Machine Learning-Based Decision Support for Stormwater Management and Workforce Optimization in Environmental Engineering

Abstract

Urban stormwater management has emerged as a critical environmental engineering challenge in the context of intensifying climate volatility, rapid urbanization, and aging municipal infrastructure. Traditional stormwater management relies on static design standards and historical rainfall statistics that are increasingly inadequate for capturing the non-stationary hydrological dynamics of a changing climate, while the engineering workforce responsible for designing, operating, and maintaining stormwater infrastructure faces parallel challenges of skills shortages, inefficient task allocation, and rising operational costs. This paper presents a comprehensive review and integrated decision-support framework that applies machine learning (ML) techniques to two traditionally siloed domains: stormwater infrastructure management and environmental engineering workforce optimization. We examine ML applications across stormwater forecasting and real-time control, including Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRU) for precipitation-runoff modeling, Random Forest and Gradient Boosting models for green infrastructure performance prediction, and reinforcement learning for real-time control of detention basins and combined sewer overflow systems. In parallel, we review ML applications in environmental engineering workforce management, including predictive models for field crew scheduling optimization, skills-gap forecasting, safety incident risk prediction, and workforce well-being monitoring in high-exposure environmental field roles. We propose an Integrated ML Decision Support Architecture (IMDSA) that unifies hydrological prediction, infrastructure asset management, and workforce allocation within a single optimization framework, enabling municipalities and environmental engineering firms to simultaneously improve flood resilience and operational workforce efficiency. Keywords: