Monthly prediction of streamflow using data-driven models

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Cite
Yaghoubi, Behrouz, et al. “Monthly Prediction of Streamflow Using Data-Driven Models”. Journal of Earth System Science, vol. 128, no. 6, 2019, https://doi.org/10.1007/s12040-019-1170-1.
Yaghoubi, B., Hosseini, S. A., & Nazif, S. (2019). Monthly prediction of streamflow using data-driven models. Journal of Earth System Science, 128(6). https://doi.org/10.1007/s12040-019-1170-1
Yaghoubi, Behrouz, Seyed Abbas Hosseini, and Sara Nazif. “Monthly Prediction of Streamflow Using Data-Driven Models”. Journal of Earth System Science 128, no. 6 (2019). https://doi.org/10.1007/s12040-019-1170-1.
1.
Yaghoubi B, Hosseini SA, Nazif S. Monthly prediction of streamflow using data-driven models. Journal of Earth System Science. 2019;128(6).
Journal Categories
Science
Geology
Science
Science (General)
Refrences
Title Journal Journal Categories Citations Publication Date
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  • Technology: Hydraulic engineering: River, lake, and water-supply engineering (General)
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  • Technology: Environmental technology. Sanitary engineering
  • Science: Biology (General): Ecology
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39 2013
Citations
Title Journal Journal Categories Citations Publication Date
Comparative analysis of data-driven and conceptual streamflow forecasting models with uncertainty assessment in a major basin in Iran International Journal of Energy and Water Resources 2024
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions Engineering Applications of Artificial Intelligence
  • Technology: Mechanical engineering and machinery
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Engineering (General). Civil engineering (General)
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
7 2024
Monthly streamflow prediction and performance comparison of machine learning and deep learning methods Acta Geophysica
  • Science: Geology
  • Science: Geology
  • Science: Geology
2 2023
Hybrid and Integrative Evolutionary Machine Learning in Hydrology: A Systematic Review and Meta-analysis Archives of Computational Methods in Engineering
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Engineering (General). Civil engineering (General)
  • Science: Mathematics
  • Science: Mathematics
  • Technology: Engineering (General). Civil engineering (General)
  • Technology: Engineering (General). Civil engineering (General)
2023
Streamflow prediction based on machine learning models and rainfall estimated by remote sensing in the Brazilian Savanna and Amazon biomes transition Modeling Earth Systems and Environment
  • Geography. Anthropology. Recreation: Environmental sciences
1 2023
Citations Analysis
The category Technology: Engineering (General). Civil engineering (General) 10 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Hydrotechnical structures and was published in 2020. The most recent citation comes from a 2024 study titled Comparative analysis of data-driven and conceptual streamflow forecasting models with uncertainty assessment in a major basin in Iran. This article reached its peak citation in 2023, with 6 citations. It has been cited in 14 different journals, 35% of which are open access. Among related journals, the Engineering Applications of Artificial Intelligence cited this research the most, with 2 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year