Evaluation of water quality and key factors influencing water quality in intensive shrimp farming systems using principal component analysis-fuzzy approach

The evaluation of water quality is a crucial process for preventing and managing adverse events in aquaculture systems. This study aimed to develop a novel water quality index (WQI) using multivariate statistical techniques, including principal component analysis (PCA) and a fuzzy inference system, to assess water quality in two shrimp farming techniques. A total of 1179 daily water samples were analyzed for 17 key parameters: temperature, pH, salinity, total dissolved solids (TDS), turbidity, dissolved oxygen (DO), transparency, alkalinity, hardness, total ammonia nitrogen (TAN), nitrite, nitrate, phosphate, silica, calcium (Ca), magnesium (Mg) and potassium (K). PCA identified salinity and silica as the primary factors contributing to water quality deterioration. The results showed that the two-stage shrimp farming technique had a WQI of 0.70 during the grow-out period and 0.64 during the nursery period, compared to 0.80 and 0.76 for the one-stage technique. Water quality was significantly better during the rainy season, with a WQI of 0.69, compared to 0.77 in the dry season. This study demonstrates the effectiveness of the fuzzy inference system for real-time water quality monitoring and management, offering valuable insights for enhancing shrimp production through timely interventions under polluted conditions.