## 4 Conclusions  
This study introduces and validates a novel approach for assessing the condition of asbestos-cement (AC) roofs by integrating field data with the performance of five supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. The methodology includes an in-depth analysis of 380 reflectance bands using information gain-based techniques, resulting in a ranked list of significant bands. The main contributions and impacts are listed below:  
* The ranking list played a crucial role in grouping the bands and identifying the optimal number needed to enhance model accuracy in differentiating between low intervention priority (LIP) and high intervention priority (HIP) AC roofs;
* The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it a robust tool for the identification of AC roof conditions. Additionally, the random forest model demonstrated reliable performance across various band groups, further validating its utility in this context;
* The gain method has enabled the filtering of the most relevant bands to optimize the performance of the machine learning approach. The highest efficiency in identifying the deterioration state of AC roofs is achieved using the decision tree (DT) method, particularly when only 10 bands (attributes) are considered. Subsequently, 20 and 30 bands result in slightly lower efficiencies, reduced by 4.12% and 5.48%, respectively. For band numbers ranging from 50 to 270, the random forest (RF) method demonstrates superior efficiencies, though still falling short of the DT method applied to 10 bands by less than 10%. Beyond 270 bands, the performance of DT and RF methods becomes nearly equivalent. ;
* The K-Nearest Neighbors (KNN), logistic regression (LR), and support vector machine (SVM) methods do not surpass the efficiencies of the other methods in any scenario. These comparative analyses, which are rare in the literature, especially in applications related to determining the deterioration state of AC roofs, suggest that the DT method is more suitable for this task;
* The decision tree (DT) method is inherently simple and highly interpretable, which allows it to effectively capture and model complex relationships between the attributes (bands) and the target variable. This simplicity may enable DT to better focus on the most relevant bands, leading to higher accuracy in classifying the state of deterioration in AC roofs;
* The DT method is less prone to overfitting when working with a smaller number of attributes, compared to methods like RF, KNN, LR, and SVM. In cases where only a limited number of bands are available, DTs may outperform other methods by avoiding the noise that can complicate the decision boundaries in more complex models like SVM or KNN, which may require more data to generalize well;
* The findings of this study provide a valuable tool for government and environmental authorities, particularly in developing countries, where the efficient and cost-effective assessment of AC roof conditions is essential for public health and safety. By leveraging open-source software like Weka, this research offers a practical and scalable solution for material characterization, especially in regions where resources for advanced spectral and hyperspectral image analysis are limited. The methodology developed here is adaptable to different urban environments and climatic conditions, making it a versatile tool for global asbestos management efforts, particularly in countries where asbestos regulations are newly implemented. Future research will explore comparisons with deep learning-based models for different bands groups, organized by relevance using the InfoGainAttributeEval and Ranker methods.