Data-driven Hydrologic Forecasting
Data-Driven Hydrologic Forecasting is a hydrologic prediction methodology that, in contrast to physics-based approaches, relies primarily on statistical and machine learning techniques to model watershed behavior directly from observational data without explicit representation of underlying physical processes.
179
Publications
11.7K
Citations
627
Authors
310
Institutions
Publications per year
2017–2026
112
179
A neuro-fuzzy computing technique for modeling hydrological time series
P. C. Nayak, K. P. Sudheer, D. Mohana Rangan et al. · Journal of Hydrology · 2004 · 631 citations
1–5 of 179
627
7
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4
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4
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National Technical University of Athens, Greece
3 publications · 160 citations
3
h-index
3
h-index
1–5 of 627
310

Pennsylvania State University
State College, United States
5 h-index
21 publications
4.1K citations

Hohai University
Nanjing, China
5 h-index
24 publications
2.1K citations
NOAA National Weather Service
Silver Spring, United States
4 h-index
16 publications
1.8K citations
National University of Singapore
Singapore, Singapore
4 h-index
11 publications
551 citations
China Institute of Water Resources and Hydropower Research
Beijing, China
4 h-index
15 publications
518 citations

Beijing Normal University
Beijing, China
4 h-index
11 publications
268 citations
National Technical University of Athens
Athens, Greece
3 h-index
8 publications
348 citations
State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering
Nanjing, China
3 h-index
6 publications
618 citations
1–8 of 310
25 publications · 2.9K citations
22
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9 publications · 268 citations
9
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7 publications · 378 citations
7
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6 publications · 330 citations
6
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5 publications · 916 citations
5
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1–5