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<p>Water quality prediction and management are crucial for ensuring the sustainability of water supplies. Contaminated water can harm humans and aquatic life. As the demand for seafood grows, the aquaculture industry faces several obstacles, including disease management, feeding optimization, water quality monitoring, and aquaculture area extraction. Recently, aquaculture systems have increasingly used AI techniques to successfully and sustainably handle these issues. However, traditional AI techniques such as random forest (RF) and multi-layer perceptron (MLP) among others frequently face data scarcity and poor physical consistency. This research bridges this gap by integrating physical sciences with AI algorithms through the solution of the two coupled pollution–aeration equations to generate a high-fidelity physics-derived dataset of 50,000 observations over an extended spatial domain ranging from 0 to 4. This dataset is then used to train a novel hybrid RF–MLP algorithm to identify fish-survival zones within a polluted river at a given time, while determining the minimum allowable water velocity and the upstream dissolved oxygen level required to maintain environmentally safe conditions along the entire river reach. The proposed algorithm employs a three-stage sequential residual learning logic, combining RF’s stable feature partitioning with MLP’s improved non-linear error correction. The algorithm’s performance was benchmarked against nine standalone AI algorithms using a comprehensive suite of metrics. The experiments demonstrated exceptional precision with a Correlation Coefficient (CC) of 0.9999999973, a Scatter Index (SI) of 0.00007326, a Willmott’s Index (WI) of 0.9999999986, a Test RMSE of 0.00012966, and a 0.9999999692. Beyond accuracy, the hybrid algorithm demonstrated superior computational efficiency, training in just 22.58&#xa0;s—a 24.45-fold reduction compared to BiLSTM architectures. These results provide a robust tool for decision-makers to identify optimal river reaches for fish farms based on minimum water velocity and permissible dissolved oxygen transfer levels, bridging the gap between theoretical physics and industrial aquaculture management.</p>

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Integrating physical modeling with artificial intelligence for predicting fish survival zones in polluted rivers to maintain a sustainable aquaculture industry

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月18日

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  • Hussein Karam Abd El-Sattar,
  • Mohammed Elshambakey,
  • Ahmed Saleh,
  • Samar Antar

摘要

Water quality prediction and management are crucial for ensuring the sustainability of water supplies. Contaminated water can harm humans and aquatic life. As the demand for seafood grows, the aquaculture industry faces several obstacles, including disease management, feeding optimization, water quality monitoring, and aquaculture area extraction. Recently, aquaculture systems have increasingly used AI techniques to successfully and sustainably handle these issues. However, traditional AI techniques such as random forest (RF) and multi-layer perceptron (MLP) among others frequently face data scarcity and poor physical consistency. This research bridges this gap by integrating physical sciences with AI algorithms through the solution of the two coupled pollution–aeration equations to generate a high-fidelity physics-derived dataset of 50,000 observations over an extended spatial domain ranging from 0 to 4. This dataset is then used to train a novel hybrid RF–MLP algorithm to identify fish-survival zones within a polluted river at a given time, while determining the minimum allowable water velocity and the upstream dissolved oxygen level required to maintain environmentally safe conditions along the entire river reach. The proposed algorithm employs a three-stage sequential residual learning logic, combining RF’s stable feature partitioning with MLP’s improved non-linear error correction. The algorithm’s performance was benchmarked against nine standalone AI algorithms using a comprehensive suite of metrics. The experiments demonstrated exceptional precision with a Correlation Coefficient (CC) of 0.9999999973, a Scatter Index (SI) of 0.00007326, a Willmott’s Index (WI) of 0.9999999986, a Test RMSE of 0.00012966, and a 0.9999999692. Beyond accuracy, the hybrid algorithm demonstrated superior computational efficiency, training in just 22.58 s—a 24.45-fold reduction compared to BiLSTM architectures. These results provide a robust tool for decision-makers to identify optimal river reaches for fish farms based on minimum water velocity and permissible dissolved oxygen transfer levels, bridging the gap between theoretical physics and industrial aquaculture management.