EVALUATING LAND USE TRANSITIONS THROUGH DATA-DRIVEN AGENT-BASED MODELING WITH RANDOM FOREST CLASSIFICATION
Abstract. Urban growth is making it harder to plan for land use that is beneficial for the environment. This study looks at how land use changes from 2020 to 2040 by using a combined modeling approach that uses both agent-based simulation and machine learning. We model changes in three main types of land: residential, forest, […]