the application of machine learning algorithms with a fuzzy approach to investigating faults in the control system of consumable water resources

Nowadays, proper management of water resources is essential due to the scarcity of freshwater resources and the high cost of wastewater treatment. to control and optimize while avoiding human mistakes, it is necessary to design and implement an intelligent automated system to minimize human intervention. on the other hand, inevitable deficiencies in system equipment require fault detection and localization methods, all of which involve time and cost. nevertheless, these costs can be reduced by examining faults in the design phase before entering the implementation phase. this article uses a simulated sample system to demonstrate the method's effectiveness. in this way, system faults in the design phase are predicted through machine learning methods. the system's tolerance for dealing with them is evaluated using fuzzy approaches. the proposed approach consists of a process-oriented framework comprising offline and online phases.