Understanding Climate-Driven Migration: A Nonlinear Machine Learning Approach

The relationship between climate change and migration has gained significant attention in recent years, particularly regarding its impact on vulnerable regions. This study proposes a novel modeling approach to climate-induced migration by systematically comparing traditional statistical models and machine learning techniques. While most existing research in this field relies on linear models or case-specific analyses, our work explicitly models complex non-linear and multidimensional relationships among environmental and socio-economic drivers of migration. By comparing autoregressive, polynomial, and logarithmic models with a Random Forest model, we demonstrate the added value of machine learning in capturing complex patterns that conventional models often fail to detect. Furthermore, we analyze how variables such as temperature anomalies, the Human Development Index (HDI), water stress, and the agricultural sector’s contribution to the Gross Domestic Product influence migration. This framework is applied to data-scarce and climate-sensitive regions, namely North Africa, Sub-Saharan Africa, and Southeast Asia, where robust modeling remains both challenging and urgently needed. Model performance was systematically evaluated through multiple metrics, including correlation, Mean Error (ME), Mean Absolute Error (MAE), and their normalized forms (NME and NMAE). Results show that while simpler models exhibit limited explanatory power, the Random Forest model substantially improves predictive accuracy, achieving correlation = 0.81, MAE = 0.95, NME =–0.009, and NMAE = 0.25. These findings demonstrate the model’s effectiveness in capturing migration dynamics, and also provides a replicable methodology that can support targeted adaptation strategies, effective migration management, and climate policies, particularly relevant for data-scarce regions of the Global South.