1002/clen.200700122
16. Numerical Simulation of Alpine Flash Flood Flow and Sedimentation in Gullies With Large Gradient Variations Y. Ding et al. https://doi.org/10.3389/fenvs.2022.858692
17. A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile L. Díaz-Robles et al. https://doi.org/10.1016/j.atmosenv.2008.07.020
18. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds J. Adamowski & K. Sun https://doi.org/10.1016/j.jhydrol.2010.06.033
19. Flood modeling and fluvial dynamics: A scoping review on the role of sediment transport H. Hamidifar et al. https://doi.org/10.1016/j.earscirev.2024.104775
20. Predicting longitudinal dispersion coefficient in natural streams by artificial intelligence methods Z. Toprak & H. Cigizoglu https://doi.org/10.1002/hyp.7012
21. Rainfall-runoff modeling of flash floods in the absence of rainfall forecasts: the case of “Cévenol flash floods” M. Toukourou et al. https://doi.org/10.1007/s10489-010-0210-y
22. Flash flood forecasting in poorly gauged basins using neural networks: case study of the Gardon de Mialet basin (southern France) G. Artigue et al. https://doi.org/10.5194/nhess-12-3307-2012