Beyond Point Sampling: AI-Enabled Sensor Fusion and Holistic Frameworks for Aquatic System Intelligence

Authors

  • Xiaochen Zhang

    School of Computer Science, Faculty of Engineering, The University of Sydney, Sydney 2006, Australia

DOI:

https://doi.org/10.30564/jees.v8i5.13252
Received: 8 February 2026 | Revised: 5 April 2026 | Accepted: 10 April 2026 | Published Online: 6 May 2026

Abstract

The fast-changing environment is becoming a greater influence on aquatic systems, and the traditional approaches still look at conventional monitoring as point-based sampling, which under-samples the spatiotemporal variation and episodic dynamics. This review summarizes future trends toward ultraviolet sampling using Artificial Intelligence (AI)-enhanced sensor fusion and comprehensive conceptualizations of the intelligence of aquatic systems. We then explore how aquatic observation has evolved over the years by beginning with the limitations of fixed stations and grab sampling and moving on to multi-modal observations involving the incorporation of in-situ networks, remote sensing, and mobile autonomous systems. Subsequently, we are interested in AI methods that allow integration of heterogeneous streams of data with an accent on fusion architectures, representation learning, and hybrids that are based on data-driven inference and physics-based constraints. We elaborate on these developments with holistic models that combine sensor fusion with system-level modeling, such as digital aquatic twins, real-time assimilation, and adaptive spatiotemporal intelligence for detecting events and understanding processes. In a variety of uses, including water quality evaluation, ecosystem health surveillance, and hazards early warning, among others, we point out how the combination of AI and AI-sensing systems can be used to enhance state estimation, forecasting, and decision support in the face of uncertainty. Lastly, we discover unresolved issues, such as a lack of data, no stationarities in generalization, the limitations of computational and operational projects, and the need to have trustworthy, scalable aquatic intelligence, and outline future research opportunities. This review brings together sensing, AI, and systems thinking to create a roadmap on how to transform heterogeneous observations into actionable insights that result in resilient aquatic management.

Keywords:

Aquatic System Intelligence; Sensor Fusion; Multi-Modal Sensing; Digital Twins; Physics-Informed Machine Learning

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How to Cite

Zhang, X. (2026). Beyond Point Sampling: AI-Enabled Sensor Fusion and Holistic Frameworks for Aquatic System Intelligence. Journal of Environmental & Earth Sciences, 8(5), 18–30. https://doi.org/10.30564/jees.v8i5.13252

Issue

Vol. 8 , Iss. 5 (May 2026): In Progress

Article Type

Review

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Copyright © 2026 Xiaochen Zhang

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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