Using Data-Base Deep Learning Artificial Intelligence in Leak Detection for Sustainable Water Resources Management

To store and make use of measurement data, water supply systems have been implementing cutting-edge infrastructure and utilization technologies brought about by the Fourth Industrial Revolution. From this vantage point, it is evident that the use of leak detection technologies in water supply networks will be crucial for the proper management of sustainable water resources and the provision of clean drinking water worldwide in the future. Given the state of the art, leak detection in underground pipelines is seen as a particularly difficult area of study. Given the state of the art, however, studying leaks in underground pipes is considered an extremely difficult task. This work used inflow meter data and deep learning technology to create a data-driven leak detection program. Python and the Google Collaboratory were used to design an RNN/LSTM (Recurrent Neural Networks Long Short-Term Memory models), then multiple threshold-based models were employed to narrow the deep learning and false prediction range (a big data analysis tool). Modules for extracting flow pattern shapes, predicting such shapes with an RNN-LSTM, and establishing thresholds make up the created model. After testing the generated model with real-world leakage accident data, we assessed its efficacy. The outcome was that the leak was detected at most locations shortly after the incident. Leak detection performance was measured using a Confusion matrix, and it was shown to have an accuracy of greater than 90% outside of singularities. As a result, the developed model can serve as a crucial piece of software technology to proactively identify different kinds of issues that are occurring right now with the introduction of smart water infrastructure that can be considered generally and in Bahrain. Since it is built on an expert system, this model can adapt to a wide range of operational contexts, Additionally, it is incredibly scalable and can accurately represent the results of pipe network assessments in a range of settings.