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arXiv Machine Learning By Pramod Lekhak, Chetan Sharma, Hakan Ba\c{s}a\u{g}ao\u{g}lu, F. Paul Bertetti, Debaditya Chakraborty

Predictors and Orchestrators: Parsimonious Machine Learning within an Agentic AI Harness for Multi-Horizon Karst Aquifer Forecasting

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The study presents a deployment‑aware framework for forecasting spring discharge and groundwater levels in the Edwards Aquifer over 1‑12 week horizons using 79 years of hydroclimatic data. Five machine‑learning families—extreme gradient boosting, extremely randomized trees, LSTM, CNN, and Transformers—were compared, with extreme gradient boosting consistently delivering the highest reliability (R² ≥ 0.94) and strong agreement with operational drought thresholds. The validated models were integrated into a five‑agent operational architecture that automates data acquisition, model selection, prediction, threshold monitoring, verification, literature retrieval, and reporting.

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