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The runoff curve number (also called a curve number or simply CN) is an empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess. [1] The curve number method was developed by the USDA Natural Resources Conservation Service , which was formerly called the Soil Conservation Service or SCS — the ...
The runoff curve number (also called a curve number or simply CN) is an empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess. [13] The curve number method was developed by the USDA Natural Resources Conservation Service , which was formerly called the Soil Conservation Service or SCS — the ...
This approach is adopted from the NRCS (SCS) curve number method for estimating runoff. It assumes that the total infiltration capacity of a soil can be found from the soil's tabulated curve number. During a rain event this capacity is depleted as a function of cumulative rainfall and remaining capacity.
Among these are the Green and Ampt (1911) [9] method, Parlange et al. (1982). [10] Beyond these methods, there are a host of empirical methods such as SCS method, Horton's method, etc., that are little more than curve fitting exercises.
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The International Generic Sample Number or IGSN is a persistent identifier for sample.As an active persistent identifier it can be resolved through the Handle System.The system is used in production by the System for Earth Sample Registration (SESAR), Geoscience Australia, Commonwealth Scientific and Industrial Research Organisation Mineral Resources, Australian Research Data Commons (ARDC ...
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence [clarify] on the values of the estimates. Therefore, it also can be interpreted as an outlier detection method. [1]
The average silhouette of the data is another useful criterion for assessing the natural number of clusters. The silhouette of a data instance is a measure of how closely it is matched to data within its cluster and how loosely it is matched to data of the neighboring cluster, i.e., the cluster whose average distance from the datum is lowest. [8]