Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning

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Year : 2026 | Volume : 16 | 01 | Page :
    By

    PALISETTI JHNANA PRASUNA ALEKHYA,

  • MANAS KUMAR YOGI,

  1. Undergraduate Student, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India
  2. Assistant Professor, Department of Computer Science and Engineering, Pragati Engineering College (A), Surampalem, Andhra Pradesh, India

Abstract

The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the EIA of OECs, integrating variational quantum circuits (VQCs), quantum neural networks (QNNs), and quantum support vector machines (QSVMs) to process oceanographic, biological, and electromechanical datasets simultaneously. Applied across five OEC technology classes—wave energy converters, tidal stream turbines, ocean thermal energy converters (OTEC), salinity gradient devices, and tidal barrages—the proposed hybrid QML architecture achieves a prediction accuracy of 94.1% and an R² of 0.961 on composite environmental impact scores, outperforming equivalent classical deep learning benchmarks by 12.8 percentage points. Tabular benchmarks, three synthesised figures, and cross-validated performance metrics are presented in support of these findings. The framework demonstrates particular efficacy in predicting electromagnetic field (EMF) exposure corridors, sediment plume dispersion, and underwater radiated noise (URN) thresholds, all identified as critical impact vectors for marine megafauna. The results affirm that QML-driven EIA constitutes a transformative paradigm shift for offshore renewable energy governance and environmental compliance workflows.

Keywords: Ocean Energy Converters; Quantum Machine Learning; Environmental Impact Assessment; Variational Quantum Circuits; Marine Ecology; Tidal Energy; Offshore Renewable Energy

How to cite this article:
PALISETTI JHNANA PRASUNA ALEKHYA, MANAS KUMAR YOGI. Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning. Journal of Energy, Environment & Carbon Credits. 2026; 16(01):-.
How to cite this URL:
PALISETTI JHNANA PRASUNA ALEKHYA, MANAS KUMAR YOGI. Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning. Journal of Energy, Environment & Carbon Credits. 2026; 16(01):-. Available from: https://journals.stmjournals.com/joeecc/article=2026/view=240805


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Ahead of Print Subscription Original Research
Volume 16
01
Received 18/03/2026
Accepted 26/03/2026
Published 24/04/2026
Publication Time 37 Days
37

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