Looking for a reliable and user-friendly software that can help you analyze and predict different environmental phenomena? Look no further than KNN-WG by AgriMetSoft! This powerful tool is specifically designed for Windows 7 users, offering advanced data mining capabilities and accurate classification algorithms. With KNN-WG, you can easily analyze large data sets, extract meaningful insights, and make informed decisions based on the latest environmental trends and patterns. So why wait? Download KNN-WG today and discover the power of data-driven environmental analysis!

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).

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