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

Abstract

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.