通过对滦河流域66个河段大型底栖动物采集和生境指标监测,基于大型底栖动物完整性评价和13种景观指数构建,探讨了不同景观指数对于大型底栖无脊椎动物完整性的解释能力。景观指数类型包括流域及欧式距离缓冲区土地利用百分比、水流路径...通过对滦河流域66个河段大型底栖动物采集和生境指标监测,基于大型底栖动物完整性评价和13种景观指数构建,探讨了不同景观指数对于大型底栖无脊椎动物完整性的解释能力。景观指数类型包括流域及欧式距离缓冲区土地利用百分比、水流路径缓冲区土地利用百分比、局部区域土地利用百分比和基于水流路径的反距离权重指数。基于多元线性逐步回归模型,根据调整R2(Square of the coefficient)来判断不同指数的解释能力。研究结果表明基于水流路径的反距离权重指数对于大型底栖动物完整性的解释能力最好,其次为基于水流路径的缓冲区和局部区域的土地利用百分比指数,全流域及欧氏距离缓冲区内土地利用百分比解释能力最差。农田是影响大型底栖动物完整性最重要的景观类型,距离河流越近的农田对大型底栖动物完整性的影响越大,因此流域及河岸带农田的控制和管理对于滦河流域大型底栖动物完整性的恢复具有重要的作用。展开更多
A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called corre...A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally, a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods.展开更多
文摘通过对滦河流域66个河段大型底栖动物采集和生境指标监测,基于大型底栖动物完整性评价和13种景观指数构建,探讨了不同景观指数对于大型底栖无脊椎动物完整性的解释能力。景观指数类型包括流域及欧式距离缓冲区土地利用百分比、水流路径缓冲区土地利用百分比、局部区域土地利用百分比和基于水流路径的反距离权重指数。基于多元线性逐步回归模型,根据调整R2(Square of the coefficient)来判断不同指数的解释能力。研究结果表明基于水流路径的反距离权重指数对于大型底栖动物完整性的解释能力最好,其次为基于水流路径的缓冲区和局部区域的土地利用百分比指数,全流域及欧氏距离缓冲区内土地利用百分比解释能力最差。农田是影响大型底栖动物完整性最重要的景观类型,距离河流越近的农田对大型底栖动物完整性的影响越大,因此流域及河岸带农田的控制和管理对于滦河流域大型底栖动物完整性的恢复具有重要的作用。
文摘A new method by integrating the multivariate statistical analysis with neural network used for complex pattern classification was proposed in this paper. First, a particularly developed statistical method called correlational components analysis was employed to extract pattern characteristics from the original sample pattern space. These pattern characteristics were then used as inputs to a multi-layered feedforward neural networks for further pattern classification, The proposed approach transforms the complex patterns into lower dimensional and mutually decoupled ones, it also takes the advantages of the self-learning capability of the neural networks. Finally, a practical example of natural spearmint oil was used to verify the effectiveness of the new method. The results showed that the proposed integrated approach gives better results than other conventional methods.
