Search for: All records

Artificial intelligence (AI) is increasingly used in hydrogeological analysis for forecasting groundwater levels and properties, discovering hydrochemical facies and anomalies, and modelling spatio-temporal signals. Current practice emphasizes spatio-temporal validation, interpretability, and uncertainty quantification, while generative AI (GenAI) is emerging to accelerate data curation, documentation, augmentation, and code prototyping. This report summarizes representative applications of AI and machine learning (ML) in hydrogeology and describes an open, reproducible course developed for university students and professional hydrogeologists. This continuously updated online course is one of the outcomes of the GRANDE-U “Groundwater Resilience Assessment through Integrated Data Exploration for Ukraine” project. It covers Python fundamentals, environment setup, data engineering, and documented case studies in time-series and spatio-temporal modelling. Application examples are drawn from recent teaching materials and GRANDE-U and related studies and include predicting terrestrial water-storage anomalies, groundwater-level forecasting, isotope estimation from routine chemistry, risk or prospectivity mapping, and automated lineament/facies mapping. 

Artificial intelligence (AI) is increasingly used in hydrogeological analysis for forecasting groundwater levels and properties, discovering hydrochemical facies and anomalies, and modelling spatio-temporal signals. Current practice emphasizes spatio-temporal validation, interpretability, and uncertainty quantification, while generative AI (GenAI) is emerging to accelerate data curation, documentation, augmentation, and code prototyping. This report summarizes representative applications of AI and machine learning (ML) in hydrogeology and describes an open, reproducible course developed for university students and professional hydrogeologists. This continuously updated online course is one of the outcomes of the GRANDE-U “Groundwater Resilience Assessment through Integrated Data Exploration for Ukraine” project. It covers Python fundamentals, environment setup, data engineering, and documented case studies in time-series and spatio-temporal modeling. Application examples are drawn from recent teaching materials and GRANDE-U and related studies and include predicting terrestrial water-storage anomalies, groundwater-level forecasting, isotope estimation from routine chemistry, risk or prospectivity mapping, and automated lineament/facies mapping. 

Evaluating public health interventions during disease outbreaks requires an understanding of the spatial patterns underlying epidemiological processes. In this study, we explore how Large Language Models (LLMs) can leverage spatial understanding and contextual reasoning to support spatially-disaggregated epidemiological simulations. We present an approach in which a system dynamics model queries an LLM at key decision points to determine appropriate mitigation strategies, informed by local profiles and the current outbreak status, and incorporates these strategies into the simulations. Through a series of experiments with COVID-19 data from San Diego County, we show how different LLMs perform in tasks requiring spatial adaptation of mitigation strategies, and how incorporating connectivity information through Retrieval-Augmented Generation (RAG) enhances the performance of these customizations. The results reveal significant differences among LLMs in their ability to account for spatial structure and optimize mitigation strategies accordingly. This highlights the importance of selecting the right model and enhancing it with relevant contextual information for effective public health interventions. 

Evaluating public health interventions during disease outbreaks requires an understanding of the spatial patterns underlying epidemiological processes. In this study, we explore how Large Language Models (LLMs) can leverage spatial understanding and contextual reasoning to support spatially-disaggregated epidemiological simulations. We present an approach in which a system dynamics model queries an LLM at key decision points to determine appropriate mitigation strategies, informed by local profiles and the current outbreak status, and incorporates these strategies into the simulations. Through a series of experiments with COVID-19 data from San Diego County, we show how different LLMs perform in tasks requiring spatial adaptation of mitigation strategies, and how incorporating connectivity information through Retrieval-Augmented Generation (RAG) enhances the performance of these customizations. The results reveal significant differences among LLMs in their ability to account for spatial structure and optimize mitigation strategies accordingly. This highlights the importance of selecting the right model and enhancing it with relevant contextual information for effective public health interventions. 

EarthCube Data Discovery Studio (DDStudio) is a crossdomain geoscience data discovery and exploration portal. It indexes over 1.65 million metadata records harvested from 40+ sources and utilizes a configurable metadata augmentation pipeline to enhance metadata content, using text analytics and an integrated geoscience ontology. Metadata enhancers add keywords with identifiers that map resources to science domains, geospatial features, measured variables, and other characteristics. The pipeline extracts spatial location and temporal references from metadata to generate structured spatial and temporal extents, maintaining provenance of each metadata enhancement, and allowing user validation. The semantically enhanced metadata records are accessible as standard ISO 19115/19139 XML documents via standard search interfaces. A search interface supports spatial, temporal, and text‐based search, as well as functionality for users to contribute, standardize, and update resource descriptions, and to organize search results into shareable collections. DDStudio bridges resource discovery and exploration by letting users launch Jupyter notebooks residing on several platforms for any discovered datasets or dataset collection. DDStudio demonstrates how linking search results from the catalog directly to software tools and environments reduces time to science in a series of examples from several geoscience domains. URL: datadiscoverystudio.org 

Science gateways, also known as advanced web portals, virtual research environments, and more, have changed the face of research and scholarship over the last two decades. Scholars world-wide leverage science gateways for a wide variety of individual research endeavors spanning diverse scientific fields. Evaluating the value of a given gateway to its constituent community is critical in obtaining the financial and human resources to sustain gateway operations. Accordingly, those who run gateways must routinely measure and communicate impact. Just as gateways are varied, their success metrics vary as well. In this survey paper, a variety of different gateways briefly share their approaches.