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

Peláez-Rodríguez, C.; Marina, Cosmin M.; Pérez-Aracil, Jorge; Casanova-Mateo, C.; Salcedo‐Sanz, Sancho

doi:10.3390/atmos14030542

Cited by 9 publications

(3 citation statements)

References 62 publications

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“…Although AI models can provide accurate predictions, they often lack interpretability, making it challenging to identify the critical variables driving the occurrence of fog. Peláez-Rodríguez et al [22] propose different explainable forecasting approaches, based on inductive and evolutionary decision rules, for extreme low-visibility event predictions. The explainability of the processes derived from the rules generated by their system is one of the core objective of this work.…”

Section: Machine Learningmentioning

confidence: 99%

Fog Decision Support Systems: A Review of the Current Perspectives

Bari,

Bergot,

Tardif

2023

Atmosphere

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Section: Machine Learningmentioning

confidence: 99%

Fog Decision Support Systems: A Review of the Current Perspectives

Bari,

Bergot,

Tardif

2023

Atmosphere

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“…They found that the ensemble models and meteorological-based methods, which combined multiple deep learning architectures, achieved a better forecasting accuracy than the individual deep learning models. For the forecasting of lowvisibility conditions, Peláez-Rodríguez et al [21] proposed an iterative forward selection algorithm based on evolutionary algorithms, which was applied to determine the optimal variables and nodes in a region for each regressor model. Differential evolution and particle swarm optimization have been used as optimization algorithms, producing an improvement of up to 17.3% concerning the baseline databases.…”

Section: Introductionmentioning

confidence: 99%

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

He,

Wang,

Tang

et al. 2024

Atmosphere

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High-quality visibility forecasting benefits traffic transportation safety, public services, and tourism. For a more accurate forecast of the visibility in the Guizhou region of China, we constructed several visibility forecasting models via progressive refinements in different compositions of input observational variables and the adoption of the Unet architecture to perform hourly visibility forecasts with lead times ranging from 0 to 72 h over Guizhou, China. Three Unet-based visibility forecasting models were constructed according to different inputs of meteorological variables. The model training via multiple observational variables and visibility forecasts of a high-spatiotemporal-resolution numerical weather prediction model (China Meteorological Administration, Guangdong, CMA-GD) produced a higher threat score (TS), which led to substantial improvements for different thresholds of visibility compared to CMA-GD. However, the Unet-based models had a larger bias score (BS) than the CMA-GD model. By introducing the U2net architecture, there was a further improvement in the TS of the model by approximately a factor of two compared to the Unet model, along with a significant reduction in the BS, which enhanced the stability of the model forecast. In particular, the U2net-based model performed the best in terms of the TS below the visibility threshold of 200 m, with a more than eightfold increase over the CMA-GD model. Furthermore, the U2net-based model had some improvements in the TS, BS, and RMSE (root-mean-square error) compared to the LSTM_Attention model. The spatial distribution of the TS showed that the U2net-based model performed better at the model grid scale of 3 km than at the scale of individual weather stations. In summary, the visibility forecasting model based on the U2net algorithm, multiple observational variables, and visibility data from the CMA-GD model performed the best. The compositions of input observational variables were the key factor in improving the deep learning model’s forecasting capability, and these improvements could improve the value of forecasts and support the socioeconomic needs of sectors reliant on visibility forecasting.

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“…However, neural networks require large datasets, high computational power to train the model initially, and time‐intensive fine‐tuning (Memon et al ., Memon et al ., 2019) which also has an impact on the environment in terms of carbon emission (Lacoste et al ., 2019). Furthermore, the use of complex ML (deep learning) algorithms compared to simpler ML algorithms does not always yield significantly better results at much higher computational costs (Peláez‐Rodríguez, Marina, et al ., 2023; Peláez‐Rodríguez, Pérez‐Aracil, et al ., 2023). In this study, we therefore aim to use the eXtreme Gradient Boosting (XGB) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Improving classification‐based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data

Schütz,

Bendix

et al. 2023

Quart J Royal Meteoro Soc

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Radiation fog nowcasting remains a complex yet critical task due to its substantial impact on traffic safety and economic activity. Current numerical weather prediction models are hindered by computational intensity and knowledge gaps regarding fog‐influencing processes. Machine‐Learning (ML) models, particularly those employing the eXtreme Gradient Boosting (XGB) algorithm, may offer a robust alternative, given their ability to learn directly from data, swiftly generate nowcasts, and manage non‐linear interrelationships among fog variables. However, unlike recurrent neural networks XGB does not inherently process temporal data, which is crucial in fog formation and dissipation. This study proposes incorporating preprocessed temporal data into the model training and applying a weighted moving‐average filter to regulate the substantial fluctuations typical in fog development. Using an ML training and evaluation scheme for time series data, we conducted an extensive bootstrapped comparison of the influence of different smoothing intensities and trend information timespans on the model performance on three levels: overall performance, fog formation and fog dissipation. The performance is checked against one benchmark and two baseline models. A significant performance improvement was noted for the station in Linden‐Leihgestern (Germany), where the initial F1 score of 0.75 (prior to smoothing and trend information incorporation) was improved to 0.82 after applying the smoothing technique and further increased to 0.88 when trend information was incorporated. The forecasting periods ranged from 60 to 240 min into the future. This study offers novel insights into the interplay of data smoothing, temporal preprocessing, and ML in advancing radiation fog nowcasting.

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

Cited by 9 publications

References 62 publications

Fog Decision Support Systems: A Review of the Current Perspectives

Fog Decision Support Systems: A Review of the Current Perspectives

Improvement in the Forecasting of Low Visibility over Guizhou, China, Based on a Multi-Variable Deep Learning Model

Improving classification‐based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data

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