Importance of 3D convolution and physics on a deep learning coastal fog model. (August 2022)

• Record Type: Journal Article
• Authors: Kamangir, Hamid; Krell, Evan; Collins, Waylon; King, Scott A.; Tissot, Philippe
• Is Part Of: Environmental modelling & software. Volume 154(2022)
• Publication Date: 2022-08
• Subjects: 3D convolutional neural network -- SHAP -- Explainable AI -- Permutation feature importance -- Atmospheric prediction; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105424
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The forecasting of hazardous atmospheric phenomena is often challenging. Artificial intelligence (AI) models have been applied to atmospheric science problems. Model complexity provides a motivation to quantify the importance of model architecture components. We studied the relative importance of the components of the FogNet model that was designed for big atmospheric data: 1) 3D versus 2D convolution, 2) physics-based grouping and ordering of meteorological input features, 3) different auxiliary CNN-based feature learning modules and 4) parallel versus sequential spatial-variable-wise feature learning. We investigate the relative importance of these CNN architectural features by predicting coastal fog, a complex spatiotemporal dynamical process. We use four explainable AI techniques to better understand input feature contributions. The results of the experiments demonstrate that 3D-CNN based models better capture the complexity of the fog prediction process than the 2D-CNNs. We also show that physics-based feature grouping, and the order in which they are fed into the CNNs, significantly impacts performance. Highlights: Evaluating FogNet v1.0 on fused numerical model output and satellite imagery. Comparing 3D vs 2D kernel learning ability of the atmospheric vertical structure. Evaluating physics-based grouping and ordering of atmospheric input data. Using four XAI techniques for discovering input atmospheric variable importance. Evaluating using auxiliary modules such as attention to improve the FogNet v1.0.