FORECASTING GROUNDWATER EVAPORATION USING MULTIPLE LINEAR REGRESSION
Main Article Content
Models of regression analysis and classification of time-series data based on machine learning algorithms allow solving the problem of forecasting the state of the region in various fields, including agriculture. One of the problems in this area is soil salinity, one of the main causes of salinization being associated with rising groundwater levels. This paper is devoted to defining a model for predicting groundwater evaporation using a multiple variable linear regression method using geographic data from the region. Data from of Khorezm region between 1980 and 2010 were used as input data for the construction of the model, and a training sample was developed based on this data. A correlation analysis was performed to study the relationship between the sample variables, and a three-variable linear regression model consisting of precipitation, water evaporation, and air temperature was used to predict the groundwater level and to increase the accuracy of the model. The method of clearing the data in the training sample from interference is also presented in this article.
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