Architecting Predictive Workforce Intelligence for Sustainable Stormwater Management Using Artificial Intelligence and Machine Learning

Abstract

Sustainable stormwater management increasingly depends not only on the technical sophistication of hydrological infrastructure but on the human workforce capacity required to design, deploy, and sustain that infrastructure over its operational lifecycle. While prior research has separately examined machine learning applications in hydrological forecasting and in general workforce management, the deliberate architectural design of predictive workforce intelligence systems purpose-built for the stormwater management domain remains an underdeveloped area of inquiry. This paper presents a systematic architectural framework for predictive workforce intelligence in sustainable stormwater management, conceptualizing workforce capacity not as a peripheral operational constraint but as a first-class design variable within artificial intelligence (AI) and machine learning (ML)-driven environmental infrastructure systems. We define predictive workforce intelligence as the systematic application of AI/ML methods to forecast, optimize, and sustain the human capital systems—technical competencies, field deployment capacity, occupational safety, and long-term career sustainability—required for resilient stormwater infrastructure management under conditions of climate volatility and demographic workforce transition. The proposed architecture comprises four functional layers: a Workforce Data Fabric integrating heterogeneous human capital and infrastructure data sources; a Predictive Intelligence Layer applying supervised learning, survival analysis, and natural language processing to competency forecasting, attrition prediction, and deployment optimization; a Sustainability Optimization Layer applying multi-objective optimization balancing hydrological performance, workforce well-being, and long-term institutional capacity; and a Governance and Trust Layer ensuring explainability, equity, and labor ethics compliance. We examine the application of this architecture across the workforce lifecycle—recruitment and competency development, deployment and scheduling, retention and career sustainability, and knowledge transfer and succession planning—and present implementation evidence from pilot programs in three institutional contexts of varying technical maturity.