Smart Waste Management Through Automated Machine Learning Approaches

The novel AutoML-based method presented in this study is intended to maximize intelligent waste management techniques. The system makes precise predictions about waste collection requirements depending on location, waste quantities, and time by utilizing algorithms like Random Forests and decision trees. Sensor data collected in real time allows the system to establish the best collection schedules, reducing operating expenses and increasing productivity. This research demonstrates the revolutionary potential of AutoML in tackling issues related to urban waste management, encouraging environmental sustainability, and helping to create a more sustainable future.