Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics
Joint Authors
Kaingo, Jacob
Tumbo, Siza D.
Kihupi, Nganga I.
Mbilinyi, Boniface P.
Source
Applied and Environmental Soil Science
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-08-29
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Earth Science , Water and Environment
Abstract EN
Soil moisture-holding capacity data are required in modelling agrohydrological functions of dry subhumid environments for sustainable crop yields.
However, they are hardly sufficient and costly to measure.
Mathematical models called pedotransfer functions (PTFs) that use soil physicochemical properties as inputs to estimate soil moisture-holding capacity are an attractive alternative but limited by specificity to pedoenvironments and regression methods.
This study explored the support vector machines method in the development of PTFs (SVR-PTFs) for dry subhumid tropics.
Comparison with the multiple linear regression method (MLR-PTFs) was done using a soil dataset containing 296 samples of measured moisture content and soil physicochemical properties.
Developed SVR-PTFs have a tendency to underestimate moisture content with the root-mean-square error between 0.037 and 0.042 cm3·cm−3 and coefficients of determination (R2) between 56.2% and 67.9%.
The SVR-PTFs were marginally better than MLR-PTFs and had better accuracy than published SVR-PTFs.
It is held that the adoption of the linear kernel in the calibration process of SVR-PTFs influenced their performance.
American Psychological Association (APA)
Kaingo, Jacob& Tumbo, Siza D.& Kihupi, Nganga I.& Mbilinyi, Boniface P.. 2018. Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics. Applied and Environmental Soil Science،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1117689
Modern Language Association (MLA)
Kaingo, Jacob…[et al.]. Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics. Applied and Environmental Soil Science No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1117689
American Medical Association (AMA)
Kaingo, Jacob& Tumbo, Siza D.& Kihupi, Nganga I.& Mbilinyi, Boniface P.. Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics. Applied and Environmental Soil Science. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1117689
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1117689