An Adaptive Learning-Based Model for Water Quality Assessment in Aquaculture
提出两阶段方法，先用模糊推理系统标记水质类别，再用自适应神经模糊系统预测，结合专家知识和数据学习，提升水质评估的准确性和可解释性。
Water quality monitoring is critical for sustainable aquaculture, as it directly affects fish health, growth, and productivity. Traditional monitoring methods, reliant on manual sampling and laboratory testing, are labor-intensive, time-consuming, and subjective. Although numerous artificial intelligence (AI) techniques have been proposed to forecast individual water quality parameters, the challenge of assessing overall water quality by combining multiple sensor inputs into a meaningful quality category remains underexplored. To address this gap, we propose a two-stage approach consisting of two complementary systems: a Mamdani–Assilian fuzzy inference system (MAFIS) for water quality labeling and a Takagi–Sugeno–Kang compact adaptive neuro-fuzzy system (TSK-CANFS) for predictive modeling. MAFIS utilizes expert-defined membership functions to label water quality based on individual parameter values, effectively generating labeled datasets for training. TSK-CANFS, on the other hand, leverages a novel rule generation mechanism and the sliding window approaches to construct a reduced fuzzy rule base for efficient and accurate water quality prediction. Experiments on real-world water quality datasets demonstrate that the proposed MAFIS and TSK-CANFS achieve competitive performance compared to state-of-the-art methods. Combining data-driven learning with expert-defined reasoning, our approach enhances interpretability, scalability, and accuracy, offering a practical solution for sustainable aquaculture management.