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Factorial or indicator modelling
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40 2019 ND-CP 413905
- Điều hướng trang này:
- Process modelling
| Factorial or indicator modelling
Since many of the processes and factors that influence a particular type of environmental problem are well known, it is possible to rank individual factors by the strength of their association with the problem, providing a series of indicators. For example, climatic indices may be based on the frequency of high-intensity precipitation, and on the degree of aridity or rainfall seasonality. Soil indicators may reflect the tendency to crusting and sealing. Similar rank indicators may be developed for parent materials, topographic gradients and other factors. Individual indicators may be mapped separately, but combining the factors into a single scale –by adding or multiplying suitably weighted indicators for each individual factor– is more problematic. There are difficulties both about the individual weightings and about the assumed linearity and statistical independence of the separate factors. The method may be effective for identifying the extremes of high and low susceptibility, but is less satisfactory in identifying the gradations between the extremes. Despite these theoretical limitations, factor or indicator mapping has the considerable advantage that it can be widely applied using data that are available at a regional or even continental scale. There is a continuous spectrum between indicators based on simple ranking and those based on equations with a more explicit physical or empirical basis. Process modelling A process model consists of an equation or a set of equations designed to represent the process and its behaviour under study. A large variety of models have been developed for describing processes. Depending on the level of process understanding and data availability, process models may be characterized in different ways. Mechanistic models, as opposed to empirical models, describe the process on the basis of physical understanding. Deterministic models are capable of producing quantitative predictions, whereas stochastic models also incorporate associated probability distributions and random elements, so that not only a value can be predicted but also its uncertainty. The technical complexity of stochastic models justifies in some cases the combination of Monte Carlo simulation and deterministic models to deal with uncertainties. Dynamic models, in contrast to static models, incorporate time as an explicit component in describing processes. A review of many models dealing with changes in soil properties can be found in Young (1994). Process models have the potential to respond explicitly and rationally to changes in input variables, e.g. climate, and so have promise for developing scenarios of change, and what-if analyses of policy or economic options. Set against this advantage, process models generally cannot assess environmental processes up to the present time, and can only incorporate the impact of past events where this is recorded, as in soil databases, yield statistics or hydrological records. Also, models generally are simplifications of the set of processes operating, so they may not be appropriate under particular local circumstances. tải về 0.57 Mb. Chia sẻ với bạn bè của bạn:
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