Spatial-Temporal Analysis Of Environmental Data Using Convolutional Neural Networks

Abstract

This paper presents a comprehensive spatial-temporal analysis of environmental data using Convolutional Neural Networks (CNNs) and Empirical Orthogonal Functions (EOFs) decomposition. The study employs two distinct methodologies to extract meaningful patterns from environmental datasets, contributing to a deeper understanding of spatial and temporal dynamics in environmental systems. Firstly, CNNs are utilized for spatial-temporal analysis by simulating environmental data comprising spatial and temporal dimensions, followed by basic statistical analysis and visualization techniques to illustrate spatial-temporal patterns. Secondly, EOFs decomposition is applied to a simulated environmental dataset, enabling the identification of dominant spatial and temporal patterns. Through graphical representations such as bar graphs, line graphs, and pie charts, the distribution of mean environmental data values across locations, temporal analysis for specific locations, and the variance explained by EOF components are visually depicted and discussed. The results demonstrate the importance of spatial context in understanding environmental data patterns, the temporal variability inherent in environmental processes, and the relative contributions of EOF components in capturing dataset variability. This research provides valuable insights into environmental dynamics and facilitates evidence-based decision-making in environmental research and management.