Prediction of soil moisture-holding capacity with support vector machines in dry subhumid tropics
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Date
2018-07
Journal Title
Journal ISSN
Volume Title
Publisher
Hindawi
Abstract
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 cm 3 ·cm −3 and coefficients of determination (R 2 ) 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.
Description
Keywords
Descriptive Statistics of Soil Datasets, Soil moisture, Crop yields in dry subhumid zones