A modeling approach for early-warning of water bloom risk in urban lake based on neural network
ZHENG Jian-feng1,2, JIAO Ji-dong1, SUN Li-ping1,2
1. School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China;
2. Tianjin Key Laboratory of Aquatic Science and Technology, Tianjin 300384, China
Formation process of water bloom was complicated, time-varied and uncertain. So far water bloom prediction of urban lake was still difficult. An integrated modeling approachby using interior-outer-set, rough sets reduction algorithm and RBF neural network model was proposed for early-warning of water bloom risk. Interior-outer-set model was employed to define the threshold of chlorophyll a for predictingwater bloom risk, and a method was put forward for calculating the risk probability of water bloom.Rough sets reduction algorithm was used to identify the keydriving factors ofwater bloom. An early-warning model of water bloom risk was developed based on RBF neural network model. Feasibility of themodeling approach was proved though the application in Qingjing Lake. The results indicated thatthe threshold value of chlorophyll a was 70.98μg/L; water bloom risk was divided into five grades based on the risk probability of water bloom; fourwater quality indexes including water temperature, dissolved oxygen, permanganate index and total dissolved solids were identified as the indicators of water bloom. Result of model validation showed that the RBF neural network model's accurate rate exceeded 85%, and could be applied to early-warning of water bloom risk in Qingjing Lake.
郑剑锋, 焦继东, 孙力平. 基于神经网络的城市内湖水华预警综合建模方法研究[J]. 中国环境科学, 2017, 37(5): 1872-1878.
ZHENG Jian-feng, JIAO Ji-dong, SUN Li-ping. A modeling approach for early-warning of water bloom risk in urban lake based on neural network. CHINA ENVIRONMENTAL SCIENCECE, 2017, 37(5): 1872-1878.
Coad P, Cathers B, Ball J E, et al. Proactive management of estuarine algal blooms using an automated monitoring buoy coupled with an artificial neural network [J]. Environmental Modeling & Software, 2014,61:393-409.
JΦrgensen S E. Structural dynamic model [J]. Ecological Modeling, 1986,31:1-9.
[8]
Kirchner W B, Dillon P J. Comment on an empirical method of estimating the retention of phosphorus in lakes [J]. Water Researches, 1975,2(1):182-183.
[9]
Wool T A, Ambrose R B, Martin J L, et a1. Water Quality Analysis Simulation Program (WASP) Version6. 0, DRAFF: User's Manual [M]. Georgia: U S Environmental Protection Agency-Region, Atlanta. 2001:22-28.
Vivian P, Roberto J C. Qual2E model for the Corumbata'?River [J]. Ecological Modelling, 2006,198:269-275.
[12]
Mehmet Y, Erdal K, Ridvan B. Simulation of river streams: Comparison of a new technique with QUAL2E [J]. Mathematical and Computer Modelling, 2007,46:292-305.
Cracknell A P, Newcombe S K, Black A F, et al. TheABDMAP (algal bloom detection,monitoring and prediction) concerted action [J]. International Journal of Remote Sensing, 2001,22(2/3): 205-247.
Chen Q, Mynett A E. Predicting phaeocystisglobosa bloom in Dutch coastal waters by decision trees and non-linear piecewise regression [J]. Ecological Modeling, 2004,176:277-290.
