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Analysis of institutional authors

Mortazavizadeh, FatemehsadatAuthorBolonio, DavidAuthor

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April 12, 2025
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Advances in machine learning for agricultural water management: a review of techniques and applications

Publicated to:Journal Of Hydroinformatics. 27 (3): 474-492 - 2025-03-01 27(3), DOI: 10.2166/hydro.2025.258

Authors: Mortazavizadeh, Fatemehsadat; Bolonio, David; Mirzaei, Majid; Ng, Jing Lin; Mortazavizadeh, Seyed Vahid; Dehghani, Amin; Mortezavi, Saber; Ghadirzadeh, Hossein

Affiliations

Islamic Azad Univ, Dept Comp Engn, Maybod Branch, Maybod, Iran - Author
Kharazmi Univ, Dept Elect & Comp Engn, Tehran, Iran - Author
Univ Maryland, Dept Environm Sci & Technol, College Pk, MD 20742 USA - Author
Univ Politecn Madrid, Dept Energy & Fuels, ETS Ingn Minas & Energia, Rios Rosas 21, Madrid 28003, Spain - Author
Univ Tehran, Coll Engn, Sch Environm, Tehran, Iran - Author
Univ Teknol MARA, Coll Engn, Sch Civil Engn, Shah Alam 40450, Malaysia - Author
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Abstract

The escalating challenge of water scarcity demands advanced methodologies for sustainable water management, particularly in agriculture. Machine learning (ML) has become a crucial tool in optimizing the hydrological cycle within both natural and engineered environments. This review rigorously assesses various ML algorithms, including neural networks, decision trees, support vector machines, and ensemble methods, for their effectiveness in agricultural water management. By leveraging diverse data sources such as satellite imagery, climatic variables, soil properties, and crop yield data, the study highlights the frequent use and superior predictive accuracy of the Random forest (RF) model. Additionally, artificial neural networks (ANNs) and support vector machines (SVM) show significant efficacy in specialized applications like evapotranspiration estimation and water stress prediction. The integration of ML techniques with real-time data streams enhances the precision of water management strategies. This review underscores the critical role of ML in advancing decision-making through the development of explainable artificial intelligence, which improves model interpretability and fosters trust in automated systems. The findings position ML models as indispensable for real-time, data-driven management of agricultural water resources, contributing to greater resilience and sustainability under the dynamic pressures of global environmental change.

Keywords

Agricultural water managementAlgorithmArtificial-intelligenceBig dataDecision-makingMachine learningSustainabilitSustainability

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Journal Of Hydroinformatics due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2025, it was in position 239/358, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Environmental Sciences. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría .

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-13:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 14 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

    • The work has been submitted to a journal whose editorial policy allows open Open Access publication.

    Leadership analysis of institutional authors

    This work has been carried out with international collaboration, specifically with researchers from: Iran; Malaysia; United States of America.

    There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (MORTAZAVIZADEH, FATEMEHSADAT) .