Identifying Key Environmental Factors for Paulownia coreana Habitats: Implementing National On-Site Survey and Machine Learning Algorithms

Monitoring and preserving natural habitats has become an essential activity in many countries today. As a native tree species Korea, Paulownia coreana periodically been surveyed national ecological surveys was identified as important target for conservation well habitat monitoring management. This study explores suitability models (HSMs) conjunction with survey data various environmental factors. Together variables, the were run through machine learning algorithms such Artificial Neural Network Decision Tree & Rules, which used to identify impact of individual variables create HSMs coreana, respectively. Unlike other studies, remote sensing HSMs, this employed periodical on-site enhanced validity. Moreover, localized resources topography, soil, rainfall taken into account project suitability. Among environment used, critical attributes that affect conditions coreana. Therefore, modelling methods could play key roles planning, monitoring, managing plants regional levels. Furthermore, it shed light on existing challenges future research needs.