Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.
Physics-Informed Learning for Predictive Multi-Scale Modeling of Water Cycle and Extreme Events