126 citations as recorded by crossref.

1. Assessment of the impacts of nonstationarity on watershed runoff using artificial neural networks: a case study in Ardebil, Iran K. Javan et al.
2. Flood risk under future climate in data sparse regions: Linking extreme value models and flood generating processes Y. Tramblay et al.
3. Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya Y. Ouma et al.
4. Multi-objective performance comparison of an artificial neural network and a conceptual rainfall—runoff model N. DE VOS & T. RIENTJES
5. Improving event-based rainfall–runoff modeling using a combined artificial neural network–kinematic wave approach L. Chua & T. Wong
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12. Integration of Volterra model with artificial neural networks for rainfall-runoff simulation in forested catchment of northern Iran M. H. Kashani et al.
13. Future climate scenarios and rainfall–runoff modelling in the Upper Gallego catchment (Spain) C. Bürger et al.
14. Investigation of Impact of Vapor Pressure on Hybrid Streamflow Prediction Modeling H. Babacan et al.
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16. 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.
17. Hydrological Response of Tropical Catchments to Climate Change as Modeled by the GR2M Model: A Case Study in Costa Rica M. Mendez et al.
18. A review of models for predicting soil water dynamics during trickle irrigation R. Subbaiah
19. Runoff Estimation by Machine Learning Methods and Application to the Euphrates Basin in Turkey A. Yilmaz & N. Muttil
20. HydroTest: A web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts C. Dawson et al.
21. Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis C. Wu & K. Chau
22. Current awareness
23. Artificial neural networks vis-à-vis MODFLOW in the simulation of groundwater: a review N. Zeydalinejad
24. Investigation of Internal Functioning of the Radial-Basis-Function Neural Network River Flow Forecasting Models D. Fernando & A. Shamseldin
25. Application of Artificial Neural Networks to Rainfall Forecasting in the Geum River Basin, Korea J. Lee et al.
26. Diagnostic evaluation of conceptual rainfall–runoff models using temporal clustering N. de Vos et al.
27. Deep learning convolutional neural network in rainfall–runoff modelling S. Van et al.
28. Genetic Algorithm Driven ANN Model for Runoff Estimation N. Dhamge et al.
29. The Utility of Information Flow in Formulating Discharge Forecast Models: A Case Study From an Arid Snow‐Dominated Catchment C. Tennant et al.
30. Genetic Programming for storm surge forecasting N. Hien et al.
31. Software Reliability Prediction by Adaptive Gated Recurrent Unit‐Based Encoder‐Decoder Model With Ensemble Empirical Mode Decomposition M. Yang et al.
32. Predicting monthly streamflow using data‐driven models coupled with data‐preprocessing techniques C. Wu et al.
33. Consequences of Continuous Zero Values and Constant Values in Time Series Modeling: Understanding through Chaotic Approach T. Fathima & V. Jothiprakash
34. Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network based rainfall–runoff modeling V. Nourani et al.
35. Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model G. Kan et al.
36. Soft computing for estimating concrete strength in marine settings using water and environmental data B. Siddiq et al.
37. Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model R. Adnan et al.
38. Comparing the Selected Transfer Functions and Local Optimization Methods for Neural Network Flood Runoff Forecast P. Maca et al.
39. Discussion on the paper “Success and challenges in predicting TBM penetration rate using recurrent neural networks” by Feng Shan, Xuzhen He, Danial Jahed Armaghani, Pin Zhang, Daichao Sheng G. Erharter & T. Marcher
40. Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation M. Hasanpour Kashani et al.
41. Mitigation of the time-lag issue in LSTM-based significant wave height prediction using long-term (1984–2023) ERA5 dataset B. D'Costa & S. Mandal
42. Removing prediction lag in wave height forecasting using Neuro - Wavelet modeling technique P. Dixit et al.
43. Integrated Reservoir-Based Canal Irrigation Model. I: Description A. Bhadra et al.
44. Wave Forecasting Using Neuro Wavelet Technique P. Dixit et al.
45. Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm S. Samantaray et al.
46. Short-term rainfall prediction using ANN and MT techniques T. Mandal & V. Jothiprakash
47. A Pareto-optimal moving average-multigene genetic programming model for rainfall-runoff modelling A. Danandeh Mehr & V. Nourani
48. Effect of lithological variations on the performance of artificial intelligence techniques for estimating total organic carbon through well logs K. Maroufi & I. Zahmatkesh
49. The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models M. Seyam & F. Othman
50. Current awareness
51. Evaluation of rainfall and discharge inputs used by Adaptive Network-based Fuzzy Inference Systems (ANFIS) in rainfall–runoff modeling A. Talei et al.
52. Flow forecasts in a non-perennial river of an arid basin using neural networks M. Zemzami
53. A dynamic artificial neural network for tomato yield prediction R. Salazar et al.
54. Simulating water lateral inflow and its contribution to spatial variations of rainfed wheat yields T. Tenreiro et al.
55. Wave-Former: Lag removing univariate long time series forecasting transformer for ocean waves S. Dixit & P. Dixit
56. Successive-station monthly streamflow prediction using neuro-wavelet technique A. Danandeh Mehr et al.
57. Uncovering the underlying drivers of West African monsoon variability: towards the development of efficient rainfall monitoring systems N. Arreyndip & Z. Mouliom
58. Daily Stream Flow Prediction Capability of Artificial Neural Networks as influenced by Minimum Air Temperature Data M. Nayebi et al.
59. Linear genetic programming application for successive-station monthly streamflow prediction A. Danandeh Mehr et al.
60. Improvement of artificial neural networks to predict daily streamflow in a semi-arid area M. Zemzami & L. Benaabidate
61. Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data A. Sohail et al.
62. 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.
63. Echo state networks as an alternative to traditional artificial neural networks in rainfall–runoff modelling N. de Vos
64. Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia A. El-Shafie et al.
65. On the pointlessness of machine learning based time delayed prediction of TBM operational data G. Erharter & T. Marcher
66. Long‐term streamflow forecasting using artificial neural network based on preprocessing technique F. Li et al.
67. Multiobjective training of artificial neural networks for rainfall‐runoff modeling N. de Vos & T. Rientjes
68. Comparing three models to estimate transpiration of desert shrubs S. Xu et al.
69. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine H. Ebrahimi & T. Rajaee
70. Comparison of performance of twelve monthly water balance models in different climatic catchments of China P. Bai et al.
71. A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction A. Danandeh Mehr & E. Kahya
72. Using artificial neural network approach for modelling rainfall–runoff due to typhoon S. CHEN et al.
73. Overcoming time delays challenges: Decomposition–Reconstruction approaches for time-series TBM performance predictions C. Li et al.
74. Daily streamflow forecasting using a wavelet transform and artificial neural network hybrid models C. Guimarães Santos & G. Silva
75. Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition W. Wang et al.
76. A hybrid EMD-SVR model for the short-term prediction of significant wave height W. Duan et al.
77. A comparison of ANN and HSPF models for runoff simulation in Gharehsoo River watershed, Iran K. Javan et al.
78. Downscaling rainfall using deep learning long short‐term memory and feedforward neural network D. Tran Anh et al.
79. Combined Wavelet Transform With Long Short-Term Memory Neural Network for Water Table Depth Prediction in Baoding City, North China Plain Z. Liang et al.
80. A deterministic linearized recurrent neural network for recognizing the transition of rainfall–runoff processes T. Pan et al.
81. Successive-station monthly streamflow prediction using different artificial neural network algorithms A. Danandeh Mehr et al.
82. Estimation of the water level variations in the 2022 Tonga tsunami event based on multiple machine learning models D. Tang & H. Liu
83. River flow time series using least squares support vector machines R. Samsudin et al.
84. Review of Applications of Neuro-Wavelet Techniques in Water Flows P. Dixit et al.
85. Urmia lake water level fluctuation hydro informatics modeling using support vector machine and conjunction of wavelet and neural network M. Noury et al.
86. Prediction of rainfall time series using modular soft computingmethods C. Wu & K. Chau
87. Optimising training data for ANNs with Genetic Algorithms R. Kamp & H. Savenije
88. Assessment of the impact of climate change on current and future flows of the ungauged Aga-Foua-Djilas watershed: a comparative study of hydrological models CWatM under ISIMIP and HMF-WA P. Dione et al.
89. Modeling Fluid Flow in Ship Systems for Controller Tuning Using an Artificial Neural Network N. Assani et al.
90. Artificial Neural Networks–Based Model Parameter Transfer in Streamflow Simulation of Brazilian Atlantic Rainforest Watersheds R. Vilanova et al.
91. Machine learning models to predict sewer concrete strength exposed to sulfide environments: unveiling the superiority of Bayesian-optimized prediction models B. Siddiq et al.
92. Projecting Impacts of Climate Change on Water Availability Using Artificial Neural Network Techniques E. Swain et al.
93. Timing error correction procedure applied to neural network rainfall—runoff modelling R. ABRAHART et al.
94. A Hybrid Model for Runoff Prediction Using Variational Mode Decomposition and Artificial Neural Network . Muhammad Sibtain et al.
95. Hydrological model coupling with ANNs R. Kamp & H. Savenije
96. Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm H. Ouyang
97. Rainfall-Runoff Modeling of Sutlej River Basin (India) Using Soft Computing Techniques A. Hussain et al.
98. Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data V. Jothiprakash & R. Magar
99. Improved Rainfall Prediction Using Combined Pre-Processing Methods and Feed-Forward Neural Networks D. Tran Anh et al.
100. Typhoon flood forecasting using integrated two-stage Support Vector Machine approach G. Lin et al.
101. Modeling saltwater intrusion using an integrated Bayesian model averaging method in the Pearl River Delta K. Lin et al.
102. Comparative Analysis of Training Methods and Different Data for the Rainfall-Runoff Predication Using Artificial Neural Networks K. Solaimani & Z. Darvari
103. Forecasting stream flow using hybrid neuro-wavelet technique S. Londhe & S. Narkhede
104. Phase-space reconstruction and self-exciting threshold modeling approach to forecast lake water levels H. Tongal & R. Berndtsson
105. Applying Cascade-Correlation Neural Networks to In-Fill Gaps in Mediterranean Daily Flow Data Series C. Vega-Garcia et al.
106. A Neural Network Approach to Smarter Sensor Networks for Water Quality Monitoring E. O’Connor et al.
107. Hydrological response of tropical rivers basins to climate change using the GR2M model: the case of the Casamance and Kayanga-Géva rivers basins C. Sadio et al.
108. A Study of Rainfall Forecasting Models Based on Artificial Neural Network K. Solaimani
109. Improvement of sap flow estimation by including phenological index and time-lag effect in back-propagation neural network models J. Tu et al.
110. Monthly Rainfall Anomalies Forecasting for Southwestern Colombia Using Artificial Neural Networks Approaches T. Canchala et al.
111. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques C. Wu et al.
112. Methods to improve neural network performance in daily flows prediction C. Wu et al.
113. Symbiotic adaptive neuro-evolution applied to rainfall–runoff modelling in northern England C. Dawson et al.
114. Rainfall to runoff: a comprehensive review of AI and traditional models for inflow forecasting in semi-arid basin S. Agarwal et al.
115. Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy E. Snieder et al.
116. Data-driven models for monthly streamflow time series prediction C. Wu & K. Chau
117. Rainfall-runoff modeling through regression in the reproducing kernel Hilbert space algorithm M. Safari et al.
118. A generic data-driven technique for forecasting of reservoir inflow: Application for hydropower maximization S. Ahmad & F. Hossain
119. A systematic quantitative review on the performance of some of the recent short-term rainfall forecasting techniques S. Ashok & S. Pekkat
120. A robust artificial intelligence informed over complete rational dilation wavelet transform technique coupled with deep learning for long-term rainfall prediction M. Diykh et al.
121. Behavioural analysis of a time series–A chaotic approach T. FATHIMA & V. JOTHIPRAKASH
122. Including spatial distribution in a data-driven rainfall-runoff model to improve reservoir inflow forecasting in Taiwan M. Tsai et al.
123. Cascaded-ANFIS to simulate nonlinear rainfall–runoff relationship N. Rathnayake et al.
124. Runoff forecasting for an asphalt plane by Artificial Neural Networks and comparisons with kinematic wave and autoregressive moving average models L. Chua & T. Wong
125. A Comparison of ANN and HSPF Models for Runoff Simulation in Balkhichai River Watershed, Iran F. Amirhossien et al.
126. Propagation of CMORPH rainfall errors to REW streamflow simulation mismatch in the upper Zambezi Basin W. Gumindoga et al.