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, and agricultural. We also include important elements that affect decisions, like how close the land is to roadways, its productivity index, how it was used before, and how it is changing next door. A feature importance analysis shows that the most important factor in land use decisions is how close it is to highways, followed by how productive the area is. The random forest classifier was the best of the machine learning models tested. It had an accuracy of 89.3%, a precision of 0.91, and a recall of 0.88, which was better than decision tree and neural network models. The results show that residential land usage is likely to rise at the expense of forest area, whereas agricultural land is likely to grow only a little. These results show how useful hybrid modeling approaches are for predicting how things will change in space and for making policy decisions that strike a balance between urban growth and environmental protection. Combining geographical data and historical trends into predictive modeling gives land management and urban planning strategies a strong framework.