50 citations as recorded by crossref.

1. Rainfall‐runoff analysis based on competing linear impulse responses: decomposition of rainfall‐runoff processes C. Yoo & J. Park 10.1002/hyp.6633
2. Automated quality assessment of autonomously acquired microscopic images of fluorescently stained bacteria M. Zeder et al. 10.1002/cyto.a.20810
3. A risk evaluation model for karst groundwater pollution based on geographic information system and artificial neural network applications L. Bo et al. 10.1007/s12665-018-7539-7
4. Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks J. Drisya et al. 10.1007/s10668-020-00737-7
5. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions H. Maier et al. 10.1016/j.envsoft.2010.02.003
6. Current awareness 10.1002/hyp.419
7. Using Probabilistic Neural Networks for Handwritten Digit Recognition A. Lotfi & A. Benyettou 10.3923/jai.2011.288.294
8. Convective Oxygen Transport in a Constructed Wetland Pond: Mechanism, Measurements and Modelling by Multilayer Perceptrons B. SCHMID et al. 10.1081/ESE-200055681
9. Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques C. Wu et al. 10.1029/2007WR006737
10. Uncorrected land-use planning highlighted by flooding: the Alba case study (Piedmont, Italy) F. Luino et al. 10.5194/nhess-12-2329-2012
11. A comparative study of three neural network forecast combination methods for simulated river flows of different rainfall—runoff models A. SHAMSELDIN et al. 10.1623/hysj.52.5.896
12. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms J. Adamowski & C. Karapataki 10.1061/(ASCE)HE.1943-5584.0000245
13. M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey M. Taghi Sattari et al. 10.1134/S0097807813030123
14. Catchment flow estimation using Artificial Neural Networks in the mountainous Euphrates Basin A. Yilmaz et al. 10.1016/j.jhydrol.2011.09.031
15. Predictive modelling of the LD50 activities of coumarin derivatives using neural statistical approaches: Electronic descriptor-based DFT R. Hmamouchi et al. 10.1016/j.jtusci.2015.06.013
16. Competitive revenue management for fixing quota and price of hotel commodities under uncertainty S. Padhi & V. Aggarwal 10.1016/j.ijhm.2010.12.007
17. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models C. Guimarães Santos & G. Silva 10.1080/02626667.2013.800944
18. Hourly predictive Levenberg–Marquardt ANN and multi linear regression models for predicting of dew point temperature M. Zounemat-Kermani 10.1007/s00703-012-0192-x
19. Investigation internal parameters of neural network model for Flood Forecasting at Upper river Ping, Chiang Mai T. Chaipimonplin 10.1007/s12205-015-1282-3
20. Stream flow forecasting by artificial neural network (ANN) model trained by real coded genetic algorithm (GA) S. A et al. 10.5917/jagh1987.48.233
21. Application of ANN and ANFIS models for reconstructing missing flow data M. Dastorani et al. 10.1007/s10661-009-1012-8
22. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model R. Panda et al. 10.1016/j.cageo.2009.07.012
23. Spatial analysis, assessment and mapping of flood hazard in the alluvial plains of Boumerzoug and Rhumel (city of Constantine, north-eastern Algeria): application to development and urban planning projects H. Bourenane & Y. Bouhadad 10.1007/s10064-020-01980-y
24. Detection of conceptual model rainfall—runoff processes inside an artificial neural network R. WILBY et al. 10.1623/hysj.48.2.163.44699
25. Rainfall-runoff model usingan artificial neural network approach S. Riad et al. 10.1016/j.mcm.2004.10.012
26. Deep learning for water quality W. Zhi et al. 10.1038/s44221-024-00202-z
27. Application of Artificial Neural Networks in Instantaneous Peak Flow Estimation for Kharestan Watershed, Iran S. Mohammad & N. Shabani 10.5814/j.issn.1674-764x.2012.04.012
28. Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff M. Zounemat-kermani et al. 10.1016/j.asoc.2013.07.007
29. Strategic Revenue Management under Uncertainty: A Case Study on Real Estate Projects in India S. Padhi et al. 10.1002/mcda.1537
30. Artificial neural network for bedload estimation in alluvial rivers M. Sasal et al. 10.3826/jhr.2009.3183
31. Towards Safer Data-Driven Forecasting of Extreme Streamflows J. Matos et al. 10.1007/s11269-017-1834-z
32. A comprehensive survey on conventional and modern neural networks: application to river flow forecasting M. Zounemat-Kermani et al. 10.1007/s12145-021-00599-1
33. Methods to improve neural network performance in daily flows prediction C. Wu et al. 10.1016/j.jhydrol.2009.03.038
34. Closure to “Evaluation of MLP-ANN Training Algorithms for Modeling Soil Pore-Water Pressure Responses to Rainfall” by M. R. Mustafa, R. B. Rezaur, S. Saiedi, H. Rahardjo, and M. H. Isa M. Mustafa et al. 10.1061/(ASCE)HE.1943-5584.0000974
35. Assessing Hydrological and Sedimentation Effects from Bottom Topography Change in a Complex River–Lake System of Poyang Lake, China X. Ye et al. 10.3390/w11071489
36. Streamflow Modelling: A Primer on Applications, Approaches and Challenges D. Bourdin et al. 10.1080/07055900.2012.734276
37. Hydrological modelling using artificial neural networks C. Dawson & R. Wilby 10.1177/030913330102500104
38. Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting A. Sedki et al. 10.1016/j.eswa.2008.05.024
39. Data-driven models for monthly streamflow time series prediction C. Wu & K. Chau 10.1016/j.engappai.2010.04.003
40. Comparison of growth models between artificial neural networks and nonlinear regression analysis in Cherry Valley ducks C. Kaewtapee et al. 10.3382/japr.2010-00223
41. Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network Z. Latt & H. Wittenberg 10.1007/s11269-014-0600-8
42. Granular Computing for Prediction of Scour Below Spillways R. Noori et al. 10.1007/s11269-016-1526-0
43. Neural Networks for Real Time Catchment Flow Modeling and Prediction M. Aqil et al. 10.1007/s11269-006-9127-y
44. Prediction of accumulated temperature in vegetation period using artificial neural network C. Mi et al. 10.1016/j.mcm.2009.10.005
45. Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling A. Talei et al. 10.1016/j.jhydrol.2010.07.023
46. Future climate scenarios and rainfall–runoff modelling in the Upper Gallego catchment (Spain) C. Bürger et al. 10.1016/j.envpol.2007.02.002
47. Self‐organizing maps with multiple input‐output option for modeling the Richards equation and its inverse solution N. Schütze et al. 10.1029/2004WR003630
48. Enhancing hydraulic conductivity field characterization through integration of hydraulic head and tracer data using multi-modal neural network models C. Wang et al. 10.1016/j.jhydrol.2024.132295
49. Improving stream flow estimation by incorporating time delay approach in soft computing models V. Garg et al. 10.1080/09715010.2019.1676171
50. Neural pattern recognition and multivariate data: water typology of the Paraı́ba do Sul River, Brazil C. Gatts et al. 10.1016/j.envsoft.2004.03.018