This study demonstrates the evolution from traditional geoscientific methods to data-driven approaches for determining the tectonic setting of peridotites using their chemical composition. It introduces an application of Machine learning (ML) methods, including unsupervised learning and dimensionality reduction, to identify the tectonic setting of peridotite samples using their mineral chemistry. Integrating mineral chemistry analysis with ML overcomes inefficiencies in processing the vast datasets generated by electron probe micro-analyzers (EPMA). Peridotites from the abyssal (oceanic lithosphere) and fore-arc (subduction) zones provide important insights into the composition and dynamics of the upper mantle, such as melt generation and mantle convection processes, interactions between tectonic plates, and oceanic crust generation. This study evaluates the efficacy of established mineral chemistry diagrams in discerning the tectonic settings of peridotite samples from ophiolitic fragments. Initial evaluations revealed that several conventional diagrams could classify abyssal and fore-arc peridotites, but significant misclassifications occurred, necessitating improved methods. We propose new diagrams utilizing spinel and olivine compositions against the pyroxenes' Al 2 O 3 content, showing enhanced discrimination capabilities. Further, an ML approach was implemented, successfully categorizing abyssal and fore-arc settings with high accuracy (92–96 % based on a 20 % test subset) using olivine and spinel compositional data, outperforming traditional methods. This work highlights the value of ML workflows in supporting petrological analysis for accurate tectonic setting classification. • The paper introduces a new algorithm for automating mineral chemistry data processing. • A new method focusing on the essential phase's composition for the mantle rock generation setting. • The algorithm can discriminate between the abyssal and fore-arc settings more successfully than the traditional methods. • We proposed a new set of diagrams for peridotite classification for the first time, too.

Forecasting water availability is becoming increasingly vital due to rising human demands and changing climatic pressures have caused declines in groundwater levels across many regions. While numerous studies employ data-driven approaches to predict groundwater fluctuations using meteorological data and groundwater level observations, few incorporate measurements from the vadose zone into predictive models. This study proposes a novel method leveraging an advanced hydrogeological monitoring system with high spatio-temporal resolution to forecast groundwater levels in a shallow alluvial aquifer used for drinking purposes. The monitoring system comprises a thermo-pluviometric station and three probes that measure soil water content, electrical conductivity, and temperature at depths of 0.6, 0.9, and 1.7 meters, in addition to a piezometer with a permanent water-level sensor. Data was collected at 15 min intervals over two hydrological years and integrated as exogenous inputs to enhance model predictive performance. Statistical, machine learning and deep learning architectures were tested through ARIMA, SARIMAX, Prophet and NeuralProphet providing a comprehensive evaluation of different approaches. For a robust evaluation, a rolling K-fold cross-validation strategy was implemented and coupled with a grid search to fine-tune all the models. Evaluation metrics and correlation coefficients are employed to assess the predictive capabilities of each model. Our findings indicate that prediction accuracy improves across all models with increasing depth in the vadose zone, with machine learning and deep learning models showing the most significant improvements. Specifically, at 1.7 m depth, Prophet achieves a MAPE of 4.5%, and NeuralProphet achieves a MAPE of 4.1% compared to statistical models. This study has successfully highlighted the enhancement of AI-based models for estimating levels of groundwater incorporating subsurface information from the vadose zone at different depths and phreatic zones, alongside climatic variables. • High-resolution vadose zone data for improved groundwater level forecasting • Vadose zone depth impact on prediction accuracy in machine and deep learning models. • Advanced hydrogeological monitoring with high-resolution real-time data collection. • AI forecasting validation for groundwater management in climate-vulnerable regions.