Detecting Anomalies in Population Growth using A Hybrid Reinforcement Learning Approach

Abstract

This paper introduces a novel approach, combining traditional statistical models with reinforcement learning algorithms, to detect abnormal population growth patterns. Our method utilizes diverse demographic data sources, including census records, satellite imagery, and social media analytics. By incorporating deep neural networks, the reinforcement learning framework learns historical growth patterns and adapts to changing scenarios. Statistical metrics provide baseline values for normal population growth, enhancing model robustness. Experimental evaluations using real-world datasets demonstrate the superior performance of our hybrid approach in identifying abnormal population growth across different regions. The model's applications span urban planning, disaster response, and resource allocation, highlighting its importance in addressing modern population challenges.