Work

From Information Flow to Generative Insights: Advancing AI/ML Applications for Predicting Solute Dynamics in Streams

Year: 2025

Type: conference-paper

Abstract: Predicting and understanding solute dynamics in streams is critical for managing water quality, protecting aquatic ecosystems, and addressing broader hydrological challenges. This presentation explores how the integration of advanced AI and machine learning (ML) methodologies enhances our predictive understanding of solute transport while fostering novel perspectives on system behavior. Beginning with an information-theoretic framework, we demonstrate how causal history analysis captures the interplay of self- and cross-dependencies among environmental variables, unraveling how multivariate interactions sustain the emergent behaviors of solute dynamics. This foundational approach provides a lens to understand the systemic influences that drive variability and long-term trends. Building on this theoretical foundation, we incorporate Long Short-Term Memory (LSTM) models to enhance temporal predictions by embedding solute flux dynamics, highlighting the model’s capacity to bridge the temporal resolution gap and capture rapid fluctuations. To further advance predictive capabilities, we leverage transformer models, which excel in learning contextual dependencies across temporal and spatial scales. By adapting non-stationary transformers for environmental applications, we overcome limitations of stationarity assumptions in traditional models, enabling better predictions of both transient and extreme solute dynamics. These models provide a significant leap in uncovering system behaviors while maintaining robustness across diverse hydrological conditions. However, conventional ML models, even transformers, often lack the capacity to interrogate the mechanisms behind their predictions. To address this, we turn to the transformative potential of generative AI, employing counterfactual reasoning to explore alternative scenarios and push the boundaries of predictive understanding. Finally, we introduce agentic large language models (LLMs) that dynamically interact with data and hypotheses, enabling iterative exploration and facilitating decision-making frameworks that are as responsive as they are insightful. This progression—from information-theoretic approaches to predictive ML models like LSTMs and transformers, to generative and interactive AI—illustrates a seamless integration of methodologies designed to tackle the complexity and nonlinearity inherent in solute transport. By weaving together these complementary perspectives, we offer a holistic framework that not only improves predictive accuracy but also fosters new lines of inquiry into the mechanisms shaping stream solute dynamics. This vision underscores the promise of AI/ML as tools for advancing both scientific discovery and practical solutions in environmental science, paving the way for more informed management of water resources and ecosystems.

Source: ARPHA Conference Abstracts

DOI: 10.3897/aca.8.e155752