An Intelligent Visibility Retrieval Framework Combining Meteorological Factors and Image Features

Video imagery enables both qualitative characterization and quantitative retrieval of low-visibility conditions. These phenomena exhibit complex nonlinear dependencies on atmospheric processes, particularly during moisture-driven weather events such as fog, rain, and snow. To address this challenge, we propose a dual-branch neural architecture that synergistically processes optical imagery and multi-source meteorological data (temperature, humidity, and wind speed). The framework employs a convolutional neural network (CNN) branch to extract visibility-related visual features from video imagery sequences, while a parallel artificial neural network (ANN) branch decodes nonlinear relationships among the meteorological factors. Cross-modal feature fusion is achieved through an adaptive weighting layer. To validate the framework, multimodal Backpropagation-VGG (BP-VGG) and Backpropagation-ResNet (BP-ResNet) models are developed and trained/tested using historical imagery and meteorological observations from Nanjing Lukou International Airport. The results demonstrate that the multimodal networks reduce retrieval errors by approximately 8%–10% compared to unimodal networks relying solely on imagery. Among the multimodal models, BP-ResNet exhibits the best performance with a mean absolute percentage error (MAPE) of 8.5%. Analysis of typical case studies reveals that visibility fluctuates rapidly while meteorological factors change gradually, highlighting the crucial role of high-frequency imaging data in intelligent visibility retrieval models. The superior performance of BP-ResNet over BP-VGG is attributed to its use of residual blocks, which enables BP-ResNet to excel in multimodal processing by effectively leveraging data complementarity for synergistic improvements. This study presents an end-to-end intelligent visibility inversion framework that directly retrieves visibility values, enhancing its applicability across industries. However, while this approach boosts accuracy and applicability, its performance in critical low-visibility scenarios remains suboptimal, necessitating further research into more advanced retrieval techniques—particularly under extreme visibility conditions.