Machine Learning-Based Waste Management Analysis Supporting SDG 11 & 12: A Review of Approaches for Indian Cities

Abstract

This paper presents a comprehensive review of machine learning approaches for waste management analysis in Indiancities,withafocusonsupportingSustainableDevelopmentGoals(SDGs)11and12.Weanalyzevariouspredictivemodelsforwastegeneration,includingRandomForest,GradientBoosting,andLinearRegressiontechniques. The review examines data preprocessing methodologies, feature importance analysis, and visualization approaches that provide insights into waste generation patterns, recyclingrates, and municipal efficiency. We evaluate model performance metrics and discuss how these analytical approaches can inform policy decisions to achieve sustainability targets. Key findings highlight the significance of population density, municipal efficiency scores, and awareness campaigns in predicting and managing waste generation. The paper concludes with recommendations for future research directions and practical applications to enhance waste management practices in developing urban environments.

Introduction

Rapid urbanization and population growth in developing countries like India have intensified waste management challenges, raising environmental, social, and economic concerns. The United Nations’ Sustainable Development Goals (SDGs), especially SDG 11 (Sustainable Cities) and SDG 12 (Responsible Consumption and Production), provide frameworks to address these issues.

This review paper investigates the application of machine learning (ML) techniques to analyze waste management data from major Indian cities (2019-2023) to support these SDGs. The study uses a rich dataset covering multiple waste types, recycling rates, population density, municipal efficiency, and related factors. Key ML models—Random Forest, Gradient Boosting, and Linear Regression—were evaluated for predicting waste generation.

Results show that the Random Forest model performed well (R²=0.68), capturing complex patterns, while Linear Regression showed an unusually high R² (0.98), possibly due to overfitting. Feature importance analysis highlighted population density, municipal efficiency, awareness campaigns, and temporal trends as major influencers of waste generation and recycling.

Despite ML’s potential to inform policy and track SDG progress, challenges remain, including data quality issues, balancing model accuracy and interpretability, complex feature engineering, aligning ML outputs with SDG metrics, and practical implementation barriers within municipal authorities.

Future research directions include adopting advanced ML methods like deep learning and reinforcement learning, integrating IoT and citizen science for real-time data, improving SDG-specific modeling, and developing user-friendly decision-support tools to enhance waste management practices across diverse urban contexts.

Conclusion

Thisreviewexaminedtheapplicationofmachinelearningapproachesforwastemanage- ment analysis in Indian cities, with a specific focus on supporting SDGs 11 and 12.The analysis yielded several key insights: 1) Model Performance: Machine learning models demonstrated significant potential for waste generation prediction. The Random Forest model (R² = 0.68, MSE = 75.54) provided valuable feature importance analysis, while the Linear Regression model achieved surprisingly strong performance (R² = 0.98, MSE = 59.44), though this may indicate potential overfitting. 2) Key Factors: Feature importance analysis revealed that population density, munic- ipal efficiency scores, and awareness campaigns are the most significant factors influenc- ing waste generation and recycling rates. These findings highlight potential intervention points for policymakers seeking to improve waste management practices. 3) SDG Integration: The integration of machine learning approaches with SDG frame- works presents both opportunities and challenges. While these approaches can provide valuable insights for tracking progress toward SDG targets, challenges remain in aligning metrics, quantifying impact, and translating insights into policy actions. 4) Implementation Challenges: Several barriers impede the implementation and adop- tion of machine learning approaches in waste management, including data quality issues, technical capacity limitations, resource constraints, and institutional resistance.

References

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Copyright

Copyright © 2025 Gnanendra Naidu N, Kashish Gupta, G Vijaya Kumar, Trilokchandran B, Divya Ajish. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.