AI-BASED STRAY ANIMAL POPULATION GROWTH PREDICTOR

The rapid increase in stray animal populations in urban environments has become a significant concern affecting public safety, environmental hygiene, and the overall quality of life. Uncontrolled growth of stray animals leads to issues such as the spread of zoonotic diseases, road accidents, waste mismanagement, and ecological imbalance. Traditional methods for monitoring and controlling stray animal populations primarily rely on manual surveys and statistical estimations, which are time-consuming, error-prone, and lack scalability. Moreover, these approaches do not provide predictive insights, making it difficult for authorities to implement proactive and efficient management strategies. This project proposes an Artificial Intelligence-based system for predicting stray animal population growth using advanced machine learning techniques. The system utilizes structured datasets comprising various parameters such as geographical location, historical population data, sterilization rates, waste management conditions, and environmental factors. Data preprocessing techniques including data cleaning, normalization, and feature engineering are applied to enhance the quality and relevance of the dataset. The core of the system is built using the Random Forest algorithm, an ensemble learning technique known for its high accuracy and robustness. The model is trained on historical data to identify patterns and relationships between multiple variables, enabling it to forecast future population trends effectively. Performance evaluation metrics such as accuracy, precision, recall, and mean squared error are used to validate the reliability of the model. Experimental results demonstrate that the proposed system achieves superior performance compared to traditional statistical methods and other machine learning algorithms such as Decision Tree and Support Vector Machine (SVM).