Comparison of generative algorithms for conceptual groundwater modeling of coastal volcanic aquifer features with disparate, sparse and extremely imbalanced data

Abstract

In coastal volcanic aquifers, the reliability of freshwater seawater-exchange simulations are governed by accuracy of the conceptual groundwater model (CGM). The traditional CGMs are constructed by qualitatively combining independent hydrogeophysical features, limiting their ability to capture the complexity of volcanic terrains. To integrate these disparate, sparse, and imbalanced features, we propose an AI-assisted workflow. First, the self-organizing map (SOM) is applied to estimate a deterministic set of transdisciplinary features called the reference model. Second, generative algorithms are applied to the reference model and empirical distributions constructed to obtain sets of stochastic point clouds called the site model. Data quality metrics identify the preferred generative algorithm whose set of stochastic features are mapped using SOM to the groundwater model grid and assigned as the stochastic CGM. The proposed algorithm is applied to extremely imbalanced multiclass features and multiple discrete numerical features observed at the Halawa-Moanalua aquifer, Oahu, Hawaii. At this stie, the Copula Generative Adversarial Network is deemed as the preferred generative algorithm whose set of stochastic transdisciplinary features represent the Halawa-Moanalua CGM. The simulated spatial geologic units correspond to published surface maps; and the simulated conductance, temperature, and barometric pressure profiles correlate with those measured at deep monitoring wells. Inspecting the 3-dimensional conductance models reveal groundwater flow and discharge driven by the aquifer hydraulic gradient, freshwater pumping, seawater intrusion induced by onshore withdrawals, and preferred pathways for freshwater-seawater exchange, such as landward intrusion of seawater and seaward discharge of freshwater.