73 citations as recorded by crossref.

1. A comparative study of hybrid artificial neural network models for predicting Cr(VI) adsorption onto activated carbon C. Sutherland et al.
2. Flood forecasting in large rivers with data-driven models P. Nguyen et al.
3. Dynamic simulation of vapor-compression cycle using neural networks Y. Yoon & M. Lee
4. Ideal point error for model assessment in data-driven river flow forecasting C. Dawson et al.
5. Monthly total sediment forecasting using adaptive neuro fuzzy inference system M. Firat & M. Güngör
6. Neural Network Based Seasonal Predictions of Lake-Effect Snowfall H. Hartmann
7. Improving Dam Seepage Prediction Using Back-Propagation Neural Network and Genetic Algorithm X. Zhang et al.
8. An RBF‐based model with an information processor for forecasting hourly reservoir inflow during typhoons G. Lin et al.
9. Computer Aided Numerical Methods for Hydrological Model Calibration: An Overview and Recent Development G. Kan et al.
10. Comparative analysis of data driven rainfall-runoff models in the Kolar river basin D. Tiwari et al.
11. Application of surrogate artificial intelligent models for real‐time flood routing H. Ghalkhani et al.
12. Modeling the Time Variation of Reservoir Trap Efficiency V. Garg & V. Jothiprakash
13. Multilayer perceptron neural network-based approach for modeling phycocyanin pigment concentrations: case study from lower Charles River buoy, USA S. Heddam
14. Monthly stream flow forecasting via dynamic spatio-temporal models M. Dehghani et al.
15. Multilayer perceptron and support vector machine trained with grey wolf optimiser for predicting floods in Barak river, India A. Sahoo et al.
16. An Hourly Streamflow Forecasting Model Coupled with an Enforced Learning Strategy M. Wu & G. Lin
17. Suspended Sediment Yield Forecasting with Single and Multi-Objective Optimization Using Hybrid Artificial Intelligence Models A. Yadav et al.
18. Flood prediction in southern strip of Caspian Sea watershed S. Chavoshi et al.
19. Selection of smoothing parameter estimators for general regression neural networks – Applications to hydrological and water resources modelling X. Li et al.
20. Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network C. Gao et al.
21. Comparison of Multivariate Regression and Artificial Neural Networks for Peak Urban Water-Demand Forecasting: Evaluation of Different ANN Learning Algorithms J. Adamowski & C. Karapataki
22. Hybrid Deep Learning Modeling for Water Level Prediction in Yangtze River Z. Xie et al.
23. Improving Ann‐Based Short‐Term and Long‐Term Seasonal River Flow Forecasting with Signal Processing Techniques H. Badrzadeh et al.
24. Characteristics of modelling hourly water surface evaporation in Lake Taihu and comparison of simulation results by three models W. Dandan et al.
25. Use of Optimally Pruned Extreme Learning Machine (OP-ELM) in Forecasting Dissolved Oxygen Concentration (DO) Several Hours in Advance: a Case Study from the Klamath River, Oregon, USA S. Heddam
26. Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data A. Sohail et al.
27. 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.
28. Runoff modeling in Kolar river basin using hybrid approach of wavelet with artificial neural network D. Tiwari et al.
29. Discharge projection in the Yangtze River basin under different emission scenarios based on the artificial neural networks X. Zeng et al.
30. Developing a hybrid multi‐model for peak flood forecasting Y. Chidthong et al.
31. Hybridizing Artificial Intelligence Algorithms for Forecasting of Sediment Load with Multi-Objective Optimization A. Yadav et al.
32. Dynamic conversion coefficients improve alpine lake daily evaporation estimation based on multi-evaporator observations F. Zhang et al.
33. A Neural Network Approach to Smarter Sensor Networks for Water Quality Monitoring E. O’Connor et al.
34. Current awareness
35. DAMP: A protocol for contextualising goodness-of-fit statistics in sediment-discharge data-driven modelling R. Abrahart et al.
36. Evaluating the substitution potential of SBS with crumb rubber-polypropylene blends as asphalt binder and mixture modifiers D. Nasr et al.
37. Current awareness
38. Improving Flood Forecasting in a Developing Country: A Comparative Study of Stepwise Multiple Linear Regression and Artificial Neural Network Z. Latt & H. Wittenberg
39. Auto-control of pumping operations in sewerage systems by rule-based fuzzy neural networks Y. Chiang et al.
40. 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
41. A Study on Forecasting of Crude Oil Prices Employing Optimized Deep Learning Models and LUBE Method G. Yaswanth et al.
42. Multimodel Approach Using Neural Networks and Symbolic Regression to Combine the Estimated Discharges of Rainfall-Runoff Models P. Phukoetphim et al.
43. Gap-filling of daily streamflow time series using Direct Sampling in various hydroclimatic settings M. Dembélé et al.
44. Study on Hydropower Energy and its Future Changes in the Upper Yangtze River Basin under Climate Change X. Zeng et al.
45. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition W. Wang et al.
46. River Suspended Sediment Prediction Using Various Multilayer Perceptron Neural Network Training Algorithms—A Case Study in Malaysia M. Mustafa et al.
47. A comparison between wavelet based static and dynamic neural network approaches for runoff prediction M. Shoaib et al.
48. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al.
49. Flow forecasts in a non-perennial river of an arid basin using neural networks M. Zemzami
50. Discharge and suspended sediment patterns in a small mountainous watershed with widely distributed rock fragments N. Fang et al.
51. Applicability assessment of the CASCade Two Dimensional SEDiment (CASC2D‐SED) distributed hydrological model for flood forecasting across four typical medium and small watersheds in China L. Chao et al.
52. The need for operational reasoning in data‐driven rating curve prediction of suspended sediment N. Mount et al.
53. Multi-Objective Variance-Bias Tradeoff Optimization of ANN by Hybridizing with Multi-Objective GA for Water Discharge Estimation A. Yadav et al.
54. Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting M. Dehghani et al.
55. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts C. Dawson et al.
56. Dynamic rule curves for flood control of a multipurpose dam C. Chaleeraktrakoon & Y. Chinsomboon
57. An information theoretic approach to select alternate subsets of predictors for data-driven hydrological models R. Taormina et al.
58. ReDF-net: a feature extraction and dynamic fusion framework based on residual networks for runoff forecasting Z. Yang et al.
59. The Annual Maximum Flood Peak Discharge Forecasting Using Hermite Projection Pursuit Regression with SSO and LS Method W. Wang et al.
60. Urban Pluvial Flood Forecasting using Open Data with Machine Learning Techniques in Pattani Basin J. Noymanee et al.
61. ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS R. Taormina & K. Chau
62. Comparison of Artificial Intelligence Techniques for river flow forecasting M. Firat
63. Multilayer perceptron with different training algorithms for streamflow forecasting P. Hosseinzadeh Talaee
64. Forecasting interest rates: a comparative assessment of some second-generation nonlinear models D. Nachane & J. Clavel
65. Evaluation of reservoir sedimentation using data driven techniques V. Garg & V. Jothiprakash
66. An RBF network with a two-step learning algorithm for developing a reservoir inflow forecasting model G. Lin & M. Wu
67. Uncertainty analysis of streamflow drought forecast using artificial neural networks and Monte‐Carlo simulation M. Dehghani et al.
68. Monthly river flow forecasting by an adaptive neuro-fuzzy inference system M. Firat & M. Turan
69. Modelling nitrogen composition in streams on the Boreal Plain using genetic adaptive general regression neural networks X. Li et al.
70. Stream flow forecasting by artificial neural network (ANN) model trained by real coded genetic algorithm (GA) S. A et al.
71. Incorporating Forecasts of Rainfall in Two Hydrologic Models Used for Medium-Range Streamflow Forecasting J. Bravo et al.
72. Multi-criteria validation of artificial neural network rainfall-runoff modeling R. Modarres
73. Neural network model for discharge and water-level prediction for Ramganga River catchment of Ganga Basin, India M. Khan et al.