Monitoring the quality of ground water in pipelines using deep neural network model

Abstract

Monitoring the water quality in pipelines is crucial to guaranteeing that consumers receive safe and hygienic water. Conventional monitoring techniques rely on labor-intensive, time-consuming laboratory testing and physical sampling. This study aims to introduce an integrated LSTM-BP neural network model for real-time water quality monitoring in pipelines, showcasing improved predictions for parameters like dissolved oxygen and pH. This paper investigated the results of real-time Water Quality (WQ) monitoring in pipelines using several Deep Neural Network (DNN) models and pre-processing techniques for the information being provided. This paper proposes an integrated long-short-term memory (LSTM) and backpropagation (BP) neural network to offer an I-LSTM-BP model for Monitoring Water Quality in Pipelines (MWQP). The performance of predictions has been enhanced through the integration of LSTM and BP neural networks, particularly for time-series forecasting applications like MWQP. The model may use the LSTM network’s memory and adaptability while still keeping the BP network’s capacity to simulate non-linear interactions and enhance performance during generalization. Dissolved oxygen (DO; mg/L) and hydrogen (pH) potential are the parameters considered during the MWQP process. Information collected from China’s autonomous water quality monitoring units has been used to examine the water quality characteristics thoroughly. The forecasting model has been trained and evaluated using data collected from monitoring stations from June 2018 to December 2022. Statistical measures such as the correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), and their normalized equivalents have been used to evaluate the DNN model’s performance throughout the training and testing stages. The results of this work demonstrate the viability and efficiency of using the proposed I-LSTM-BP-based DNN for MWQP applications. These results show the I-LSTM-BP model’s significant performance benefit, which makes it the model of choice for precise MWQP parameter prediction and WQ evaluation.