[cosc-grad-students-list] Thi Hong Hue Dinh MS Thesis Defense - 4/14/21 1:30pm ": Fog Prediction Using Deep Learning Models"

Tue Apr 13 12:36:02 CDT 2021

You ae all invited to the following thesis defense.  It will be virtual (link at bottom)

Subject: Fog Prediction Using Deep Learning Models

Speaker: Thi Hong Hue Dinh

Date and Time: April 14th, 2021 1:30-3:00 pm.

Abstract:

The occurrence of fog has adverse impacts to human activities, aviation, and water transportation operations. These events can cause postponement and cancellation of flights and accidents between ships and vessels leading to economic costs. The design of accurate models is required to forecast the low visibility events caused by fog. However, the prediction of fog remains a challenge due to the rare occurrence of these events. In this study, deep learning networks (DNN) were proposed using the output from Numerical Weather Prediction (NWP) to predict 6hr, 12hr, and 24-hour lead time low visibility levels in the Corpus Christi Area. These models based on the autoencoder architecture were applied as a post-processing of deterministic NWP model and sea surface temperature (SST) output. The autoencoder was utilized to reduce the dimension of the input features to select a higher order of representation from raw data. By converting data from high dimensional space into a lower dimension, autoencoder models preserve the meaningful properties of original features in an unsupervised learning fashion. A logistic regression was also added to solve the classification problem of visibility level. Additionally, the undersampling and oversampling were also examined to solve the class imbalance problem caused by the less positive cases (fog cases). A 11-year database of NWP and SST was used to develop, train, validate, and test the proposed models to predict the occurrence of fog. The target of the models was categorized into three overlapping classes, including ≤ 1600m, ≤ 3200m, and ≤ 6400m. The prediction skill of these models was evaluated by relative operating characteristic curves and seven different skill scores. The results indicate that the DNN models are able to generate good discrimination for all lead times and visibility categories. The DNN models consistently outperform that of the operational model used by National Weather Service with respect to five skill scores (HSS, PSS, POD, CSI, and ORSS). The performance of the proposed models in dimensional reduction exceeds that of the combination between principal component analysis and logistic regression.

Wednesday, April 14, 2021
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Professor of Computer Science
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Texas A&M University - Corpus Christi

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