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Hydrological Forecasting Using AI
TopicLeading institutions, researchers & key papers
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.
35
Works
IDs:OpenAlex
How has Hydrological Forecasting Using AI's publication output changed over time?
ScholarIQpublication output · 2007–2023
Output grew200% over the shown period — from 1 works in 2007 to 3 in 2023.
1
1
1
5
3
20072020202120222023
What are the most-cited papers on Hydrological Forecasting Using AI?
ScholarIQmost cited works
Groundwater level prediction using machine learning algorithms in a drought-prone area
Quoc Bao Pham, Manish Kumar, Fabio Di Nunno, Ahmed Elbeltagi, Francesco Granata, Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Xuan Cuong Nguyen, Ali Najah Ahmed, Duong Tran Anh
S147897268. 2022204 Citations
An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation
Ali El Bilali, Taleb Abdeslam, Ayoub Nafii, Houda Lamane, Mohamed Abdellah Ezzaouini, Ahmed Elbeltagi
S44455300. 2022196 Citations
Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models
Saber Kouadri, Chaitanya B. Pande, Balamurugan Panneerselvam, Kanak N. Moharir, Ahmed Elbeltagi
Environmental Science and Pollution Research. 2021181 Citations
Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin
Bachir Sakaa, Ahmed Elbeltagi, Samir Boudibi, Hicham Chaffaï, Abu Reza Md. Towfiqul Islam, Luc Cimusa Kulimushi, Pandurang Choudhari, Azzedine Hani, Youssef Brouziyne, Yong Jie Wong
Environmental Science and Pollution Research. 2022148 Citations
Estimation of water quality index using artificial intelligence approaches and multi-linear regression
Muhammad Sani Gaya, Sani I. Abba, Aliyu Muhammad Abdu, Abubakar Ibrahim Tukur, Mubarak Auwal Saleh, Parvaneh Esmaılı, Norhaliza Abdul Wahab
IAES International Journal of Artificial Intelligence. 202099 CitationsOPEN ACCESS
Where is Hydrological Forecasting Using AI research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
S147897268247
S44455300196
S3483833189
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Hydrological Forecasting Using AI is open access?
ScholarIQopen access share
27%OPEN ACCESS
Gold
18%
Green
0%
Hybrid
9%
Bronze
0%
Closed
73%
Related on ScholarIQ
Performance of machine learning methods in predicting water quality index based on irregular data set: application on Illizi region (Algerian southeast)
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A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data
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Air Temperature Forecasting Using Machine Learning Techniques: A Review
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Variation of water level in Dongting Lake over a 50-year period: Implications for the impacts of anthropogenic and climatic factors
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Groundwater level prediction using machine learning algorithms in a drought-prone area
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An interpretable machine learning approach based on DNN, SVR, Extra Tree, and XGBoost models for predicting daily pan evaporation
Paper