Attention based Long Short-Term Memory Network for Coastal Visibility Forecast

Min, Rui; Wu, Ming; Xu, Mengqiu; Zu, Xun

doi:10.1109/ccis57298.2022.10016374

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Machine learning methods based on single-modal data, such as deep learning and support vector machines, have significantly progressed in low-visibility prediction. Min et al [12] proposed a deep learning framework with an attention mechanism for visibility prediction, achieving state-of-the-art accuracy (68.9%) in runway visual range prediction using a custom dataset collected from airport observation stations. Cornejo-Bueno et al [13] developed polynomial regression and deep neural network (DNN) models for visibility prediction.…”

Section: Machine Learning-based Approachmentioning

confidence: 99%

ATCNet: A Novel Approach for Predicting Highway Visibility Using Attention-Enhanced Transformer–Capsule Networks

Li,

Yang,

Yuan

et al. 2024

Electronics

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Meteorological disasters on highways can significantly reduce road traffic efficiency. Low visibility caused by dense fog is a severe meteorological disaster that greatly increases the incidence of traffic accidents on highways. Accurately predicting highway visibility and taking timely countermeasures can mitigate the impact of meteorological disasters and enhance traffic safety. This paper introduces the ATCNet model for highway visibility prediction. In ATCNet, we integrate Transformer, Capsule Networks (CapsNet), and self-attention mechanisms to leverage their respective complementary strengths. The Transformer component effectively captures the temporal characteristics of the data, while the Capsule Network efficiently decodes the spatial correlations and hierarchical structures among multidimensional meteorological elements. The self-attention mechanism, serving as the final decision-refining step, ensures that all key temporal and spatial hierarchical information is fully considered, significantly enhancing the accuracy and reliability of the predictions. This integrated approach is crucial in understanding highway visibility prediction tasks influenced by temporal variations and spatial complexities. Additionally, this study provides a self-collected publicly available dataset, WD13VIS, for meteorological research related to highway traffic in high-altitude mountain areas. This study evaluates the model’s performance in terms of Mean Squared Error (MSE) and Mean Absolute Error (MAE). Experimental results show that our ATCNet reduces the MSE and MAE by 1.21% and 3.7% on the WD13VIS dataset compared to the latest time series prediction model architecture. On the comparative dataset WDVigoVis, our ATCNet reduces the MSE and MAE by 2.05% and 5.4%, respectively. Our model’s predictions are accurate and effective, and our model shows significant progress compared to competing models, demonstrating strong universality. This model has been integrated into practical systems and has achieved positive results.

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Section: Machine Learning-based Approachmentioning

confidence: 99%

ATCNet: A Novel Approach for Predicting Highway Visibility Using Attention-Enhanced Transformer–Capsule Networks

Li,

Yang,

Yuan

et al. 2024

Electronics

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“…In recent years, there has been a focus on predicting the presence of fog, called a binary classification task in the context of ML, given meteorological variables, using either deep learning (Kipfer, 2017;Miao et al, 2020;Kamangir et al, 2021;Liu et al, 2022;Min et al, 2022;Park et al, 2022;Zang et al, 2023) or more standard techniques (Marzban et al, 2007;Boneh et al, 2015;Dutta and Chaudhuri, 2015;Cornejo-Bueno et al, 2017;Wang et al, 2021;Vorndran et al, 2022). Such approaches forego the opportunity for insights regarding the dissipation and formation of fog over time by not predicting fog intensity and, thus, do not lend themselves to characterizing the underlying fog-evolution mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Machine learning analysis and nowcasting of marine fog visibility using FATIMA Grand Banks campaign measurements

Gultepe,

Wang,

Blomquist

et al. 2024

Front. Earth Sci.

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Introduction: This study presents the application of machine learning (ML) to evaluate marine fog visibility conditions and nowcasting of visibility based on the FATIMA (Fog and turbulence interactions in the marine atmosphere) campaign observations collected during July 2022 in the North Atlantic in the Grand Banks area and vicinity of Sable Island, northeast of Canada.Methods: The measurements were collected using instrumentation mounted on the Research Vessel Atlantic Condor. The collected meteorological parameters were: visibility (Vis), precipitation rate, air temperature, relative humidity with respect to water, pressure, wind speed, and direction. Using all variables, the droplet number concentration was used to qualitatively indicate and assess characteristics of the fog using the t-distributed stochastic neighbor embedding projection method (t-SNE), which clustered the data into groups. Following t-SNE analysis, a correlation heatmap was used to select relevant meteorological variables for visibility nowcasting, which were wind speed, relative humidity, and dew point depression. Prior to nowcasting, the input variables were preprocessed to generate additional time-lagged variables using a 120-minute lookback window in order to take advantage of the intrinsic time-varying features of the time series data. Nowcasting of Vis time series for lead times of 30 and 60 minutes was performed using the ML regression methods of support vector regression (SVR), least-squares gradient boosting (LSB), and deep learning at visibility thresholds of Vis < 1 km and < 10 km.Results: Vis nowcasting at the 60 min lead time was best with LSB and was significantly more skillful than persistence analysis. Specifically, using LSB the overall nowcasts at Vis 1 < km and Vis 10 < km were RMSE = 0.172 km and RMSE = 2.924 km, respectively. The nowcasting skill of SVR for dense fog (Vis ≤ 400 m) was significantly better than persistence at all Vis thresholds and lead times, even when it was less skillful than persistence at predicting high visibility.Discussion: Thus, ML techniques can significantly improve Vis prediction when either observations or modelbased accurate time-dependent variables are available. The results suggest that there is potential for future ML analysis that focuses on modeling the underlying factors of fog formation.

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Predicting Airport Visibility using Seq2Seq Models

Wang,

Wang

2023

2023 11th International Conference on Information Systems and Computing Technology (ISCTech)

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Attention based Long Short-Term Memory Network for Coastal Visibility Forecast

Cited by 3 publications

References 23 publications

ATCNet: A Novel Approach for Predicting Highway Visibility Using Attention-Enhanced Transformer–Capsule Networks

ATCNet: A Novel Approach for Predicting Highway Visibility Using Attention-Enhanced Transformer–Capsule Networks

Machine learning analysis and nowcasting of marine fog visibility using FATIMA Grand Banks campaign measurements

Predicting Airport Visibility using Seq2Seq Models

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