Smoothing and approximation of grassland fire loading data for engineering structures by Capped Extended Support Vector Regression

Abstract

This research proposes a machine learning-aided data smoothing and approximation scheme to investigate grassland fire loading for engineering structures. The investigations on grassland fire loading have significant impacts on wildfire-resistant design, building codes, regulations, and the like in specific regions. Real-world data collection systems inevitably introduce imperfections, such as noise, outliers, and the like. Furthermore, the collected data are normally discretized on time and location, and thus the information between them is completely absent. Achieving a smooth fit to data and reducing these imperfections are desired to facilitate subsequent analyses. Accordingly, a supervised machine learning algorithm is developed to smooth and approximate grassland fire loading data. Within the proposed algorithm, a convex optimization programming is established, which provides theoretical support to the accuracy of the algorithm. Then, through the proposed scheme, the grassland fire loading data can be approximated into an explicit regression model, which continuously completes the missing information during the observations. Moreover, attributed to the sparsity feature of the developed algorithm, the model can be simplified in its expression to further improve its applicability in engineering. Finally, real-world experimental data and numerical simulation results are separately investigated to demonstrate the effectiveness of the proposed scheme.