Machine Learning-Based Risk Modeling for Operational Performance and Cost Optimization in Fecal Sludge Treatment Plants: A Decision Support Framework for Urban Sanitation

Urban sanitation services in many low- and middle-income countries face growing operational and financial risks due to rapid urbanization, inadequate monitoring systems, and inefficient resource allocation. Fecal sludge treatment plants (FSTPs) are particularly vulnerable to fluctuating sludge inflows, equipment failures, rising operational costs, and limited data-driven decision-making. These challenges reduce treatment efficiency and threaten the sustainability of sanitation services.

This study proposes a machine learning-based framework to model operational and economic risks in FSTPs and support evidence-based decision-making, using SECA Cameroon as a case study. The framework integrates operational, financial, and environmental data to identify key factors affecting plant performance and operating costs. Machine learning algorithms are applied to predict risk scenarios, estimate cost variations, and detect patterns associated with reduced treatment efficiency.

The proposed framework enhances infrastructure management through predictive maintenance, optimized resource allocation, and strategic planning. It also supports the digital transformation of sanitation services by providing an intelligent decision-support system capable of generating timely recommendations for plant managers and policymakers.

This research addresses the urgent need for data-driven risk management in rapidly urbanizing African cities, where financial constraints and operational uncertainties often compromise service delivery. By combining machine learning with data analytics, the framework demonstrates how artificial intelligence can strengthen operational resilience, improve cost efficiency, and support sustainable urban sanitation management.

The study contributes a scalable and adaptable decision-support model that can be implemented in different sanitation contexts. Its findings provide practical insights for utility operators, policymakers, and researchers seeking to improve infrastructure performance through next-generation risk modeling and data-driven decision-making.