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PREDICTION OF SOIL MOISTURE DATA BY VARIOUS REGRESSION TECHNIQUES

Milan Čistý

First published: 2018-11-20https://doi.org/10.5593/sgem2018v/1.5/s02.048View metrics

Abstract

Measuring soil moisture it is usually not performed on a daily time step due to financial costs, time-consuming measurements and variability of the weather. However, since this information is an important part of many hydrological and water management studies, the author tries to solve this situation by proposing a suitable interpolation method. The goal is to predict daily moisture values so that their conformity with the test data is as high as possible. The calculation is based on the use of soil moisture data obtained by derivation from satellite images В©EUMESAT and data on temperatures and rainfall from climate database ECA&D. The article examines the use of linear and data-mining methods and uses multiple algorithms such as simple linear regression, Radom Forest, Support Vector Machines. The highest degree of precision was achieved using the SVM model with a correlation with measured data of 0.83. This indicates the suitability of using the SVM method for the prediction of soil moisture.

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Publication details

Title
PREDICTION OF SOIL MOISTURE DATA BY VARIOUS REGRESSION TECHNIQUES
Authors
Milan Čistý
Proceedings
SGEM International Multidisciplinary Scientific GeoConference EXPO Proceedings; 18th SGEM International Multidisciplinary Scientific GeoConference SGEM2018, Science and Technologies in Geology, Exploration And Mining
Publisher
STEF92 Technology
Year
2018
Pages
383-390
SWS Citekey
Cisty20182383390
ISSN
1314-2704
ISBN
978-619-7408-72-0
Language
en
Publication type
Conference Paper
Keywords
soil moistureremote sensingdata miningsupport vector machines
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