Using an Artificial Neural Network Model for Early Warning of Water Quality

Abstract

Water quality early warning model is a key technology for building intelligent decision-making and environmental management systems in the era of big data. In recent years, the improvement of automatic water quality monitoring capabilities and the high demand for ecological models in measurement and management coordination have prompted researchers to explore new modeling methods and strive to improve model predictions. Among them, the artificial neural network (ANN) model has developed rapidly. This paper summarizes the development history and structural characteristics of three types of ANN models, and also integrates the ANN model with water quality data and soft measurement data. It summarizes the anomaly detection, time series forecasting and other research processes, and introduces the general modeling process, technical proposals, and commonly used indicators of model performance. Research shows that the use of ANN models depends on the quality of monitoring data, the interpretability of the model is poor, and the hardware resources required to run the model are required. It is necessary to accelerate the coordinated development and business application of water environment monitoring technology and early warning model, carry out technical iteration through testing various application scenarios, and form an online water quality system, intelligent early warning and emergency management system based on big data, which will help modernize China's environmental management capabilities.