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Shrimp-rice systems in Vietnam’s Mekong Delta face volatile water quality, rising energy costs, and underutilized nutrient waste. We present a hybrid fuzzy-GNN-MOERL-circular framework that unifies prediction and prescription for sustainable control. First, fuzzy spatiotemporal utility mining extracts interpretable early-warning rules under sensor uncertainty. Second, a graph neural encoder captures inter-pond/canal dependencies to form compact states. Third, multi-objective evolutionary RL (NSGA-II/MOEA-D) discovers Pareto policies that balance yield, energy, CO \(_2\) , and reuse, with fuzzy rules enabling safety-aware reward shaping and action masking. Finally, a graph-based circular-allocation module optimizes sludge/effluent routing to rice fields and biogas units. On ten Mekong farms, our approach improved early-warning F1 by +19% with \(-31\%\) false alarms, and achieved −18% energy and −22% CO \(_2\) compared with a yield-only PPO controller and rule-based management, with a modest yield trade-off. Circular allocation raised sludge reuse from 45 \(\rightarrow \) 83% while cutting transport cost by 26%. An XAI dashboard (SHAP + rule summaries) increased expert trust and auditability. The results demonstrate a practical, explainable pathway to low-carbon, circular aquaculture at the smallholder scale and towards the cooperative scale.

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Hybrid Fuzzy Utility Mining with Graph-Reinforcement Learning for Circular Shrimp-Rice Farming

  • 会议论文

  • 发表日期：

    2026年

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  • Tan Duy Le,
  • Duc Dat Pham,
  • Kha Tu Huynh,
  • Hong Quan Nguyen,
  • Minh Tu Nguyen

摘要

Shrimp-rice systems in Vietnam’s Mekong Delta face volatile water quality, rising energy costs, and underutilized nutrient waste. We present a hybrid fuzzy-GNN-MOERL-circular framework that unifies prediction and prescription for sustainable control. First, fuzzy spatiotemporal utility mining extracts interpretable early-warning rules under sensor uncertainty. Second, a graph neural encoder captures inter-pond/canal dependencies to form compact states. Third, multi-objective evolutionary RL (NSGA-II/MOEA-D) discovers Pareto policies that balance yield, energy, CO \(_2\) , and reuse, with fuzzy rules enabling safety-aware reward shaping and action masking. Finally, a graph-based circular-allocation module optimizes sludge/effluent routing to rice fields and biogas units. On ten Mekong farms, our approach improved early-warning F1 by +19% with \(-31\%\) false alarms, and achieved −18% energy and −22% CO \(_2\) compared with a yield-only PPO controller and rule-based management, with a modest yield trade-off. Circular allocation raised sludge reuse from 45 \(\rightarrow \) 83% while cutting transport cost by 26%. An XAI dashboard (SHAP + rule summaries) increased expert trust and auditability. The results demonstrate a practical, explainable pathway to low-carbon, circular aquaculture at the smallholder scale and towards the cooperative scale.