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Topic · Environmental Engineering

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. 10.1% 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%
1.9k
per year above
Top-10% rate
10.1%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+73%
the tick is no change
Largest producer
China
36% of the 10 largest, 2022-now
Hydrological Forecasting Using AI

Which countries lead Hydrological Forecasting Using AI research?

By volume, China and India publish the most (1.9k and 1.4k works in 2022-now).

By volume, 2022-now

  1. 1 China 1.9k works
  2. 2 India 1.4k works
  3. 3 United States 612 works
  4. 4 Iran 317 works
  5. 5 Türkiye 231 works
  6. 6 Indonesia 215 works
  7. 7 South Korea 183 works
  8. 8 Malaysia 155 works
  9. 9 Canada 144 works
  10. 10 United Kingdom 129 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.

China: 36.4%India: 26.6%United States: 11.4%Iran: 5.9%6 others listed: 19.7%36%largest
China1,949 · 36.4%India1,423 · 26.6%United States612 · 11.4%Iran317 · 5.9%6 others listed1,056 · 19.7%

Shares of the rows listed above, not of the whole node.

Which institutions lead Hydrological Forecasting Using AI research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Hydrological Forecasting Using AI research, followed by Hohai University and North China University of Water Resources and Electric Power.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 96 works
  2. 2 Hohai UniversityChina 88 works
  3. 3 North China University of Water Resources and Electric PowerChina 48 works
  4. 4 Ministry of Water Resources of the People's Republic of ChinaChina 48 works
  5. 5 Islamic Azad University, TehranIran 44 works
  6. 6 Saveetha UniversityIndia 41 works
  7. 7 SRM Institute of Science and TechnologyIndia 35 works
  8. 8 China Institute of Water Resources and Hydropower ResearchChina 34 works
  9. 9 Wuhan UniversityChina 32 works
  10. 10 Indian Council of Agricultural ResearchIndia 30 works

Who are the leading researchers in Hydrological Forecasting Using AI?

The most-cited researchers publishing on Hydrological Forecasting Using AI include Yoshua Bengio, Martin McKee and Kai You Wang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Martin McKee United Kingdom 270k citations
  3. 3 Kai You Wang China 224k citations
  4. 4 Guangming Zeng China 204k citations
  5. 5 Kalyanmoy Deb United States 171k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

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).

Most cited papers
#PaperReleasedCitations
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) PreprintOpen access 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 Open access 2012 3,460
All 99 most cited papers, with filters and sorting

Where is Hydrological Forecasting Using AI research done?

The largest centres of Hydrological Forecasting Using AI research in 2022-now 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-now

  1. 1 BeijingChina 379 works
  2. 2 NanjingChina 189 works
  3. 3 ChennaiIndia 134 works
  4. 4 WuhanChina 123 works
  5. 5 ZhengzhouChina 99 works
  6. 6 TehranIran 98 works
  7. 7 GuangzhouChina 81 works
  8. 8 New DelhiIndia 78 works
  9. 9 ShanghaiChina 77 works
  10. 10 DhakaBangladesh 68 works

Where it is the local speciality

  1. RoorkeeIN · 36.1 works9.9×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Hydrological Forecasting Using AI than the world average.

See Hydrological Forecasting Using AI on the map

Where is the best place to study Hydrological Forecasting Using AI?

Among universities, judged by research, Hohai University, North China University of Water Resources and Electric Power and Shanghai Ocean University 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.

0%20%40%mean 22.02%fractional works in this field (log) →share of work in the world top 10% →Sultan Qaboos Univer…Shanghai Ocean Unive…North China Universi…Hohai University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Hohai UniversityChina 64.111.0%22.3×88 +33.0%
2North China University of Water Resources and Electric PowerChina 62.917.3%47.6×48 +377.3%
3Shanghai Ocean UniversityChina 61.830.9%8.5×13 +1010.6%
4Sultan Qaboos UniversityOman 61.335.6%7.0×9 -
5King Fahd University of Petroleum and MineralsSaudi Arabia 60.524.4%6.4×15 +531.7%
6Hong Kong Polytechnic UniversityChina 59.626.0%1.5×9 +79.4%
7China University of GeosciencesChina 57.423.3%5.3×16 +194.6%
8United Arab Emirates UniversityUnited Arab Emirates 54.220.9%5.9×9 +155.6%
9Erciyes UniversityTürkiye 53.215.6%7.3×10 +195.4%
10Indian Institute of Technology RoorkeeIndia 52.215.2%7.6×23 +67.8%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Hydrological Forecasting Using AI, country by country

Is Hydrological Forecasting Using AI research growing?

Output in 2022-2025 was 73% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Hydrological Forecasting Using AI.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Hydrological Forecasting Using AI as a percentage of every work published that year, 0.031% in 2024.

still counting0.020%0.040%Hydrological Forecasting Using AI0.037% in 202519801990200020102020

Fractional works of Hydrological Forecasting Using AI over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Hydrological Forecasting Using AI10.1% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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