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Pérez-Aracil JAuthorCasanova-Mateo CAuthor

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April 17, 2023
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Extreme Low-Visibility Events Prediction Based on Inductive and Evolutionary Decision Rules: An Explicability-Based Approach

Publicated to: Atmosphere. 14 (3): 542- - 2023-03-01 14(3), DOI: 10.3390/atmos14030542

Authors:

Peláez-Rodríguez, C; Marina, CM; Pérez-Aracil, J; Casanova-Mateo, C; Salcedo-Sanz, S
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Affiliations

Univ Alcala, Dept Signal Proc & Commun, Alcala De Henares 28805, Spain - Author
Univ Politecn Madrid, Dept Comp Syst Engn, Campus Sur, Madrid 28031, Spain - Author
Universidad de Alcalá - Author
Universidad Politécnica de Madrid - Author
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Abstract

In this paper, we propose different explicable forecasting approaches, based on inductive and evolutionary decision rules, for extreme low-visibility events prediction. Explicability of the processes given by the rules is in the core of the proposal. We propose two different methodologies: first, we apply the PRIM algorithm and evolution to obtain induced and evolved rules, and subsequently these rules and boxes of rules are used as a possible simpler alternative to ML/DL classifiers. Second, we propose to integrate the information provided by the induced/evolved rules in the ML/DL techniques, as extra inputs, in order to enrich the complex ML/DL models. Experiments in the prediction of extreme low-visibility events in Northern Spain due to orographic fog show the good performance of the proposed approaches.
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Keywords

deep learning techniquesexplicable artificial intelligence (xai)extreme low-visibility eventsmodeloptimizationprim decision rulesradiation fogrule evolutionDeep learning techniquesExplicable artificial intelligence (xai)Extreme low-visibility eventsFogNeural-networksPrim decision rulesRule evolution

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Atmosphere 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, 2023, it was in position , thus managing to position itself as a Q2 (Segundo Cuartil), in the category Environmental Science (Miscellaneous). Notably, the journal is positioned en el Cuartil Q3 for the agency WoS (JCR) in the category Meteorology & Atmospheric Sciences.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 1.67. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.18 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-27, the following number of citations:

  • WoS: 11
  • Scopus: 12
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Impact and social visibility

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/81851/

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: 197
  • Downloads: 77
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