Nearest Neighbor Soil Water Retention Estimator

Metadata Updated: November 10, 2020

The k-nearest neighbor (k-NN) technique is a non-parametric technique that can be used to make predictions of discrete (class-type) as well as continuous variables. The k-NN technique and many of its derivatives belong to the group of .lazy learning algorithms.. It is lazy, as it passively stores the development data set until the time of application; all calculations are performed only when estimations need to be generated. The ability of soil to retain and to transmit water has to be known for many engineering, meteorological, agronomic, and hydrological applications. Measurements of soil hydraulic properties are costly and impractical for large scale projects. For such projects, soil hydraulic properties are estimated from publicly available basic soil data using statistical regression. Such regression equations have been developed in various regions of the World. The serious problem with these equations is that their applicability is unpredictable. We have empirically proven that information regarding soil properties can be used to reasonably predict hydraulic properties. The nearest-neighbor algorithm appears to be applicable to evaluate the similarity for this purpose. This algorithm has been coded in simple software to estimate water contents that are commonly associated with the ability of soil to hold water and with the dryness of soil causing wilting of plants. The substantial advantage of our software is that available local information on soil hydraulic properties can be easily incorporated in the similarity evaluation. The more that is known about local soil hydraulic properties, the better the estimation that can be obtained. Technical Abstract: A computer tool has been developed that uses a k-Nearest Neighbor (k-NN) lazy learning algorithm to estimate soil water retention at –33 and –1500 kPa matric potentials and its uncertainty. The user can customize the provided source data collection to accommodate specific local needs. Ad hoc calculations make this technique a competitive alternative to published pedotransfer equations, as re-development of such equations is not needed when new data become available.

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Public: This dataset is intended for public access and use. License: Creative Commons CCZero

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Metadata Created Date November 10, 2020
Metadata Updated Date November 10, 2020

Metadata Source

Harvested from USDA JSON

Additional Metadata

Resource Type Dataset
Metadata Created Date November 10, 2020
Metadata Updated Date November 10, 2020
Publisher Agricultural Research Service
Unique Identifier Unknown
Identifier 4a8cd322-f574-4ecc-8eda-16f7f9d4499a
Data Last Modified 2019-08-05
Public Access Level public
Bureau Code 005:18
Metadata Context
Schema Version
Catalog Describedby
Harvest Object Id 7a835122-cc1a-495a-981d-86750dc2c713
Harvest Source Id d3fafa34-0cb9-48f1-ab1d-5b5fdc783806
Harvest Source Title USDA JSON
Program Code 005:040
Source Datajson Identifier True
Source Hash 4b31f507b6bd9beb2798a841eea71e07b78dfdca
Source Schema Version 1.1

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