Hydrological Forecasting Using AI
Hydrological Forecasting Using AI is a research topic within Environmental Engineering. Science Explorer counts 18k research works in it since 1951. 19.4% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the application of machine learning methods, such as artificial neural networks, support vector machines, and wavelet analysis, in hydrological modeling and forecasting for water resources management. The papers cover topics including rainfall-runoff modeling, groundwater level forecasting, river flow prediction, and water quality modeling.
- Machine Learning
- Hydrology
- Forecasting
- Artificial Neural Networks
- Water Resources
- Rainfall-Runoff Modeling
- Support Vector Machines
- Wavelet Analysis
- Model Performance
- Groundwater Level Forecasting
- Research works
- 18k fractional, since 1951
- In the world top 10%
- 3.6k per year above
- Top-10% rate
- 19.4% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +92% the tick is no change
Which countries lead Hydrological Forecasting Using AI research?
By volume, China and India publish the most (1.6k and 1.2k works in 2022–2025).
By volume, 2022–2025
- 1 China 1.6k works
- 2 India 1.2k works
- 3 United States 513 works
- 4 Iran 274 works
- 5 Türkiye 194 works
- 6 Indonesia 174 works
- 7 South Korea 150 works
- 8 Malaysia 138 works
- 9 Canada 124 works
- 10 Australia 106 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Hydrological Forecasting Using AI research?
By volume in 2022–2025, Hohai University publishes the most Hydrological Forecasting Using AI research, followed by North China University of Water Resources and Electric Power and Saveetha University.
By volume, 2022–2025
- 1 Hohai University China 58 works
- 2 North China University of Water Resources and Electric Power China 42 works
- 3 Saveetha University India 34 works
- 4 Chinese Academy of Sciences China 29 works
- 5 China Institute of Water Resources and Hydropower Research China 29 works
- 6 Wuhan University China 28 works
- 7 University of Tabriz Iran 26 works
- 8 Vellore Institute of Technology University India 23 works
- 9 SRM Institute of Science and Technology India 23 works
- 10 Nanjing University of Information Science and Technology China 22 works
Who are the leading researchers in Hydrological Forecasting Using AI?
The most-cited researchers publishing on Hydrological Forecasting Using AI include Vipin Kumar, Soroosh Sorooshian and Yu Zheng.
- 1 Vipin Kumar 3.9k citations
- 2 Soroosh Sorooshian 2.7k citations
- 3 Yu Zheng 2.5k citations
- 4 Eric F. Wood 2.4k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Hydrological Forecasting Using AI research done?
The largest centres of Hydrological Forecasting Using AI research in 2022–2025 are Beijing (China), Nanjing (China), Chennai (India) and Wuhan (China). Among places with at least 20 works in it, it is an unusually large share of all research in Roorkee.
Largest cities, 2022–2025
Where it is the local speciality
- RoorkeeIN · 31.4 works10×
Location quotient: how much more of its research is in Hydrological Forecasting Using AI than the world average.
Where is the best place to study Hydrological Forecasting Using AI?
Among universities, judged by research, Universiti Tenaga Nasional, National Institute Of Technology Silchar and University of Tabriz score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Universiti Tenaga Nasional Malaysia | 75.6 | 39.8% | 19.1× | 10 | +287.7% |
| 2 | National Institute Of Technology Silchar India | 74.2 | 45.6% | 12.9× | 14 | +546.9% |
| 3 | University of Tabriz Iran | 67.6 | 21.4% | 17.4× | 26 | +154.0% |
| 4 | North China University of Water Resources and Electric Power China | 63.8 | 28.8% | 44.5× | 42 | +4.1% |
| 5 | Hohai University China | 62.8 | 20.0% | 16.8× | 58 | -4.9% |
| 6 | Shahid Chamran University of Ahvaz Iran | 61.8 | 26.3% | 18.2× | 11 | +279.1% |
| 7 | King Fahd University of Petroleum and Minerals Saudi Arabia | 59.3 | 48.7% | 5.2× | 11 | — |
| 8 | National Institute of Technology Karnataka India | 58.2 | 22.6% | 9.9× | 11 | +298.8% |
| 9 | Nanjing University of Information Science and Technology China | 57.9 | 18.2% | 8.7× | 22 | +138.7% |
| 10 | Near East University Cyprus | 56.5 | 22.5% | 12.2× | 10 | — |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Hydrological Forecasting Using AI research growing?
Output in 2018–2022 was 92% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Hydrological Forecasting Using AI.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.