Table 4 Input data, output data, and model objective for CNN-based dust transport and visibility prediction.

From: Dynamic monitoring of dust transport effect on maritime visibility using multi source satellite data and advanced deep learning approach

Input Data

Output Data

Model Objective

MODIS AOD images

Predicted visibility (Risk)

The CNN model was trained to predict visibility reduction caused by dust transport based on the spatial patterns extracted from AOD data and meteorological conditions.

MERRA-2 meteorological data

-

Provides wind speed, humidity, and pressure information which is crucial for modeling dust transport dynamics.

Visibility Observations

-

Used as the target variable to validate and assess the model’s performance.

Time-series data from AOD

-

Temporal features are captured through CNN’s convolutional layers to incorporate the sequential nature of dust transport.

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