Water Quality Prediction Model of a Water Diversion Project Based on the Improved Artificial Bee Colony–Backpropagation Neural Network

Siyu Chen,Yuhong Zhang,Xianfeng Huang,Guohua Fang

doi:10.3390/w10060806

Abstract

Prediction of water quality which can ensure the water supply and prevent water pollution is essential for a successful water transfer project. In recent years, with the development of artificial intelligence, the backpropagation (BP) neural network has been increasingly applied for the prediction and forecasting field. However, the BP neural network frame cannot satisfy the demand of higher accuracy. In this study, we extracted monitoring data from the water transfer channel of both the water resource and the intake area as training samples and selected some distinct indices as input factors to establish a BP neural network whose connection weight values between network layers and the threshold of each layer had already been optimized by an improved artificial bee colony (IABC) algorithm. Compared with the traditional BP and ABC-BP neural network model, it was shown that the IABC-BP neural network has a greater ability for forecasting and could achieve much better accuracy, nearly 25% more precise than the BP neural network. The new model is particularly practical for the water quality prediction of a water diversion project and could be readily applied in this field.

Highlights

Water shortage and water resource pollution have become major problems in China
This study focuses on whether an improved artificial bee colony (IABC)-BP model can be used to forecast water quality under normal conditions in the Eastern Route (ER) project which has stable water quality due to its design and whose water quality can be measured continuously as the indices that influence water quality are measurable
Compared with the ABC-BP model, the IABC-BP model which can increase the forecasting performance of the ABC-BP by searching for the best value of each connection weight and threshold has better network stability, higher learning speed, and stronger approximation ability

Summary

Introduction

Water shortage and water resource pollution have become major problems in China. In order to solve the problem of water resource imbalance, water diversion projects in many areas have been constructed. Water quality is the key to the success of a water diversion project. The water quality prediction is of great significance to the planning and control of water quality. In order to make the plan for water pollution prevention and control, it is necessary to predict the changes of water quality at different pollution levels in the future so as to formulate a reasonable plan. For a water diversion project, it is more important to predict the water quality because quite a significant amount of the water is transferred for solving daily drinking problems. It is of great significance to explore the methods of water quality prediction in the present society

Methods

Results

Conclusion