55 citations as recorded by crossref.

1. Classification of hydro-meteorological conditions and multiple artificial neural networks for streamflow forecasting E. Toth 10.5194/hess-13-1555-2009
2. Application of surrogate artificial intelligent models for real‐time flood routing H. Ghalkhani et al. 10.1111/j.1747-6593.2012.00344.x
3. Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model C. Young & W. Liu 10.1080/02626667.2014.959446
4. Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting H. Yonaba et al. 10.1061/(ASCE)HE.1943-5584.0000188
5. Review of Recent Developments in Hydrologic Forecast Merging Techniques M. Sheikh & P. Coulibaly 10.3390/w16020301
6. Simple modifications of the nonlinear regression stream temperature model for daily data A. Piotrowski & J. Napiorkowski 10.1016/j.jhydrol.2019.02.035
7. Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction N. PRAMANIK & R. PANDA 10.1623/hysj.54.2.247
8. Knowledge Extraction from Artificial Neural Networks for Rainfall-Runoff Model Combination Systems P. Phukoetphim et al. 10.1061/(ASCE)HE.1943-5584.0000941
9. Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network M. Sulaiman et al. 10.1007/s11269-011-9824-z
10. Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling S. Galelli & A. Castelletti 10.5194/hess-17-2669-2013
11. Comparing various artificial neural network types for water temperature prediction in rivers A. Piotrowski et al. 10.1016/j.jhydrol.2015.07.044
12. Closure to “Improving Prediction Accuracy of Hydrologic Time Series by Least-Squares Support Vector Machine Using Decomposition Reconstruction and Swarm Intelligence” W. Niu et al. 10.1061/JHYEFF.HEENG-5901
13. Comparative study of different wavelet based neural network models for rainfall–runoff modeling M. Shoaib et al. 10.1016/j.jhydrol.2014.04.055
14. Flood prediction in southern strip of Caspian Sea watershed S. Chavoshi et al. 10.1134/S0097807813060122
15. Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach N. Valizadeh et al. 10.1155/2014/432976
16. Artificial neural networks aided conceptual stage design of water harvesting structures V. Chandwani et al. 10.1016/j.pisc.2016.03.015
17. Streamflow simulation methods for ungauged and poorly gauged watersheds A. Loukas & L. Vasiliades 10.5194/nhess-14-1641-2014
18. Using Artificial Neural Networks to Forecast Monthly and Seasonal Sea Surface Temperature Anomalies in the Western Indian Ocean S. Mahongo & M. Deo 10.1260/1759-3131.4.2.133
19. 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
20. Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data A. Sohail et al. 10.1007/s11269-006-9141-0
21. Runoff forecasting by artificial neural network and conventional model A. Ghumman et al. 10.1016/j.aej.2012.01.005
22. Framework for developing hybrid process-driven, artificial neural network and regression models for salinity prediction in river systems J. Hunter et al. 10.5194/hess-22-2987-2018
23. Current awareness 10.1002/hyp.5099
24. On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks A. Piotrowski et al. 10.1080/02626667.2015.1085650
25. Climate models and hydrological parameter uncertainties in climate change impacts on monthly runoff and daily flow duration curve of a Mediterranean catchment H. Sellami et al. 10.1080/02626667.2015.1040801
26. Streamflow forecasting using least-squares support vector machines A. Shabri & . Suhartono 10.1080/02626667.2012.714468
27. Modeling Slump of Ready Mix Concrete Using Genetically Evolved Artificial Neural Networks V. Chandwani et al. 10.1155/2014/629137
28. Artificial Neural Network Modeling of the Water Quality Index Using Land Use Areas as Predictors N. Gazzaz et al. 10.2175/106143014X14062131179276
29. Current awareness 10.1002/hyp.5126
30. A combined rotated general regression neural network method for river flow forecasting S. Yin et al. 10.1080/02626667.2014.944525
31. Application of neural network and time series modeling to study the suitability of drain water quality for irrigation: a case study from Egypt M. Abdel-Fattah et al. 10.1007/s11356-020-10543-3
32. 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
33. Error Correction of Rainfall-Runoff Models With the ARMAsel Program P. Broersen 10.1109/TIM.2007.908252
34. Artificial neural network modeling of the water quality index for Kinta River (Malaysia) using water quality variables as predictors N. Gazzaz et al. 10.1016/j.marpolbul.2012.08.005
35. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition W. Wang et al. 10.1016/j.envres.2015.02.002
36. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model R. Panda et al. 10.1016/j.cageo.2009.07.012
37. Monthly Streamflow Forecasting Using Decomposition-Based Hybridization with Two-step Verification Method Over the Mangla Watershed, Pakistan M. Tayyab et al. 10.1007/s40996-022-00947-1
38. River flow forecasting through nonlinear local approximation in a fuzzy model P. Nayak et al. 10.1007/s00521-014-1684-z
39. Development of Process and Data Centric Inference System for Enhanced Production of L-Asparaginase from Halotolerant Bacillus licheniformis PPD37 P. Patel et al. 10.1007/s12010-021-03707-5
40. Comparison of artificial neural networks and autoregressive model for inflows forecasting of Roseires Reservoir for better prediction of irrigation water supply in Sudan M. Abdellatif et al. 10.1080/15715124.2014.1003381
41. A metamodeling with CFD method for hydrodynamic optimisations of deflectors on a multi-wing trawl door G. Wang et al. 10.1016/j.oceaneng.2021.109045
42. Toward Optimal Rainfall for Flood Prediction in Headwater Basins—Orographic QPE Error Modeling Using Machine Learning M. Liao & A. Barros 10.1029/2023WR034456
43. Grey model for prediction of pore pressure change M. Mohammed et al. 10.1007/s12665-009-0287-y
44. Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks V. Chandwani et al. 10.1016/j.eswa.2014.08.048
45. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al. 10.1177/0309133312444943
46. Bioengineering for multiple PAHs degradation using process centric and data centric approaches H. Gosai et al. 10.1016/j.chemolab.2018.04.019
47. Review of Nitrogen Compounds Prediction in Water Bodies Using Artificial Neural Networks and Other Models P. Kumar et al. 10.3390/su12114359
48. Surface Water Quality Model: Impacts of Influential Variables P. Yousefi et al. 10.1061/(ASCE)WR.1943-5452.0000900
49. Short‐term flood forecasting with a neurofuzzy model P. Nayak et al. 10.1029/2004WR003562
50. RETRACTED: Machine learning approaches for estimation of sediment settling velocity S. Zhu et al. 10.1016/j.jhydrol.2020.124911
51. Prediction of mechanical properties of non-welded and moderately welded ignimbrite using physical properties, ultrasonic pulse velocity, and point load index tests A. Binal 10.1144/1470-9326/07-067
52. Application of Machine Learning Techniques in Rainfall–Runoff Modelling of the Soan River Basin, Pakistan M. Khan et al. 10.3390/w13243528
53. Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast P. Maca et al. 10.1155/2014/782351
54. A hybrid linear‐neural model for river flow forecasting M. Chetan & K. Sudheer 10.1029/2005WR004072
55. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al. 10.1080/02626667.2016.1159683