Prediction of Dust Storm Direction from Satellite Images by Utilized Deep Learning Neural Network

Harba, Hind S.; Harba, Eman S.; Farttoos, Mohammed

doi:10.1109/iec49899.2020.9122806

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Recent studies successfully implemented machine learning in dust modeling and forecasting [12][13][14][15][16][17][18][19][20][21][22] . For example, Shi et al 15 and Jiang et al 14 performed pixel-wise dust detection in typical dust regions of Asia using Support Vector Machine (SVM) and Convolutional Neural Network (CNN), respectively.…”

Section: Introductionmentioning

confidence: 99%

“…Chae et al 12 spatially interpolated similar stationary measurements and then transferred them into a CNN for real-time spatial PM 10 prediction. In the Middle East region, Shtein et al 17 spatially predicted intra-daily PM 10 levels in Israel using satellite Aerosol Optical Depth (AOD) and regression models; Harba et al 18 used CNN to predict the direction of a dust storm using a small set of satellite images from Iraq; Boroughani et al 20 used different machine-learning models and satellite images to identify dust sources in north-eastern Iran.…”

Section: Introductionmentioning

confidence: 99%

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Deep multi-task learning for early warnings of dust events implemented for the Middle East

et al. 2023

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Events of high dust loading are extreme meteorological phenomena with important climate and health implications. Therefore, early forecasting is critical for mitigating their adverse effects. Dust modeling is a long-standing challenge due to the multiscale nature of the governing meteorological dynamics and the complex coupling between atmospheric particles and the underlying atmospheric flow patterns. While physics-based numerical modeling is commonly being used, we propose a meteorological-based deep multi-task learning approach for forecasting dust events. Our approach consists of forecasting the local PM10 (primary task) measured in situ, and simultaneously to predict the satellite-based regional PM10 (auxiliary task); thus, leveraging valuable information from a correlated task. We use 18 years of regional meteorological data to train a neural forecast model for dust events in Israel. Twenty-four hours before the dust event, the model can detect 76% of the events with even higher predictability of winter and spring events. Further analysis shows that local dynamics drive most misclassified events, meaning that the coherent driving meteorology in the region holds a predictive skill. Further, we use machine-learning interpretability methods to reveal the meteorological patterns the model has learned, thus highlighting the important features that govern dust events in the Middle East, being primarily lower-tropospheric winds, and Aerosol Optical Depth.

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