基于高光谱遥感的湖泊水质富营养化状态评价

Evaluation of lake water quality eutrophication based on hyperspectral remote sensing

为提高湖泊水质富营养化状态评价的精度,提出了一种融合高光谱遥感和萤火虫算法(FA)改进极限学习机(Extreme learning machine,ELM)的湖泊水质富营养化状态评价方法。针对ELM模型性能受其初始输入权值和隐含层偏置参数选择的影响,将萤火虫算法应用于ELM模型参数寻优。结果表明,与PSO-ELM、GA-ELM、DE-ELM和ELM相比,FA-ELM可以有效提高水质富营养化评价的准确率,为湖泊水质富营养化状态评价提供了新的方法。

In order to improve the evaluation accuracy of the eutrophication state of lake water quality,a new evaluation method for the eutrophication state of lake water quality was proposed,which combined hyperspectral remote sensing and firefly algorithm(FA)and improved extreme learning machine(ELM).Because the performance of ELM model was affected by its initial input weight and hidden layer bias parameter selection,firefly algorithm was applied to ELM model parameter optimization.The results showed that compared with PSO-ELM,GA-ELM,DE-ELM and ELM,FA-ELM could effectively improve the accuracy of water quality eutrophi⁃cation evaluation,providing a new method for water quality eutrophication evaluation of lakes.