IJBTT

Generative Data Modelling to Improve Marine Data Predictions

Generative Data Modelling to Improve Marine Data Predictions
	© 2025 by IJETT Journal
	Volume-73 Issue-10
	Year of Publication : 2025
	Author : Agnes Nalini Vincent, K. Sakthidasan, Nassirah Laloo, Uhoze Bagurubumwe
	DOI : 10.14445/22315381/IJETT-V73I10P105

How to Cite?
Agnes Nalini Vincent, K. Sakthidasan, Nassirah Laloo, Uhoze Bagurubumwe,"Generative Data Modelling to Improve Marine Data Predictions", International Journal of Engineering Trends and Technology, vol. 73, no. 10, pp.49-78, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I10P105

Abstract
The ability to project changes in benthic communities based on environmental parameters is vital for constructing resilience in marine ecosystems. This study employs a Deep Recurrent Neural Network (RNN) to predict hard corals and fish assemblages based on water quality parameters. Marine Data of Flic en Flac Lagoon, located in Mauritius, is used for this purpose. The use of Generative Adversarial Network (GAN) and its variants, including Wasserstein GAN, Conditional GAN, and Climatic GAN, to improve the prediction accuracy of Deep RNN is investigated. A state-of-the-art Marine Data GAN (MGAN) has been proposed and investigated. Empirical evidence proves that MGAN minimizes the Wasserstein distance Jensen–Shannon divergence that can exist between the generated and original data distribution, than any other GAN. In contrast, for the pH of water, the Kullback-Leibler (KL) divergence of MGAN is much higher than WGAN, highlighting WGAN's superior performance in capturing the pH distribution. Generated data from MGAN is then used as input to the Deep RNN to perform predictions. This hybrid MGAN Deep RNN model shows substantial improvements across evaluation metrics compared to the basic Deep RNN model, which uses the actual dataset. Specifically, MAE improved by 7.44, RMSE by 8.07, and R² from a negative to a positive value, demonstrating the enhanced predictive accuracy of the hybrid model. Thus, this research identifies MGAN-Deep RNN as the best model for the prediction of marine data under consideration. As an outcome, this research provides valuable insights into the administration of marine ecosystems in the Flic en Flac Region of Mauritius.

Keywords
Deep Recurrent Neural Network, Generative Adversarial Network, Marine data, Prediction, Mauritius.

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