INTEGRATION OF MULTIMODAL DATA THROUGH INTERMEDIATE FUSION FOR ENHANCED CLIMATE PROCESSESS MODELLING

Abstract

Climate change analysis and modeling is a complex problem, which requires processing data from multiple sources and domains. Conventional algorithms and methods provide limited knowledge and may not be capable of including and processing a vast number of relevant input features. The research paper explores the application of multimodal data fusion techniques to enhance climate process modeling. Integration of data from diverse sources to provide a comprehensive understanding of climate dynamics is recommended. The aims of the study are to determine optimal methods and tools for implementing multimodal data fusion, formulate the mathematical apparatus for multimodal data fusion in climate modeling, and conduct a study of existing mathematical approaches for data fusion, modeling, and validation. Five distinct modalities have been prepared and fused as a result of the research, achieving a common data representation.

The paper presents a detailed diagram illustrating the step-by-step approach for intermediate fusion. It describes the preprocessing steps, including feature extraction, temporal and spatial synchronization, and data vectorization. The mathematical apparatus for processing each modality is thoroughly explained, including factor analysis for dimensionality reduction, k-means clustering for demographic data, FastText algorithm for text vectorization, and Enhanced Vegetation Index (EVI) calculation for satellite imagery analysis. The fused multimodal dataset is then used to provide a mathematical formulation for training a regression model, specifically an artificial neural network, to learn the relationships between climate indicators (dependent variable) and the fused features (independent variables).

The multimodal fusion approach has the potential to improve understanding of complex climate systems, enable more effective synthesis of information from various sources, and generate accurate predictions of climate changes and trends. Future work is proposed to verify the predictive potential of multimodal datasets, optimize training hyperparameters of artificial neural networks and other models, and conduct a comparative analysis of their accuracy.

This research contributes to the field of climate science by proposing an innovative approach to data integration and analysis, leading to robust and comprehensive climate models.