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- #Latin hypercube sampling uncertainty analysis manual
- #Latin hypercube sampling uncertainty analysis software
Also, additional covariates were included in the Latin Hypercube Sampling approach which is categorical in nature such as external Surficial Geology data. The iterations within the LHS sampling were run at an optimal level so the LHS model provided a good spatial representation of the environmental attributes within the watershed. The spatial resolution of covariates included within the work ranged from 5 - 30 m. The range of specific points created in LHS included 50 - 200 depending on the size of the watershed and more importantly the number of soil types found within. These additional covariates often include but are not limited to Topographic Wetness Index (TWI), Length-Slope (LS) Factor, and Slope which are continuous data. These include a required Digital Elevation Model (DEM) and subsequent covariate datasets produced as a result of a Digital Terrain Analysis performed on the DEM. Secondary soil and environmental attributes are critical inputs that are required in the development of sampling points by LHS. This allowed for specific sets of LHS points to be produced to fulfil the needs of various partners from multiple projects working in the Ontario and Prince Edward Island provinces of Canada. The Latin Hypercube Sampling ( LHS) approach to assist with Digital Soil Mapping has been developed for some time now, however the purpose of this work was to complement LHS with use of multiple spatial resolutions of covariate datasets and variability in the range of sampling points produced. AAFC - Agriculture and Agr-Food Canada, Ottawa, Canada. Sampsa Hamalainen, Xiaoyuan Geng, and Juanxia, He. Latin Hypercube Sampling ( LHS) at variable resolutions for enhanced watershed scale Soil Sampling and Digital Soil Mapping. Hamalainen, Sampsa Geng, Xiaoyuan He, Juanxia
#Latin hypercube sampling uncertainty analysis manual
This manual covers the theory behind stratified sampling as well as use of the LHS code both with the Windows graphical user interface and in the stand-alone mode. The present program replaces the previous Latin hypercube sampling program developed at Sandia National Laboratories (SAND83-2365). The Latin hypercube technique employs a constrained sampling scheme, whereas random sampling corresponds to a simple Monte Carlo technique.
#Latin hypercube sampling uncertainty analysis software
This software has been developed to generate either Latin hypercube or random multivariate samples. This document is a reference guide for LHS, Sandia`s Latin Hypercube Sampling Software. Risk Assessment and Systems Modeling Dept. [Sandia National Labs., Albuquerque, NM (United States). We can split the vertical scale into 5 equal probability ranges: 0-20%, 20-40%, …, 80-100%.A user`s guide to LHS: Sandia`s Latin Hypercube Sampling SoftwareĮnergy Technology Data Exchange (ETDEWEB)
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Imagine we want to take 5 samples from this distribution. The vertical axis represents the probability that the variable will fall at or below the horizontal axis value. Probability distributions can be described by a cumulative curve, like the one below. It works by controlling the way that random samples are generated for a probability distribution. We are often asked why we don’t implement LHS in our ModelRisk software, since nearly all other Monte Carlo simulation applications do, so we thought it would be worthwhile to provide an explanation here. LHS does not deserve a place in modern simulation software. However, desktop computers are now at least 1,000 times faster than the early 1980s, and the value of LHS has disappeared as a result. It was, at the time, an appealing technique because it allowed one to obtain a stable output with a much smaller number of samples than simple Monte Carlo simulation, making simulation more practical with the computing tools available at the time. The technique dates back to 1980 (even though the manual describes LHS as “a new sampling technique”) when computers were very slow, the number of distributions in a model was extremely modest and simulations took hours or days to complete. It is a method for ensuring that each probability distribution in your model is evenly sampled which at first glance seems very appealing. Most risk analysis simulation software products offer Latin Hypercube Sampling (LHS).