Occurrence of Harmful Blue-green Algae at Algae Alert System and Water Quality Forecast System Sites in Daegu and Gyeongsangbuk-do between 2012 and 2019

Objectives:We analyzed the occurrence of blue-green algae at Algae Alert System and Water Quality Forecast System sites in Daegu and Gyeonsangbuk-do between 2012 and 2019.Methods:Data from 17 sites in Daegu and Gyeonsangbuk-do were collected and reclassified to 8 Nakdong river (ND) sites and 9 water source (WS) sites. Influencing factors on the occurrence of blue-green algae were investigated.Results and Discussion:At the ND sites, blue-green algae were observed in the range of 0-495,360 cells/mL. Between 2012 and 2019, the average number of blue-green algae increased as the sites go downstream from ND-1 (Sangju weir) to ND-8 (Dalseong weir), while the number of blue-green algae was lower at the site in-between weirs than the site adjacent to weir. At the WS sites, blue-green algae were observed in the range of 0-112,000 cells/mL. The average numbers of blue-green algae in 2014, 2015 and 2017 were higher than those of other years. The dominant species of blue-green algae was Microcystis at all the sites during the summer when the water temperature was high; when the water temperature was low Aphanizomenon had higher rates of dominance. Water temperature was positively correlated with the number of blue-green algae grown, while the dissolved oxygen concentration was found to have a negative correlation with it. pH and chlorophyll-a were less correlated.Conclusions:In Daegu and Gyeongsangbuk-do, the main stream of the Nakdong river showed higher blue-green algae occurrence than the water source sites. In most cases, Microcystis was dominant species. In the main stream of Nakdong river, blue-green algae tended to occur more downstream, and the number of blue-green algae was higher at sites close to weir than sites in-between weirs. The occurrence of blue-green algae was highly related to water temperature.

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

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.

Application of Self-Adaptive Exponential Smoothing Method in the Water Quality Forecast of Poyang Lake

The smoothing parameter is a constant when forecasting water quality using exponential smoothing, which usually renders the error to be enlarged, but the assumption of constant is out of accord with the practice. Based on the deep analysis of deficiency of traditional exponential smoothing, this paper establishes self-adaptive exponential smoothing model and compares the forecast result. It is proved that the dynamic characteristic of water quality can be better reflected and the forecasting precision can be improved further by self-adaptive exponential smoothing model.