Modelling algal behaviour in the river thames
Modelling algal behaviour in the river thames
复制标题
模拟泰晤士河中的藻类行为
DOI：
10.1016/0043-1354(84)90244-6
复制
发表时间：
1984
期刊：
影响因子：
12.8
通讯作者：
G. Hornberger
中科院分区：
文献类型：
--
作者：
P. Whitehead;G. Hornberger
预测河流系统中藻类的运动和生长对于负责饮用水分配和供应的运营经理来说尤其重要。藻类会影响水的味道和气味，并给水处理厂带来相当大的过滤问题。在与泰晤士水务局的一项合作研究中，开发了泰晤士河的藻类模型。使用广义敏感性分析技术检查控制藻类生长的非线性过程，并确定控制系统行为的主要参数。然后使用扩展卡尔曼滤波器（EKF）来估计这些重要参数。建议在 EKF 估计之前使用广义敏感性分析技术作为一种实用方法，用于解决机械模型中控制系统行为的物理、化学或生物学有意义的参数子集的问题。
Forecasting the movement and growth of algae in river systems as particularly important for operational managers responsible for the distribution and supply of potable water. Algae affect the taste and smell of water and pose considerable filtration problems at water treatment plants. In a collaborative study with the Thames Water Authority, algal models have been developed for the River Thames. The non-linear processes controlling algal growth are examined using a generalized sensitivity analysis technique and the dominant parameters controlling system behaviour are identified. The extended Kalman filter (EKF) is then used to estimate these important parameters. The technique of using generalized sensitivity analysis prior to EKF estimation is suggested as a pragmatic approach to the problem of identifying the subset of physically, chemically or biologically meaningful parameters controlling system behaviour in mechanistic models.