Area Estimation of Forest Fires using TabNet with Transformers.

In this paper, we propose a novel approach for estimating the burned area of forest fires using the TabNet transformer-based architecture. Forest fires pose a significant threat to ecosystems, and accurate estimation of the affected area is essential for effective disaster management and resource allocation. We conducted a comprehensive analysis of various Machine Learning (ML) and Deep Learning (DL) methods, including Random Forest, Neural Networks, Neural Architecture Search (NAS), TabNet with Transformers, and Self-Supervised Learning with Autoencoders, to identify the most accurate and efficient model for area estimation. Our experiments employed a publicly available dataset, UCI Forest Fires, containing a combination of meteorological, geospatial, and categorical data. We implemented a thorough preprocessing pipeline that included handling categorical variables, standardization, and feature engineering. The results demonstrate that TabNet outperforms other methods, achieving state-of-the-art accuracy and generalization in predicting the target variable with a Mean Squared Error (MSE) of 2319 in training and 7781 in testing. [ABSTRACT FROM AUTHOR]