Abstract
Ensuring the classification of water bodies suitable for fish habitat is essential for animal preservation and commercial fish farming. However, existing supervised machine learning models for predicting water quality lack specificity regarding fish survival. This study addresses this limitation and presents a novel model for forecasting fish viability in open aquaculture ecosystems. The proposed model combines reinforcement learning through Q-learning and deep feed-forward neural networks, enabling it to capture intricate patterns and relationships in complex aquatic environments. Moreover, the model’s reinforcement learning capability reduces the reliance on labeled data and offers potential for continuous improvement over time. By accurately classifying water bodies based on fish suitability, the proposed model provides valuable insights for sustainable aquaculture management and environmental conservation. Experimental results show a significantly improved accuracy of 96% for the proposed DQN-based model, outperforming existing Gaussian Naive Bayes (78%), Random Forest (86%), and K-Nearest Neighbors (92%) classifiers on the same dataset. These findings highlight the effectiveness of the proposed approach in forecasting fish viability and its potential to address the limitations of existing models.
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Data availability
The data that support the findings of this study are available on request from the corresponding author. The data is not publicly available due to privacy or ethical restrictions. Our dataset is privately uploaded to the Kaggle database AT the below given link and will be provided or granted permission to access, if received any email requests received from the editors/reviewers/readers. After our complete research, we will publish it as a public data set, to be accessed free of cost.
https://www.kaggle.com/datasets/9487bd31c35fc7f74fa5265f94fb033722797dfe9be909d7f45c66ad2771dfc1
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We are grateful to the National Institute of Technology Raipur for encouraging us and moral support to work on research and innovation.
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All the authors have equal contribution for the article. Shruti Agrawal - Concepts, development of methodologies, experimentation, results analysis, and writing the original draft. Sonal Dubey - Concepts, development of methodologies, experimentation, results analysis, and writing the original draft. K Jairam Naik - Design and assembling the Sensor and Arduino board, Data collection, results analysis, document reviews and refining, editing and overall supervision.
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Agrawal, S., Dubey, S. & Naik, K.J. Deep reinforcement learning for forecasting fish survival in open aquaculture ecosystem. Environ Monit Assess 195, 1389 (2023). https://doi.org/10.1007/s10661-023-11937-9
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DOI: https://doi.org/10.1007/s10661-023-11937-9