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Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network

1
State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
2
Department of Water Resources and Environment, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Water 2018, 10(10), 1471; https://doi.org/10.3390/w10101471
Received: 21 September 2018 / Revised: 13 October 2018 / Accepted: 15 October 2018 / Published: 18 October 2018
(This article belongs to the Section Water Quality and Contamination)
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. View Full-Text
Keywords: daily water quality forecast; south-to-north water diversion project of China; back-propagation neural network; meta-heuristic algorithm; cuckoo search algorithm daily water quality forecast; south-to-north water diversion project of China; back-propagation neural network; meta-heuristic algorithm; cuckoo search algorithm
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MDPI and ACS Style

Shao, D.; Nong, X.; Tan, X.; Chen, S.; Xu, B.; Hu, N. Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network. Water 2018, 10, 1471. https://doi.org/10.3390/w10101471

AMA Style

Shao D, Nong X, Tan X, Chen S, Xu B, Hu N. Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network. Water. 2018; 10(10):1471. https://doi.org/10.3390/w10101471

Chicago/Turabian Style

Shao, Dongguo, Xizhi Nong, Xuezhi Tan, Shu Chen, Baoli Xu, and Nengjie Hu. 2018. "Daily Water Quality Forecast of the South-To-North Water Diversion Project of China Based on the Cuckoo Search-Back Propagation Neural Network" Water 10, no. 10: 1471. https://doi.org/10.3390/w10101471

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