Artificial Neural Networks in the Prediction and Assessment for Water Quality: A Review

Abstract

Water is one of the main elements of the environment, which determines the existence of life on the earth such as humans, aquatic animals, and plants. In order to control the water quality environment more efficiently and intelligently, numerous water quality models have been developed for predicting and evaluating water quality accurately and intelligently. In order to control the water quality environment more effectively and intelligently, artificial neural network (ANN) and the hybrid models that contain it are applied to accurately and intelligently predict and evaluate water quality, improving the reliability and assessment capabilities of water quality prediction. Therefore, this paper is a literature review aimed at analysing and comparing the characteristics and applications of existing artificial neural network models. According to the direction of information transmission in the network, we divide them into feed-forward networks and recurrent networks. In addition, we compare the pros and cons of each model. Our analysis provides guidance for model improvement in future research. Moreover, these models can be applied to aquaculture in the future to promote their development.