66 citations as recorded by crossref.

1. Exploring the spatiotemporal variability of the snow water equivalent in a small boreal forest catchment through observation and modelling A. Parajuli et al.
2. An evaluation of various data pre-processing techniques with machine learning models for water level prediction E. Tiu et al.
3. A neural network experiment on the simulation of daily nitrate-nitrogen and suspended sediment fluxes from a small agricultural catchment F. Anctil et al.
4. Added gains of soil moisture content observations for streamflow predictions using neural networks F. Anctil et al.
5. Optimizing signal decomposition techniques in artificial neural network-based rainfall-runoff model F. Karami & A. Dariane
6. An experiment on the evolution of an ensemble of neural networks for streamflow forecasting M. Boucher et al.
7. Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization Y. Tikhamarine et al.
8. Evaluating influences of seasonal variations and anthropogenic activities on alluvial groundwater hydrochemistry using ensemble learning approaches K. Singh et al.
9. Future climate scenarios and rainfall–runoff modelling in the Upper Gallego catchment (Spain) C. Bürger et al.
10. How to realize the knowledge reuse and sharing from accident reports? A knowledge-driven modeling method combining ontology and deep learning N. Xue et al.
11. Site-specific early season potato yield forecast by neural network in Eastern Canada J. Fortin et al.
12. Application of artificial neural network ensembles in probabilistic hydrological forecasting S. Araghinejad et al.
13. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al.
14. Sediment flux sensitivity to climate change: A case study in the Longchuanjiang catchment of the upper Yangtze River, China Y. Zhu et al.
15. Comparison of Multiple-Layer Perceptrons and Least Squares Support Vector Machines for Remote-Sensed Characterization of In-Field LAI Patterns – A Case Study with Potato J. Fortin et al.
16. Comparison of empirical daily surface incoming solar radiation models J. Fortin et al.
17. A neural network experiment on the site-specific simulation of potato tuber growth in Eastern Canada J. Fortin et al.
18. Current awareness
19. Data-driven modelling approaches for socio-hydrology: opportunities and challenges within the Panta Rhei Science Plan N. Mount et al.
20. Investigation internal parameters of neural network model for Flood Forecasting at Upper river Ping, Chiang Mai T. Chaipimonplin
21. Comparison of cokriging and adaptive neuro-fuzzy inference system models for suspended sediment load forecasting M. Vafakhah
22. Improved Neural Network Model and Its Application in Hydrological Simulation Z. Li et al.
23. ANN-Based LUBE Model for Interval Prediction of Compressive Strength of Concrete M. Akbari et al.
24. Optimising training data for ANNs with Genetic Algorithms R. Kamp & H. Savenije
25. A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms E. Sharma et al.
26. River network-based regionalization for advancing flood quantile estimation in arid and semi-arid regions of the USA K. Jung & D. Park
27. A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models E. Snieder et al.
28. Comparative Analysis of Training Methods and Different Data for the Rainfall-Runoff Predication Using Artificial Neural Networks K. Solaimani & Z. Darvari
29. Using bias correction and ensemble modelling for predictive mapping and related uncertainty: A case study in digital soil mapping J. Sylvain et al.
30. Finding the Optimal Multimodel Averaging Method for Global Hydrological Simulations W. Qi et al.
31. Assessment of non-linear models based on regional frequency analysis for estimation of hydrological quantiles at ungauged sites in South Korea K. Jung et al.
32. Suspended sediment flux modeling with artificial neural network: An example of the Longchuanjiang River in the Upper Yangtze Catchment, China Y. Zhu et al.
33. Comparison of physically based and empirical models to estimate corn (Zea maysL) LAI from multispectral data in eastern Canada J. Fortin et al.
34. A stacking ensemble learning framework for annual river ice breakup dates W. Sun & B. Trevor
35. A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning S. Chen et al.
36. Advancing monthly streamflow prediction accuracy of CART models using ensemble learning paradigms H. Erdal & O. Karakurt
37. Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks L. Hassan-Esfahani et al.
38. Streamflow forecasting using functional regression P. Masselot et al.
39. Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches N. Basant et al.
40. Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy E. Snieder et al.
41. Water-Level Prediction Analysis for the Three Gorges Reservoir Area Based on a Hybrid Model of LSTM and Its Variants H. Li et al.
42. The Effect of Splitting of Raw Data into Training and Test Subsets on the Accuracy of Predicting Spatial Distribution by a Multilayer Perceptron E. Baglaeva et al.
43. Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space C. Shu & T. Ouarda
44. A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome G. Napolitano et al.
45. Validation of an ANN Flow Prediction Model Using a Multistation Cluster Analysis M. Demirel et al.
46. Improving daily streamflow forecasts in mountainous Upper Euphrates basin by multi-layer perceptron model with satellite snow products G. Uysal et al.
47. A novel ensemble algorithm based on hydrological event diversity for urban rainfall–runoff model calibration and validation E. Snieder & U. Khan
48. Using complementary methods for improved flow forecasting D. LEKKAS
49. A Study of Rainfall Forecasting Models Based on Artificial Neural Network K. Solaimani
50. Flood Prediction using Deep Spiking Neural Network R. Tei & A. Saleh
51. Estimation of fish assessment index based on ensemble artificial neural network for aquatic ecosystem in South Korea H. Kang et al.
52. Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting R. Abrahart et al.
53. Artificial neural network-assisted glacier forefield soil temperature retrieval from temperature measurements M. Singhal et al.
54. Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1 A. Parent et al.
55. A diversity-centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting E. Snieder & U. Khan
56. Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting H. Yonaba et al.
57. Impact of EMD decomposition and random initialisation of weights in ANN hindcasting of daily stream flow series: An empirical examination G. Napolitano et al.
58. Toward Systematic Literature Reviews in Hydrological Sciences D. De León Pérez et al.
59. Ensemble‐Based Neural Network Modeling for Hydrologic Forecasts: Addressing Uncertainty in the Model Structure and Input Variable Selection T. Kim et al.
60. Daily Stream Flow Prediction Capability of Artificial Neural Networks as influenced by Minimum Air Temperature Data M. Nayebi et al.
61. River ice breakup timing prediction through stacking multi-type model trees W. Sun
62. Using text mining to establish knowledge graph from accident/incident reports in risk assessment C. Liu & S. Yang
63. 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.
64. Combining two-stage decomposition based machine learning methods for annual runoff forecasting S. Chen et al.
65. Data splitting for artificial neural networks using SOM-based stratified sampling R. May et al.
66. How Lucrative & Challenging the Boundary less Opportunities for Data Scientists? S. Kumar & P. Aithal