Prediction of Water Quality in Ponds Based on Temperature, Water Clarity, pH, and Dissolved Oxygen Using Mamdani Fuzzy Logic

Water quality is pivotal for successful fish breeding, particularly in pond-based systems. This paper reviews several studies on water quality assessment in fish ponds, analyzing physical and chemical parameters such as temperature, water clarity, pH, and dissolved oxygen (DO). However, existing studies often need help in precisely evaluating water quality due to uncertainties in the obtained values. This study suggests applying fuzzy logic; specifically, the Mamdani method-to produce more accurate and conclusive assessment values to overcome this problem. Fuzzy logic enables the processing of vague information, overcoming existing uncertainties. The study highlights four key parameters; temperature, water clarity, pH, and dissolved oxygen-consistently influencing water quality assessment. By incorporating Mamdani fuzzy logic into water quality evaluation, this research aims to enhance the accuracy and effectiveness of assessment methods, thereby advancing previous research efforts in fisheries cultivation.