KNN-WG

Tool Name:

KNN-WG

Tool Version: 1.1

Tool Type: Shareware

Tool Cost In: 44.95 US$

Tool Target Platform: Windows

Tool OS Support: Win2000,WinXP,Win7 x32,Win7 x64,Windows 8,Windows 10,WinServer,WinOther,WinVista,WinVista x64

Limitations:

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Short Description:
In this tool, the user can load seven different variables, for example Tmin, Tmax, Rain, Srad, ETo, WSPD, and Humidity. Then, the user can load the input data and run KNN-WG.

Long Description 1:
In this software the user can draw graphs and calculate efficiency criteria: d, NSE, RMSE, MBE, Pearson and Spearman. The user can compare outputs of KNN-WG with other models such as Lars-WG, SDSM, CMIP5, and etc. One of the most advantages of the KNN-WG software is that the user can select every number of variables (from 7 variables) to generate future data.

Long Description 2:
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 specied. 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 predened mathematical functions to estimate a target variable. Actually, the algorithm of this method typically involves selecting a specied 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).