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KNN-WG

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Name: KNN-WG
Works on: windowsWindows 7 and above
Developer: Agrimetsoft
Version: 1
Last Updated: 10 Aug 2017
Release: 07 Aug 2017
Category: Science CAD
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1694 downloads
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KNN-WG Details

Works on: Windows 10 | Windows 8.1 | Windows 8 | Windows 7 | Windows 2012
SHA1 Hash: 748ceb31b789ed154832c4343b1b453dc7eac72d
Size: 59.03 MB
File Format: exe
Rating: 2.608695652 out of 5 based on 23 user ratings
Publisher Website: External Link
Downloads: 1694
License: Demo / Trial Version
KNN-WG is a demo software by Agrimetsoft and works on Windows 10, Windows 8.1, Windows 8, Windows 7, Windows 2012.
You can download KNN-WG which is 59.03 MB in size and belongs to the software category Science CAD.
KNN-WG was released on 2017-08-07 and last updated on our database on 2017-08-10 and is currently at version 1.
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KNN-WG Description

The K-nearest neighbors (K-NN) is an analogous approach. This method has its origin as a non-parametric statistical pattern recognition procedure to distinguish between different patterns according to a selection criterion. Through this method, researchers can generate future data. In other words, the KNN is a technique that conditionally resamples the values from the observed record based on the conditional relationship speci?ed. The KNN is most simple approach.

The most promising non-parametric technique for generating weather data is the K-nearest neighbor (K-NN) resampling approach. The K-NN method is based on recognizing a similar pattern of target ?le within the historical observed weather data which could be used as reduction of the target year (Young, 1994; Yates, 2003; Eum et al., 2010). The target year is the initial seed of data which, together with the historical data, are required as

input ?les for running the model. This method relies on the assumption that the actual weather data observed during the target year could be a replication of weather recorded in the past. The k-NN technique does not use any prede?ned mathematical functions to estimate a target variable.

Actually, the algorithm of this method typically involves selecting a speci?ed number of days similar in characteristics to the day of interest. One of these days is randomly resampled to represent the weather of the next day in the simulation period. The nearest neighbor approach involves simultaneous sampling of the weather variables, such as precipitation and temperature. The sampling is carried out from the observed data, with replacement.

The K-NN method is widely used in agriculture (Bannayan and Hoogenboom, 2009), forestry (Lopez et al., 2001) and hydrology (Clark et al., 2004; Yates et al., 2003).
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KNN-WG Screenshots

KNN-WG screenshot 1
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