Water Leakage Detection Using Neural Networks

Application of Software and Hardware-Based Technologies in Leaks and Burst Detection in Water Pipe Networks: A Literature Review

“…To do this, Dawood and Elwakil [33] suggested and validated an accelerometer-based leak detection method with a precision of 98.25% based on SVM, decision tree, and Nave Bayes [78]. SVM still produced fewer performance indicators than the other two algorithms [76], with a higher deviation rate and poorer precision. In contrast, Nasir and Mysorewala [79] noted in their research on leak size detection and estimation that SVM demonstrated lower sensitivity values and higher stability to noise escalation than ANNs.…”

“…The system was found to be an efficient and practical tool for online burst detection in water distribution systems and has the potential to reduce water use and enhance customer service [75]. Shah and Sabu [76] proposed a cost-effective way to detect leaks and manage pressure, which in turn resulted in significant water savings and reduced pipe breakage frequencies. This technique was specially designed for older infrastructure systems and aimed to solve the demand issues for well-equipped and maintained dwellings.…”

“…This shows that the performance of the complex SOM-based model is comparable to that of the less complex MLP-based model. Thus, the suggested approach can be used to stop losses in a pipeline system in intelligent water management systems, guaranteeing the preservation of the natural resource [76].…”