HYBRID INTELLIGENCE IN ENVIRONMENTAL SYSTEMS: BRIDGING PHYSICS-BASED MODELING AND DEEP LEARNING FOR INTEGRATED WATER RESOURCE MANAGEMENT AND URBAN PLANNING

Abstract

Nutrient pollution represents a critical water quality challenge requiring accurate prediction of nutrient transport through complex river networks under alternative management scenarios. Traditional process-based models provide mechanistic rigor but face computational limitations constraining comprehensive scenario analysis, while pure machine learning approaches offer efficiency but frequently violate fundamental physical principles and have limited interpretability. This dissertation develops a hybrid intelligence framework that strategically integrates multiple artificial intelligence techniques with physics-based principles to achieve both computational tractability and scientific validity in watershed nutrient modeling.This dissertation contributes a hybrid intelligence framework that strategically integrates multiple AI techniques with physics-based principles to enable comprehensive watershed nutrient assessment under alternative future scenarios. The framework comprises four methodological contributions: (1) an ensemble machine learning approach using Bayesian Model Averaging that provides uncertainty-quantified predictions from natural landscapes; (2) a ConvLSTM-based deep learning framework integrating scenario planning with policy constraints for flexible long-term land use prediction; (3) a Knowledge-Guided Machine Learning architecture embedding agricultural physics to ensure mass balance consistency; and (4) a calibrated SWIFT annual routing system enabling rapid source attribution analysis. The framework comprises four interconnected components. Ensemble machine learning combining Random Forest, Gradient Boosting, and Extreme Gradient Boosting through Bayesian Model Averaging predicts nutrient loads from natural landscapes (forests and rangelands), achieving cross-validation R² values of 0.70-0.83 while reducing prediction uncertainty by 46% compared to individual algorithms. A Convolutional Long Short-Term Memory neural network trained on 39 years of annual land cover data predicts urban spatial expansion through 2050 under five Colorado Water Plan scenarios, revealing that 20-year forecasts (F1 = 0.87) substantially outperform 1-year predictions (F1 = 0.27) when dense temporal sequences enable detection of persistent growth patterns. A Knowledge-Guided Machine Learning framework incorporating physics-informed architecture and scenario-aware loss functions predicts agricultural nutrient loads across management scenarios, reducing mass balance violations by 95% while maintaining Nash-Sutcliffe Efficiency exceeding 0.85. The SWIFT routing model integrates source-specific predictions with wastewater facility data through streamlined mass balance algorithms, achieving computational speedups of approximately 1,900× relative to traditional SWAT+ applications while maintaining calibration performance (NSE > 0.90, R² > 0.91) across 200 monitoring stations. Beyond computational gains, these findings advance scientific understanding: ensemble methods reveal that prediction uncertainty derives primarily from model structural differences rather than parameter uncertainty; the 'Temporal Depth Paradox', where 20-year forecasts outperform 1-year predictions, demonstrates that urban growth follows detectable multi-decadal patterns obscured by short-term stochasticity; and physics-informed constraints serve as beneficial regularization for nutrients, especially in scarce data situations. Application across Colorado watersheds demonstrates that hybrid frameworks enable comprehensive scenario-based assessment in hours rather than weeks, transforming operational feasibility of regional water quality management. Source-specific delivery ratio quantification reveals systematic differences among nutrient sources (wastewater: 0.80; agriculture: 0.74), informing cost-effective management targeting. Results establish design principles for hybrid intelligence systems that preserve physical consistency while enabling practical application at scales relevant for regulatory decision support and strategic planning under uncertainty.