Paper Title
POPULATION GROWTH DETECTION USING PROGRESSIVE LEARNING NEURAL NETWORK

Abstract
Urbanization is a global phenomenon that is transforming city landscapes, with rapidly growing urban populations necessitating accurate forecasts for future population trends. Precise population predictions are crucial for effective urban planning, resource allocation, and sustainable development. This project introduces an innovative approach to predicting population growth using Progressive Learning Neural Networks (PLNNs). By leveraging historical demographic data and advanced machine learning techniques, the model extrapolates future urban population trends based on past dynamics. The methodology includes data collection, preprocessing, and training. A Progressive Neural Network is constructed, integrated into the initial neural network, and machine learning algorithms are applied to predict population growth. The model's performance is evaluated using metrics such as Mean Absolute Error (MAE) and Mean Squared Error (MSE). Through progressive learning mechanisms, the model continuously improves its predictive accuracy, capturing complex shifts in urban population patterns. This approach provides policymakers, urban planners, and stakeholders with critical insights, empowering them to make data-driven decisions about infrastructure investments, land management, social services, and disaster preparedness. By emphasizing transparency and accuracy, this system meets the immediate and future needs of urban societies. Keywords - population growth, urbanization, SGD, progressive learning neural networks, machine learning, deep learning, MSE, MAE, forecast.