A multi-layer soil moisture data assimilation using support vector machines and ensemble particle filter

2012 ◽  
Vol 475 ◽  
pp. 53-64 ◽  
Author(s):  
Zhongbo Yu ◽  
Di Liu ◽  
Haishen Lü ◽  
Xiaolei Fu ◽  
Long Xiang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Support Vector Machines ◽  
Data Assimilation ◽  
Particle Filter ◽  
Support Vector ◽  
Soil Moisture Data ◽  
Vector Machines

SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES

2006 ◽  
Vol 42 (4) ◽  
pp. 1033-1046 ◽  
Author(s):  
M. Kashif Gill ◽  
Tirusew Asefa ◽  
Mariush W. Kemblowski ◽  
Mac McKee
Keyword(s):  
Soil Moisture ◽  
Support Vector Machines ◽  
Support Vector ◽  
Vector Machines

scholarly journals Prediction of Soil Moisture-Holding Capacity with Support Vector Machines in Dry Subhumid Tropics

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jacob Kaingo ◽  
Siza D. Tumbo ◽  
Nganga I. Kihupi ◽  
Boniface P. Mbilinyi
Keyword(s):  
Soil Moisture ◽  
Support Vector Machines ◽  
Moisture Content ◽  
Physicochemical Properties ◽  
Support Vector ◽  
Attractive Alternative ◽  
Linear Regression Method ◽  
Soil Physicochemical Properties ◽  
Vector Machines ◽  
Subhumid Tropics

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.

Sign in / Sign up

Export Citation Format

Share Document