Sensitivity analysis (SA) is a fundamental practice for analyzing model behavior under
different conditions of application. A number of SA techniques were proposed, ranging from …

Stochastic simulation models requiring many input parameters are widely used to inform the
management of ecological systems. The interpretation of complex models is aided by global …

Sensitivity analysis is usually conducted independently for each parameter in turn. This can
give misleading results when parameter estimates are correlated. Such correlations can be …

Sensitivity analysis applied to Artificial Neural Networks (ANNs) as well as to other types of
empirical ecological models allows assessing the importance of environmental predictive …

在总结国内外对生态敏感性研究的基础上, 应用生态因子评分方法和GIS 技术对广州市城市生态
系统进行敏感性区划, 将广州市划分为最敏感区, 敏感区, 弱敏感区和非敏感区4 个等级 …

The development of parameter models is presented. The drawbacks to the use of classical
(Tomovic's) and the IPP (individual parameter perturbation) methods, in general and when …

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Uncertainty in input or parameter values affects the quality of model predictions. Uncertainty
analysis attempts to quantify these effects. This is important, first of all as part of the overall …

Through several decades of development, global sensitivity analysis has been developed
as a very useful guide tool for assessing scientific models and has gained pronounced …

针对土地适宜性评价中采用定性和单因素方法而产生的主观, 片面和精度低等缺点,
分析了土地适宜性评价中的不确定性因素, 并提出基于不确定性和灰色系统关联度的土地生态 …