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Applications Of Sensitivity Analysis To Environmental Sciences
Sensitivity analysis studies the relationship between the output of a model and its input variables or assumptions. Historically, the need for a role of sensitivity analysis in modelling, and many applications of sensitivity analysis have originated from environmental science and ecology. Early works Hydrology and water quality are two modelling fields where sensitivity analysis was applied quite early. Relevant examples are the work of Bruce Beck, George M. Hornberger, Keith Beven and Robert C. Spear. Other applications More recent applications encompass snow avalanche models, land depletion, marine biogeochemical modelling, irrigation and again hydrological modelling. Methods Several methods related sensitivity analysis have been developed in the context of environmental applications, such as Data Based Mechanistic Model due to Peter Young and VARS due to S. Razavi and H. V.Gupta. Prevalence across disciplines In a 2019 work on the take-up of sensitivity analysis in differen ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sensitivity Analysis
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on the output. A related practice is uncertainty analysis, which has a greater focus on uncertainty quantification and propagation of uncertainty; ideally, uncertainty and sensitivity analysis should be run in tandem. Motivation A mathematical model (for example in biology, climate change, economics, renewable energy, agronomy...) can be highly complex, and as a result, its relationships between inputs and outputs may be faultily understood. In such cases, the model can be viewed as a black box, i.e. the output is an "opaque" function of its inputs. Quite often, some or all of the model inputs are subject to sources of uncertainty, including errors of measurement, er ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
