From Data to Action: AI and Hydroinformatics Applications in Environmental Science

Abstract: This talk uncovers how AI and Hydroinformatics are revolutionizing environmental science through cutting-edge information technologies. We highlight the transformative role of AI and web technologies in augmenting data analysis and predictive capabilities. These technologies enable dynamic simulations and real-time decision-making, essential for tackling environmental challenges. We also explore the digital twins, Large Language Models (LLMs) and Metaverse's potential—leveraging Augmented Reality (AR) and Virtual Reality (VR) to create engaging, immersive experiences for environmental data visualization and communication. Current projects demonstrate how these tools are applied to address challenges in hydrology and disasters. Focusing on future directions and vision, we foresee an integrated approach where cutting-edge information technologies transition data into impactful solutions with unparalleled efficiency. This vision emphasizes the development of a collaborative ecosystem that leverages AI, Hydroinformatics, digital twins, and immersive technologies to empower stakeholders across sectors.

Uncertainty Quantification, Agent-Based Models, and Synthetic Populations

Abstract: Agent-based models (ABMs) use rules at the individual (agent) level to simulate a social, ecologic, or social-technical system, producing structured behavior when viewed at an aggregated level. For example, dynamic network simulation models commonly evolve a very large collection of agents interacting over a network that evolves with time. Such models are often used simulate animal populations, epidemics, or transportation, typically producing random trajectories, even when model parameters and initial conditions are identical. This will be a largely conceptual talk showing some approaches for using such models for parameter estimation and quantifying uncertainty in resulting predictions. If time allows, the talk will also deal some with accounting for uncertainty in synthetic populations which are required for ABMs. 

Why a Multi-Representational Approach is Necessary to Facilitate Prediction, Understanding & Scientific Discovery

Abstract: This talk examines the question “How can we use Models to facilitate Scientific Discovery?”, as opposed to the manner in which models are normally used, which is to generate “Predictions” or to aid (we hope) in the development of “Understanding”. As a starting point, we recognize that the key to the analysis of any data, or development of any model, is the selection of an appropriate “representational system”. The problem, however, is that the representation we chose then completely determine the “questions we can ask”, the nature of “analyses and inferences we can perform”, and the “answers we can obtain”. Consequently, any model we develop using that representational approach may be highly suitable for learning certain things about a system, but completely unsuitable for learning other (possibly equally important) things.

To explore how different representational frameworks can affect what we learn from data, we present a case study in which three complementary models approaches (one physics/process-based and two based in machine-learning methods) are simultaneously used to develop an improved understanding of how catchment-scale hydrological processes vary across the diverse hydro-geo-climatology of Chile.  Based (in part) on these results, we argue that if our goal truly is “Scientific Discovery”, then we would be well served to adopt a “general multi-representational framework” when analyzing data, so that the models we build have the possibility of converging towards accurate and comprehensive representations of the underlying data generating process.  Our view is that such an approach is key to the use of Models in support of improved Prediction, Understanding and Discovery in the Earth & Environmental Sciences.