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1. Artificial intelligence in hydrogeology: applications and an open, reproducible machine learning course

   Samalavičius, Vytautas; Zaslavsky, Ilya (March 2026, Baltica)

   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. 

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2. Data-Driven Time Series Forecasting for Social Studies Using Spatio-Temporal Graph Neural Networks

   https://doi.org/10.1145/3462203.3475929  

   Li, Yi-Fan; Dong, Bo; Khan, Latifur; Thuraisingham, Bhavani; Brandt, Patrick T.; D'Orazio, Vito J. (September 2021, GoodIT '21: Proceedings of the Conference on Information Technology for Social Good)

   Time series forecasting with additional spatial information has attracted a tremendous amount of attention in recent research, due to its importance in various real-world applications on social studies, such as conflict prediction and pandemic forecasting. Conventional machine learning methods either consider temporal dependencies only, or treat spatial and temporal relations as two separate autoregressive models, namely, space-time autoregressive models. Such methods suffer when it comes to long-term forecasting or predictions for large-scale areas, due to the high nonlinearity and complexity of spatio-temporal data. In this paper, we propose to address these challenges using spatio-temporal graph neural networks. Empirical results on Violence Early Warning System (ViEWS) dataset and U.S. Covid-19 dataset indicate that our method significantly improved performance over the baseline approaches. 

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3. (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning

   https://doi.org/10.1145/3672556  

   Azad, Fahim Tasneema; Candan, K Selçuk; Kapkiç, Ahmet; Li, Mao-Lin; Liu, Huan; Mandal, Pratanu; Sheth, Paras; Arslan, Bilgehan; Chowell-Puente, Gerardo; Sabo, John; et al (June 2024, ACM Transactions on Spatial Algorithms and Systems)

   Successfully tackling many urgent challenges in socio-economically critical domains, such as public health and sustainability, requires a deeper understanding of causal relationships and interactions among a diverse spectrum of spatio-temporally distributed entities. In these applications, the ability to leverage spatio-temporal data to obtain causally based situational awareness and to develop informed forecasts to provide resilience at different scales is critical. While the promise of a causally grounded approach to these challenges is apparent, the core data technologies needed to achieve these are in the early stages and lack a framework to help realize their potential. In this article, we argue that there is an urgent need for a novel paradigm of spatio-causal research built on computational advances in spatio-temporal data and model integration, causal learning and discovery, large scale data- and model-driven simulations, emulations, and forecasting, as well as spatio-temporal data-driven and model-centric operational recommendations, and effective causally driven visualization and explanation. We thus provide a vision, and a road map, for spatio-causal situation awareness, forecasting, and planning. 

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4. Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data

   https://doi.org/10.3390/e21020184  

   McDermott, Patrick; Wikle, Christopher (February 2019, Entropy)

   Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications. 

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5. Adaptive Joint Spatio-Temporal Graph Learning Network for Traffic Data Forecasting

   https://doi.org/10.1145/3634913  

   Wang, Tianyi; Chen, Shu-Ching (September 2024, ACM Transactions on Spatial Algorithms and Systems)

   Traffic data forecasting has become an integral part of the intelligent traffic system. Great efforts are spent developing tools and techniques to estimate traffic flow patterns. Many existing approaches lack the ability to model the complex and dynamic spatio-temporal relations in the traffic data, which are crucial in capturing the traffic dynamic. In this work, we propose AJSTGL, a novel adaptive joint spatio-temporal graph learning network for traffic data forecasting. The proposed model utilizes static and adaptive graph learning modules to capture the static and dynamic spatial traffic patterns and optimize the graph learning process. A sequence-to-sequence fusion model is proposed to learn the temporal correlation and combine the output of multiple parallelized encoders. We also develop a spatio-temporal graph transformer module to complement the sequence-to-sequence fusion module by dynamically capturing the time-evolving node relations in long-term intervals. Experiments on three large-scale traffic flow datasets demonstrate that our model could outperform other state-of-the-art baseline methods. 

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