{rfName}
Gr

Indexed in

License and Use

Icono OpenAccess

Citations

3

Altmetrics

Analysis of institutional authors

Alocén, PatriciaCorresponding AuthorFernandez-Centeno, Miguel AAuthorToledo, Miguel AAuthor

Share

May 26, 2024
Publications
>
Article
Sí

Greedy Weighted Stacking of Machine Learning Models for Optimizing Dam Deformation Prediction

Publicated to: Water (WATER-SUI). 16 (9): 1235- - 2024-05-01 16(9), DOI: 10.3390/w16091235

Authors:

Alocén, P; Fernández-Centeno, MA; Toledo, MA
[+]

Affiliations

ACISInnovat Engn S L ACIS2in, Planeta Urano 13,P18 2 A, Parla 28983, Spain - Author
Univ Politecn Madrid UPM, ETS Ingn Caminos Canales & Puertos, Prof Aranguren S-N, Madrid 28040, Spain - Author

Abstract

Dam safety monitoring is critical due to its social, environmental, and economic implications. Although conventional statistical approaches have been used for surveillance, advancements in technology, particularly in Artificial Intelligence (AI) and Machine Learning (ML), offer promising avenues for enhancing predictive capabilities. We investigate the application of ML algorithms, including Boosted Regression Trees (BRT), Random Forest (RF), and Neural Networks (NN), focussing on their combination by Stacking to improve prediction accuracy on concrete dam deformation using radial displacement data from three dams. The methodology involves training first-level models (experts) using those algorithms, and a second-level meta-learner that combines their predictions using BRT, a Linear Model (LM) and the Greedy Weighted Algorithm (GWA). A comparative analysis demonstrates the superiority of Stacking over traditional methods. The GWA emerged as the most suitable meta-learner, enhancing the optimal expert in all cases, with improvement rates reaching up to 16.12% over the optimal expert. Our study addresses critical questions regarding the GWA's expert weighting and its impact on prediction precision. The results indicate that the combination of accurate experts using the GWA improves model reliability by reducing error dispersion. However, variations in optimal weights over time necessitate robust error estimation using cross-validation by blocks. Furthermore, the assignment of weights to experts closely correlates with their precision: the more accurate a model is, the more weight that is assigned to it. The GWA improves on the optimal expert in most cases, including at extreme values of error, with improvement rates up to 41.74%. Our findings suggest that the proposed methodology significantly advances AI applications in infrastructure monitoring, with implications for dam safety.
[+]

Keywords

CombinationCross validationDam constructionDamsDeformation mechanismDisplacementErrorsExpertExpert systemExpertsForecastingForestryGreedy weighted algorithmLearning algorithmsMachine learningMachine-learningMeta-learnerModel validationOptimizationPredictionRadial displacementRadial displacementsSafetySafety engineeringStackingStackingsWeightWeighted algorithmWeights

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Water (WATER-SUI) due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), 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 , thus managing to position itself as a Q1 (Primer Cuartil), in the category Aquatic Science.

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 2026-04-26:

  • Scopus: 2
[+]

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 2026-04-26:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 17.
  • 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: 17 (PlumX).

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

  • The Total Score from Altmetric: 1.
  • The number of mentions on the social network X (formerly Twitter): 1 (Altmetric).

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/90120/

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: 98
  • Downloads: 118
[+]

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 (ALOCÉN HUMANES, PATRICIA) and Last Author (TOLEDO MUNICIO, MIGUEL ANGEL).

the author responsible for correspondence tasks has been ALOCÉN HUMANES, PATRICIA.

[+]

Awards linked to the item

The authors would like to thank Canal de Isabel II (CYII) for providing the data. Additionally, the authors extend their appreciation to Iberdrola and the International Commission on Large Dams (ICOLD) for their contribution in providing data, enriching the scope of this research. The authors would also like to thank Acis2in for their support and involvement.
[+]