Social Mobilization and Migration Predictions by Machine Learning Methods: A study case on Lake Urmia

Voluntary or compulsory immigration of people to other regions countries for different reasons can lead social, cultural, and economic problems. In recent years, especially the rate forced migration has increased it is sometimes chosen as a last resort social mobilization. Scientists governments who have gathered data on years recently realized that Artificial Intelligence (AI) Machine Learning (ML) methods are important in analyzing this developing utility models systems. It been gradually understood these new technologies very but studies either only done field sciences engineering. study, comprehensive interdisciplinary study covering both dimensions prepared. machine learning-based model presented by making multidisciplinary exemplifying Lake Urmia case study. The proposed method be used decision-making process management. our using three algorithms (Support Vector (SVM), Random Forest (RF), K-Nearest Neighbors (KNN)). According results, SVM-based outperforms others accuracy validations. trained SVM success mean near 86% with 4,00E-02 standard deviation rate. ranked first was followed RF KNN methods, respectively. context, make forward-looking predictions and, like an expert system, guide relevant researchers even state form ideas according results obtained from it.