摘要
针对目前普遍使用的确定性水质模型的局限性,在对人工神经网络的传统算法进行改进的基础上将其与地理信息系统相结合对可视化的非确定性河流水质模型进行了研究,并应用黄河白银段的水质实测资料对模型进行了检验。模拟结果表明,这种可视化的非确定性河流水质模型能够很好地模拟河流水质,并且简单可行。
Integrating Geographic Information System combined with an improved artificial neural network create a visually uncertain river water quality model to complement the limitations of deterministic models used commonly.The coupled model was tested by using the observed data of Baiyin section of Yellow River.The results indicate the proposed model can well simulate the water quality and easy to operate.
出处
《遥感技术与应用》
CSCD
2007年第5期598-601,共4页
Remote Sensing Technology and Application
关键词
人工神经网络
算法改进
非确定性河流水质模型
地理信息系统
可视化
Artificial neural network,Improved algorithms,Uncertain river water quality model,Geographic information system,Visualization
