Social Mobilization and Migration Predictions by Machine Learning Methods: A study case on Lake Urmia
Fatemeh Dehghan Khangahi1, Farzad Kiani2
1Fatemeh Dehghan Khangahi*, Political Science Faculty, International Relation Dept. Istanbul University, Istanbul, Turkey.
2Farzad Kiani, Engineering and Architecture Faculty, Computer Engineering Dept. Istanbul Arel University, Istanbul, Turkey.
Manuscript received on April 21, 2021. | Revised Manuscript received on April 29, 2021. | Manuscript published on April 30, 2021. | PP: 123-127 | Volume-10 Issue-6, April 2021 | Retrieval Number: 100.1/ijitee.F88330410621| DOI: 10.35940/ijitee.F8833.0410621
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Voluntary or compulsory immigration of people to other regions or countries for different reasons can lead to social, cultural, and economic problems. In recent years, especially the rate of forced migration has increased and it is sometimes chosen as a last resort for social mobilization. Scientists and governments who have gathered data on migration for years have recently realized that Artificial Intelligence (AI) and Machine Learning (ML) methods are important in analyzing this data and developing utility models and systems. It has been gradually understood that these new technologies are very important in recent years, but studies have either only been done in the field of social sciences or only in the field of engineering. In this study, a comprehensive interdisciplinary study covering both dimensions is prepared. In this study, a machine learning-based model is presented by making a multidisciplinary study and exemplifying the Lake Urmia case study. The proposed method can be used in the decision-making process in the migration management. In our study, is proposed a model using three different algorithms (Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN)). According to the results, the SVM-based model outperforms others in accuracy and validations. The trained model of SVM has a success rate on mean accuracy as near to 86% with 4,00E-02 standard deviation rate. SVM ranked first and this method was followed by RF and KNN methods, respectively. In this context, this model can make forward-looking predictions and, like an expert system, can guide the relevant researchers and even state or form ideas according to the results obtained from it.
Keywords: Classification, Migration, Social Mobilization, Machine Learning, Lake Urmia.