FORECASTING GROUNDWATER EVAPORATION USING MULTIPLE LINEAR REGRESSION
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Abstract
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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References
Rahmani, F., Lawson, K., Ouyang, 545 W., Appling, A., Oliver, S., and Shen, C .: Exploring
the Exceptional Performance of a Deep Learning Stream Temperature Model and the Value of
Streamflow Data, Environ. Res. Lett., Https://doi.org/10/ghsw9p, 2020.
Rajaee, T., Ebrahimi, H., and Nourani, V .: A Review of the Artificial Intelligence Methods
in Groundwater Level Modeling, Journal of Hydrology, https://doi.org/10/gfvfg3, 2019.
Rauthe, M., Steiner, H., Riediger, U., Mazurkiewicz, A., and Gratzki, A .: A Central European
Precipitation Climatology - Part I: Generation and Validation of a High-Resolution Gridded
Daily Data Set ( HYRAS), Meteorol. Z., p. 22, https://doi.org/10/f5gf49, 2013.
Reback, J., McKinney, W., Jbrockmendel, Bossche, JVD, Augspurger, T., Cloud, P., Gfyoung,
Sinhrks, Klein, A., Roeschke, M., Hawkins, S., Tratner, J ., She, C., Ayd, W., Petersen, T.,
Garcia, M., Schendel, J., Hayden, A., MomIsBestFriend, Jancauskas, V., Battiston, P., Seabold,
Water evaporates An unarbitrary variableAir temperature
S., Chris -B1, H-Vetinari, Hoyer, S., Overmeire, W., Alimcmaster1, Dong, K., Whelan, C., and
Mehyar, M .: Pandas-Dev / Pandas: Pandas 1.0.3, Zenodo, https:
//doi.org/10.5281/ZENODO.3509134, 2020.
Région Alsace - Strasbourg: Bestandsaufnahme Der Grundwasserqualität Im
Oberrheingraben / Inventaire de La Qualité Des Eaux Souterraines Dans La Vallée Du Rhin
Supérieur, 1999.
Shen, C .: A Transdisciplinary Review of Deep Learning Research and Its Relevance for
Water Resources Scientists, Water Resour. Res., 54, 8558-8593, https://doi.org/10/gd8cqb, 2018.
Sudheer, K. P., Nayak, P. C., and Ramasastri, K. S .: Improving Peak Flow Estimates in
Artificial Neural Network River Flow Models, Hydrological Processes, 17, 677-686,
https://doi.org/10/b39k4k, 2003.
Law of the Republic of Uzbekistan "On Informatization", 11.12.2003, No. 560-II, Tashkent
Collected Legislation of the Republic of Uzbekistan, 2014, No. 36.
Resolution of the President of the Republic of Uzbekistan No. PP-4642 of 03.17.2020, "On
measures for the widespread introduction of digital technologies in the city of Tashkent"
National database of legislation, 18.03.2020, No. 07/20/4642/0328
Bezruchko B. P., Smirnov D. A. Mathematical modeling and chaotic time series. - Saratov:
GosUNTS "College", 2005. - ISBN 5-94409-045-6
Rustam Yakhshibaev, Boburkhon Turaev, Khudoyorkhon Jamolov, Nozima Atadjanova,
Elena Kim, Nargiza Sayfullaeva - International Conference on Information Science and
Communications Technologies (ICISCT) 2021
Dzhumanov Zh.Kh., Razhabov F.F., Kim E.V., Yakhshiboev R. Development of a model and
calculation of the biosignal flow balance based on microcontrollers, ISSN2181-7812 2020. P.187-
Dzhumanov Zh.Kh., Yusupov R.A., Egamberdiev Kh.S., Yakhshiboev R.E. The use of
geographic information systems to substantiate promising areas for the organization of
prospecting and exploration works (for example, for water supply of national economic facilities)
IICS-2020.
Dzhumanov Zh. Kh. Yakhshiboev R. Development of a mathematical model and a software
package for calculating items of the balance of information flow (for example, drinking water).
IICS-2020
Yakhshiboev Rustam Erkinboy ўғli “Forecasting groundwater quality using machine
learning” - “EDUCATION AND SCIENCE IN THE XXI CENTURY” 5-20. Page 1131.2021yy