Deep-Learning-Based Water Quality Monitoring and Early Warning Methods: A Case Study of Ammonia Nitrogen Prediction in Rivers

Abstract

In line with rapid economic development and accelerated urbanization, the increasing discharge of wastewater and agricultural fertilizer usage has led to a gradual rise in ammonia nitrogen levels in rivers. High concentrations of ammonia nitrogen pose a significant challenge, causing eutrophication and adversely affecting the aquatic ecosystems and sustainable utilization of water resources. Traditional ammonia nitrogen detection methods suffer from limitations such as cumbersome sample handling and analysis, low sensitivity, and lack of real-time and dynamic feedback. In contrast, automated monitoring and ammonia nitrogen prediction technologies offer more efficient methods and accurate solutions. However, existing approaches still have some shortcomings, including sample processing complexity, interference issues, and the absence of real-time and dynamic information feedback. Consequently, deep learning techniques have emerged as promising methods to address these challenges. In this paper, we propose the application of a neural network model based on Long Short-Term Memory (LSTM) to analyze and model ammonia nitrogen monitoring data, enabling high-precision prediction of ammonia nitrogen indicators. Moreover, through correlation analysis between water quality parameters and ammonia nitrogen indicators, we identify a set of key feature indicators to enhance prediction efficiency and reduce costs. Experimental validation demonstrates the potential of our proposed approach to improve the accuracy, timeliness, and precision of ammonia nitrogen monitoring and prediction, which could provide support for environmental management and water resource governance.