Most cited papers in Hydrological Forecasting Using AI
What are the most cited papers in Hydrological Forecasting Using AI?
The most cited paper in Hydrological Forecasting Using AI is "River flow forecasting through conceptual models part I — A discussion of principles" (J.E. Nash, John V. Sutcliffe, 1970), with 24,392 citations. Next come "Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach" (23,031) and "Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting" (6,650).
Showing 50 of 99 papers. Every paper with more than 584 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | River flow forecasting through conceptual models part I — A discussion of principles J.E. Nash, John V. Sutcliffe · Journal of Hydrology | Apr 1970 | 24,392 |
| 2 | Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach Elizabeth R. DeLong, David M. DeLong, Daniel L. Clarke‐Pearson · Biometrics | Sep 1988 | 23,031 |
| 3 | Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting Xingjian Shi, Zhourong Chen, Hao Wang et al. · arXiv (Cornell University) | 13 Jun 2015 | 6,650 |
| 4 | The future of distributed models: Model calibration and uncertainty prediction Keith Beven, Andrew M. Binley · Hydrological Processes | Jul 1992 | 4,690 |
| 5 | SWAT: Model Use, Calibration, and Validation Jeffrey G. Arnold, Daniel N. Moriasi, Philip Walter Gassman et al. · Transactions of the ASABE | 2012 | 3,460 |
| 6 | Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences M.W Gardner, Stephen R. Dorling · Atmospheric Environment | Aug 1998 | 3,390 |
| 7 | Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications Holger Robert Maier, Graeme C. Dandy · Environmental Modelling & Software | 2000 | 2,404 |
| 8 | The influence of autocorrelation on the ability to detect trend in hydrological series Sheng Yue, Paul Pilon, Bob Phinney et al. · Hydrological Processes | 19 Jun 2002 | 2,145 |
| 9 | Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks Frederik Kratzert, Daniel Klotz, Claire Brenner et al. · Hydrology and earth system sciences | 22 Nov 2018 | 1,996 |
| 10 | Scale issues in hydrological modelling: A review Günter Blöschl, Murugesu Sivapalan · Hydrological Processes | Apr 1995 | 1,909 |
| 11 | Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes Jack V. Tu · Journal of Clinical Epidemiology | Nov 1996 | 1,848 |
| 12 | Crop yield prediction using machine learning: A systematic literature review Thomas van Klompenburg, Ayalew Kassahun, Cagatay Catal · Computers and Electronics in Agriculture | 18 Aug 2020 | 1,846 |
| 13 | The thermal regime of rivers: a review Daniel Caissie · Freshwater Biology | 10 Jul 2006 | 1,760 |
| 14 | Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)—a case study Kunwar Pal Singh, Amrita Malik, Dinesh Mohan et al. · Water Research | 14 Sep 2004 | 1,660 |
| 15 | A comparison of random forest variable selection methods for classification prediction modeling Jaime Lynn Speiser, Michael E. Miller, Janet Austin Tooze et al. · Expert Systems with Applications | 23 May 2019 | 1,589 |
| 16 | The Mann-Kendall Test Modified by Effective Sample Size to Detect Trend in Serially Correlated Hydrological Series Sheng Yue, ChunYuan Wang · Water Resources Management | Jun 2004 | 1,513 |
| 17 | Artificial Neural Networks in Hydrology. I: Preliminary Concepts ASCE Task Committee on Application of Artificial Neural Networks in Hydrology · Journal of Hydrologic Engineering | Apr 2000 | 1,485 |
| 18 | Stokesian Dynamics John F. Brady, G. Bossis · Annual Review of Fluid Mechanics | 1988 | 1,463 |
| 19 | Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology James W. Kirchner · Water Resources Research | Mar 2006 | 1,411 |
| 20 | A decade of Predictions in Ungauged Basins (PUB)—a review Markus Hrachowitz et al. · Hydrological Sciences Journal | 16 May 2013 | 1,383 |
| 21 | Flood Prediction Using Machine Learning Models: Literature Review Amir Mosavi, Pinar Ozturk, Kwok-wing Chau · Water | 27 Oct 2018 | 1,362 |
| 22 | Artificial Neural Networks in Hydrology. II: Hydrologic Applications ASCE Task Committee on Application of Artificial Neural Networks in Hydrology · Journal of Hydrologic Engineering | Apr 2000 | 1,354 |
| 23 | A review of water quality index models and their use for assessing surface water quality Md Galal Uddin, Stephen Nash, Agnieszka I. Olbert · Ecological Indicators | 17 Dec 2020 | 1,277 |
| 24 | Review and comparison of methods to study the contribution of variables in artificial neural network models Muriel Gevrey, Ioannis F. Dimopoulos, Sovan Lek · Ecological Modelling | Feb 2003 | 1,265 |
| 25 | Assessment of the surface water quality in Northern Greece Vasil Simeonov, John A. Stratis, Constantini A. Samara et al. · Water Research | 3 Sep 2003 | 1,263 |
| 26 | Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks Julian D. Olden, Donald A. Jackson · Ecological Modelling | Aug 2002 | 1,251 |
| 27 | A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists Chaopeng Shen · Water Resources Research | 30 Aug 2018 | 1,119 |
| 28 | Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Kou-lin Hsu, Xiaogang Gao, Soroosh Sorooshian et al. · Journal of applied meteorology | Sep 1997 | 1,117 |
| 29 | Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning Frederik Kratzert, Daniel Klotz, Mathew Herrnegger et al. · Water Resources Research | 23 Nov 2019 | 1,033 |
| 30 | Dual state–parameter estimation of hydrological models using ensemble Kalman filter Hamid Moradkhani, Soroosh Sorooshian, Hoshin V. Gupta et al. · Advances in Water Resources | 29 Dec 2004 | 936 |
| 31 | Application of Long Short-Term Memory (LSTM) Neural Network for Flood Forecasting Xuan-Hien Le, Hung Viet Ho, Giha Lee et al. · Water | 5 Jul 2019 | 896 |
| 32 | Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework Yuqiong Liu, Hoshin V. Gupta · Water Resources Research | Jul 2007 | 896 |
| 33 | Artificial neural networks as a tool in ecological modelling, an introduction Sovan Lek, Jean‐François Guégan · Ecological Modelling | Aug 1999 | 895 |
| 34 | Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions Holger Robert Maier, Ashu Jain, Graeme C. Dandy et al. · Environmental Modelling & Software | 23 Mar 2010 | 890 |
| 35 | Hydrological modelling using artificial neural networks Christian W. Dawson, Robert Leonard Wilby · Progress in Physical Geography Earth and Environment | Mar 2001 | 873 |
| 36 | What Role Does Hydrological Science Play in the Age of Machine Learning? Grey Nearing, Frederik Kratzert, Alden Keefe Sampson et al. · Water Resources Research | 13 Nov 2020 | 870 |
| 37 | Mathematical Modeling of Watershed Hydrology Vijay P. Singh, David A. Woolhiser · Journal of Hydrologic Engineering | Jul 2002 | 853 |
| 38 | Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology Jeffrey J. McDonnell, Murugesu Sivapalan, Kellie B. Vaché et al. · Water Resources Research | Jul 2007 | 851 |
| 39 | Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir Mohammad Valipour, Mohammad Ebrahim Banihabib, Seyyed Mahmood Reza Behbahani · Journal of Hydrology | 19 Nov 2012 | 850 |
| 40 | Cross-Validation in Statistical Climate Forecast Models Joel C. Michaelsen · Journal of Climate and Applied Meteorology | Nov 1987 | 841 |
| 41 | A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series Wenchuan Wang, Kwok‐wing Chau, Chuntian Cheng et al. · Journal of Hydrology | 22 Jun 2009 | 837 |
| 42 | Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas Jianfeng Zhang, Yan Peng Zhu, Xiaoping Zhang et al. · Journal of Hydrology | 26 Apr 2018 | 835 |
| 43 | Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter Hamid Moradkhani, Kuolin Hsu, Hoshin V. Gupta et al. · Water Resources Research | May 2005 | 825 |
| 44 | Comparing uncertainty analysis techniques for a SWAT application to the Chaohe Basin in China Jing Yang, Peter Reichert, Karim C. Abbaspour et al. · Journal of Hydrology | 22 May 2008 | 814 |
| 45 | Evaluation of the Nash–Sutcliffe Efficiency Index Richard H. McCuen, Zachary Knight, A. Gillian Cutter · Journal of Hydrologic Engineering | Nov 2006 | 811 |
| 46 | Effectiveness of Entropy Weight Method in Decision-Making Zhu Yuxin, Dazuo Tian, Feng Yan · Mathematical Problems in Engineering | 26 Mar 2020 | 802 |
| 47 | A Rainfall‐Runoff Model With LSTM‐Based Sequence‐to‐Sequence Learning Zhongrun Xiang, Jun Yan, İbrahim Demir · Water Resources Research | 2020 | 793 |
| 48 | Support vector machine applications in the field of hydrology: A review Sujay Raghavendra Naganna, Paresh Chandra Deka · Applied Soft Computing | 5 Mar 2014 | 792 |
| 49 | A review of the application of machine learning in water quality evaluation Mengyuan Zhu, Jiawei Wang, Xiao Yang et al. · Eco-Environment & Health | Jun 2022 | 791 |
| 50 | The future of hydrology: An evolving science for a changing world Thorsten Wagener, Murugesu Sivapalan, Peter A. Troch et al. · Water Resources Research | May 2010 | 787 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.