ALAES: An Object-Oriented Knowledge-Based Expert System for Overcoming Data Scarcity in Groundwater Flow Modeling

The preparation of reliable input data for groundwater flow modeling remains a persistent bottleneck in data-scarce regions, where parameter estimation relies heavily on subjective expert judgment. This study introduces ALAES, a novel object-oriented expert system that codifies formal and heuristic knowledge to guide hydrogeologists through the entire pre-modeling workflow. The knowledge base was developed through structured interviews with 20 international experts and formalized using the KOD methodology within the Kappa-PC shell. The system comprises 258 production rules, 114 classes, and 1136 instances. Validation on the data-scarce Rhis-Nekor aquifer in Morocco showed that ALAES recommended MODFLOW and diagnosed modeling feasibility as challenging. A comparative assessment revealed substantial improvements over a baseline model developed without ALAES guidance: spatial resolution increased by a factor of four in critical zones, steady-state water balance consistency improved from 87% to 94%, and mean absolute errors were reduced by over 50% under ±20% perturbations. The system guided parameter estimation, reducing porosity uncertainty by over 40%, and achieved strong transient calibration (R2 = 0.99). Three future management scenarios were evaluated, enabling formulation of a recommended exploitation strategy. By bridging the gap between data availability and modeling requirements, ALAES provides an explicit, reproducible decision-support tool for sustainable groundwater management in data-limited environments worldwide.