Abstract
Water quality forecast is a critical part of water security management. Spatiotemporal and multifactorial variations make water quality very complex and changeable. In this article, a novel model, which was based on back propagation neural network that was optimized by the Cuckoo Search algorithm (hereafter CS-BP model), was applied to forecast daily water quality of the Middle Route of South-to-North Water Diversion Project of China. Nine water quality indicators, including conductivity, chlorophyll content, dissolved oxygen, dissolved organic matter, pH, permanganate index, turbidity, total nitrogen, and water temperature were the predictand. Seven external environmental factors, including air temperature, five particulate matter (PM2.5), rainfall, sunshine duration, water flow, wind velocity, and water vapor pressure were the default predictors. A data pre-processing method was applied to select pertinent predictors. The results show that the CS-BP model has the best forecast accuracy, with the Mean Absolute Percentage Errors (MAPE) of 0.004%–0.33%, and the lowest Root Mean Square Error (RMSE) of each water quality indicator in comparison with traditional Back Propagation (BP) model, General Regression Neural Network model and Particle Swarm Optimization-Back Propagation model under default data proportion, 150:38 (training data: testing data). When training data reduced from 150 to 140, and from 140 to 130, the CS-BP model still produced the best forecasts, with the MAPEs of 0.014%–0.057% and 0.004%–1.154%, respectively. The results show that the CS-BP model can be an effective tool in daily water quality forecast with limited observed data. The improvement of the Cuckoo Search algorithm such as calculation speed, the forecast errors reduction of the CS-BP model, and the large-scale impacts such as land management on different water quality indicators, will be the focus of future research.
Highlights
Water pollution and deteriorating water quality have become serious threats to humans and the ecosystem [1]
The result of Scenario 1 showed that using the data pre-processing method to select the predictors for the model was effective that could improve the forecast accuracy of the model, 9 water quality indicators that used the selected predictors were all less than the default predictors
The forecast results of the CS-Back Propagation (BP) model were satisfactory, which have the lowest Root Mean Square Error (RMSE) and Mean Absolute Percentage Errors (MAPE) values in all water quality indicators among the four models in Scenario 2
Summary
Water pollution and deteriorating water quality have become serious threats to humans and the ecosystem [1]. According to the strictest water resources management system of China, the water quality compliance rate of important water functional areas. The research on establishing efficient and accurate water quality forecast models has become a hotspot of water environmental science in recent years [4]. Water quality forecast models are mathematical models that describe the processes of transport and fate of physical, chemical and biological water bodies, the internal laws and relationships of pollutants or water quality indicators with mathematical methods [5]. Many models and methods have previously been applied to forecast water quality in previous research [6]. According to different theoretical bases, these models can be divided into the mechanism model and non-mechanism model [7]
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