Combination of artificial neural network and clustering techniques for predicting phytoplankton biomass of Lake Poyang, China

被引:24
作者
Huang, Jiacong [1 ]
Gao, Junfeng [1 ]
Zhang, Yinjun [2 ]
机构
[1] Chinese Acad Sci, Nanjing Inst Geog & Limnol, Key Lab Watershed Geog Sci, Nanjing 210008, Jiangsu, Peoples R China
[2] China Natl Environm Monitoring Ctr, Beijing 100012, Peoples R China
关键词
Chlorophyll a; Artificial neural network; Clustering; Lake Poyang; Sensitivity analysis; GERMAN LOWLAND RIVER; WATER-QUALITY; CLIMATE-CHANGE; CHLOROPHYLL-A; MODEL; VARIABLES; IMPACTS; SIMULATION; SYSTEM; TAIHU;
D O I
10.1007/s10201-015-0454-7
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
A single artificial neural network (ANN) model is inadequate for predicting phytoplankton biomass in a large lake due to its high spatial heterogeneity. In this study, ANN was combined with a clustering technique to simulate phytoplankton biomass in a large lake (Lake Poyang) using a 7-year dataset. Two ANN models (named ANN_Downstream and ANN_Upstream) were developed for the downstream and upstream areas based on the k-means clustering results of 17 sampling sites at Lake Poyang, China. They performed better than ANN_Poyang (an ANN model for the whole lake), indicating the success of the clustering technique in improving ANN models for predicting phytoplankton biomass in different sub-regions of the large lake. A sensitivity analysis based on ANN_Downstream and ANN_Upstream showed that phytoplankton dynamics responded differently to environmental variables in different sub-regions of Lake Poyang. This case study demonstrated the good performance of ANN models in describing phytoplankton dynamics, and the potential of coupling ANN with a clustering technique to describe the spatial heterogeneity of natural ecosystems.
引用
收藏
页码:179 / 191
页数:13
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