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Analysis of institutional authors

Mateo, LfAuthorMás-Lópezpez, MiAuthorGarcía-Del-Toro, EmCorresponding AuthorGarcia-Salgado, SAuthorQuijano, MaAuthor

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April 11, 2024
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Artificial Neural Networks to Predict Electrical Conductivity of Groundwater for Irrigation Management: Case of Campo de Cartagena (Murcia, Spain)

Publicated to: Agronomy-Basel. 14 (3): 524- - 2024-03-01 14(3), DOI: 10.3390/agronomy14030524

Authors:

Mateo, Luis F; Mas-Lopezpez, M Isabel; Garcia-del-Toro, Eva M; Garcia-Salgado, Sara; Quijano, M Angeles
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Affiliations

Univ Politecn Madrid, Dept Ingn Civil Hidraul Energia & Medio Ambiente, ETSI Caminos,Canales & Puertos Edificio Retiro,Alf - Author
Univ Politecn Madrid, Dept Matemat Informat Aplicadas Ingn Civil & Naval, ETSI Caminos,Canales & Puertos Edificio Retiro,Alf - Author

Abstract

Groundwater is a crucial water resource, particularly in regions with intensive agriculture and a semi-arid climate, such as Campo de Cartagena (Murcia, Spain). Groundwater salinity in the area can be attributed to hydrogeological characteristics, irrigation return water, or even marine intrusion and communication between aquifers. The management of these waters is essential to maintain sustainable agriculture in the area. Therefore, two groundwater salinity prediction models were developed, a backpropagation artificial neural network (ANN) model and a multiple linear regression (MLR) model, based on EC (electrical conductivity) data obtained from official information sources. The data used were the bicarbonate, calcium, chloride, magnesium, nitrate, potassium, sodium, and sulphate concentrations, as well as EC, pH, and temperature, of 495 water samples from 38 sampling stations between 2000 and 2023. Variables with the least influence on the model were discarded in a previous statistical analysis. Based on seven evaluation metrics (RMSE, MAE, R2, MPE, MBE, SSE, and AARD), the ANN model showed a sligntly better accuracy in predicting EC compared to the MLR model. As a result, the ANN model, together with crop tolerance to EC, may be an effective tool for groundwater irrigation management in these areas.
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Keywords

Artificial neural networksCampo de cartagena (murcia, spain)Electrical conductivityGroundwaterIrrigation managementSalinitySustainable agriculture

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Agronomy-Basel 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, 2024 there are still no calculated indicators, but in 2023, it was in position 21/129, thus managing to position itself as a Q1 (Primer Cuartil), in the category Agronomy.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-24:

  • WoS: 6
  • Scopus: 6
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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 2026-04-24:

  • 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: 19 (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.
    • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/90115/

    As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

    • Views: 129
    • Downloads: 40
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    Leadership analysis of institutional authors

    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 (MATEO RODRIGUEZ, LUIS FRANCISCO) and Last Author (QUIJANO NIETO, M. ANGELES).

    the author responsible for correspondence tasks has been GARCIA DEL TORO, EVA MARIA.

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