In machine learning-based classifications, attribute selection plays a key role in building more efficient models for two main reasons: first, the selected attributes must adequately discriminate between classes; second, they must balance informational value and operational/computational cost. This raises a central question: What attributes and how many are truly essential to ensure an effective and resource-efficient classification? In the context of emergency mapping, particularly during disasters, attribute selection becomes even more critical. This work proposes an assessment of the dimensional trade-offs involved in feature selection, aiming to determine whether models using reduced sets of attributes can achieve performance comparable to more complex models that rely on extensive variable sets. The results indicate that it is possible to reduce the number of attributes, as well as the computational/operational costs associated with flood mapping, without significantly compromising the quality of the final product. This reduction allows to keep the efficiency of the mapping, making it easier its application to emergency contexts where agility and resources economy are essential