Hybrid Deep Learning Framework for Intelligent Waste Segregation and Water Recycling in Indian Railways: Towards Sustainable and Smart Passenger Operations
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Abstract
The rapid pace of urbanization and the growing volume of waste generated across Indian Railways highlight the urgent need for efficient and automated waste management systems that support sustainable passenger services. This study presents a Hybrid Deep Learning Framework for Intelligent Waste Segregation and Water Recycling in Indian Railways, designed to automate the sorting of solid waste and optimize water reuse. The framework classifies eight key waste categories-paper, leaves, food waste, wood, general waste, plastic bags, plastic bottles, and metal cans-based on image data collected from railway stations and train compartments. Experimental testing over 20 training cycles shows consistent improvement, with the model achieving 90% training accuracy and 83% validation accuracy, while loss values steadily declined to 0.22 and 0.37. The confusion matrix indicates strong classification performance, with only minor overlap between visually similar materials such as plastic bags and bottles. The model also demonstrated good computational efficiency, requiring approximately 18 seconds per training cycle, making it suitable for real-time implementation. Evaluation using precision-recall and ROC analyses confirms reliable detection performance across all waste types. A complementary water recycling module further enables automated sorting and reuse of graywater, contributing to sustainable operations. Overall, the proposed framework enhances waste-handling efficiency, supports cleaner stations, and aligns with India’s broader goals for green transport and the “Swachh Bharat” (Clean India) mission.
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