以隨機森林判釋都市集水區保育地貌之空間分析研究

The Spatial Analysis Study Using Random Forest To Interpret Conservation Landforms In Urban Watershed Area

The study of watershed conservation area for the sustainable development may be directly or indirectly affected by problems such as soil and debris flow disasters caused by illegal development or natural disasters. It often impacts the water quality and also causes severe reservoir sedimentation or produced by a threat to the sustainable utilization of water resources. In present, the land management in watersheds mainly relies on manual patrols to monitor for any illegal situations to maintain ecological conservation in the watersheds. Hence, this study explores how to use data mining and image recognition techniques, combined with machine learning, and rapidly calculate to determine the relevant information of environments. This study utilizes high-resolution images and analyzes them using different features with the Random Forest method, presenting the results through data visualization techniques. Applying a supervised learning approach, the training samples are used to build a model, and then using test samples for prediction with the Bayesian Optimization for modifying the Random Forest parameters for model tuning. Finally, utilizing function calculations and plotting libraries, the results are drawn as a thematic map and a series of confusion matrix for data visualization analysis with comparison. This study also employs methods such as parameter selection and texture information to improve model feasibility.