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Analysis of institutional authors

Vakaruk, StanislavAuthorKaramchandani, AmitAuthorMozo, AlbertoCorresponding AuthorGomez-Canaval, SandraAuthor

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September 1, 2024
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Deep learning methods for multi-horizon long-term forecasting of Harmful Algal Blooms

Publicated to: Knowledge-Based Systems. 301 112279- - 2024-10-09 301(), DOI: 10.1016/j.knosys.2024.112279

Authors:

Martin-Suazo, S; Moron-Lopez, J; Vakaruk, S; Karamchandani, A; Aguilar, JAP; Mozo, A; Gomez-Canaval, S; Vinyals, M; Ortiz, JM
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Affiliations

Polish Acad Sci, European Reg Ctr Ecohydrol, 3 Tylna, PL-90364 Lodz, Poland - Author
Univ Alcala, IMDEA Water Inst, Ave Punto Com 2,Parque Cientif Tecnol, Alcala De Henares 28805, Spain - Author
Univ Fed Toulouse, INRAE, MIAT, 24 Chem Borde Rouge, F-31326 Castanet Tolosan, France - Author
Univ Politecn Madrid, ETS Ingn Sistemas Informat, Calle Alan Turing S-N, Madrid 28031, Spain - Author
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Abstract

The increasing occurrence of Harmful Algal Blooms (HABs) in water systems poses significant challenges to ecological health, public safety, and economic stability globally. Deep Learning (DL) models, notably Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM), have been widely employed for HAB prediction. However, the emergence of state-of-the-art multi-horizon forecasting DL architectures such as Neural Basis Expansion Analysis for Interpretable Time Series Forecasting (N-BEATS) provides a novel solution for long-term HAB prediction. This study compares the performance of N-BEATS with LSTM and CNN models using high temporal granularity water quality data from As Conchas reservoir (NW Spain) to forecast chlorophyll-a (Chl-a) concentrations, a key indicator of HABs. The evaluation encompasses one-day and one-week prediction horizons, aligning with World Health Organization (WHO) HAB alert criteria. Results indicate that N-BEATS outperforms LSTM and CNN models for one-week predictions and when forecasting multiple consecutive days within a week. Furthermore, augmenting input data with additional variables does not significantly enhance predictive accuracy, challenging the assumption that complexity always improves model performance. The study also explores the transferability of trained models across different monitoring buoys within the same water body, emphasizing the adaptability and broad applicability of predictive models in diverse aquatic environments. This research underscores the potential of N-BEATS as a valuable tool for HAB prediction, particularly for longer-term forecasting.
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Keywords

AlgaeAlgae bloomsAlgae controlChlorophyllConvolutional neural networkDeep learningForecastinForecastingHarmful alga bloomHarmful algaeHarmful algae bloomHarmful algal bloomsLakeLearning systemsLong short-term memoryLong-term forecastingMechanisModelMonitoringNeural network modelNeural-networkReservoirs (water)Time seriesTime series analysisTime series forecastingTime-seriesTimes seriesWater quality

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Knowledge-Based Systems 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 26/204, thus managing to position itself as a Q1 (Primer Cuartil), in the category Computer Science, Artificial Intelligence.

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 2025-12-20:

  • WoS: 1
  • Scopus: 4
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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 2025-12-20:

  • 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: 50 (PlumX).

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/84623/

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: 138
  • Downloads: 43
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: France; Poland.

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 (Martin-Suazo, Silvia) .

the author responsible for correspondence tasks has been MOZO VELASCO, BONIFACIO ALBERTO.

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