Multi-Criteria Decision Analysis Using AI for Sustainable Animal Waste Management and Environmental Protection

Abstract

Sustainable animal waste management has become a critical environmental challenge due to the rapid growth of livestock production and the increasing risks of greenhouse gas emissions, water pollution, soil degradation, and inefficient resource utilization. Selecting an appropriate waste management strategy requires the simultaneous evaluation of multiple environmental, economic, technical, and social factors, making the decision-making process highly complex. This study proposes an Artificial Intelligence-Based Multi-Criteria Decision Analysis (AI-MCDA) framework for sustainable animal waste management and environmental protection. The proposed framework integrates Artificial Intelligence techniques with Multi-Criteria Decision Analysis to objectively evaluate and rank alternative waste treatment technologies, including composting, anaerobic digestion, biochar production, incineration, and direct land application. Machine learning models, namely Decision Tree, Artificial Neural Network, Random Forest, and XGBoost, are employed to analyze sustainability indicators and estimate criterion importance, while MCDA techniques are used to prioritize management alternatives. Experimental evaluation demonstrates that XGBoost achieved the highest predictive performance with an accuracy of 98.91%, and the integrated AI-MCDA framework consistently identified anaerobic digestion as the most sustainable waste management option based on environmental impact, energy recovery, nutrient recycling, and economic feasibility. The proposed framework significantly improves decision accuracy, ranking consistency, and transparency compared with conventional decision-making approaches. The findings demonstrate that the AI-MCDA framework provides an effective decision-support system for farmers, environmental agencies, and policymakers, promoting sustainable livestock waste management, resource recovery, and environmental protection.