support vector machine regression

The present research performed to estimate soil texture using visible near-infrared spectrometry in Semirom, Isfahan. A total number of 200 soil samples (0-10 cm) were collected from the Semirom area (51º 17' - 52º 3' E; 30º 42' - 31º 51' N), Isfahan. The samples were air dried and passed through a 2 mm sieve, and soil particles percentage was determined in the laboratory using hydrometry method. Reflectance spectra of all samples were measured using an ASD field spectrometer. Different pre-processing methods i.e., First Derivatives and Savitzky-Golay Filter, Multiplicative Scatter Correction and Standard Normal Variable were applied and performed on spectral data. The Partial Least Squares Regression, Support Vector Machine Regression and Artificial Neural Network models were used to estimate soil texture. The best result was obtained for Silt estimation, with excellent values of RPD >2, R2 =0.98 and RMSE=1.08 using Artificial Neural Network model with MSC pre-processing technique. The results indicated the desirable capability of Artificial Neural Network model with MSC and SNV pre-processing techniques in estimating the Clay (RPD >2, R2=0.94 and RMSE=1.21) and Sand (RPD >2, R2=0.84 and RMSE=6.24) contents of the soils, respectively. In general, based on the results of this study, VNIR spectroscopy was successful in estimating soil particles percentage and showed its potential for substituting laboratory analyses.

Evaporation is considered as a key factor in hydrological, climatic, agricultural water management, irrigation planning, etc. studies. Evaporation is a complex and nonlinear phenomenon due to the interactions of various climatic factors. Therefore, advanced models such as experimental equations and artificial intelligence should be used to estimate evaporation. In recent years, experimental equations have been widely used to estimate evaporation. In this study, the performance of Gaussian process regression (GPR) and support vector machine regression (SVR) models in estimating the daily evaporation of Amol and Bam stations in the period 2020-2016 has been evaluated.Daily meteorological data on mean temperature, relative humidity, sunshine hours and wind speed were used as input of GPR and SVR models to estimate daily evaporation. In the present study, four combined scenarios of meteorological parameters were used to estimate evaporation. The results of the mentioned models showed that both GPR and SVR models have acceptable performance in estimating evaporation (correlation coefficient about 0.94). Also, according to the evaluations, it was found that the GPR model had a better performance than the SVR model (root mean square error of 1.56 and 1.62, respectively). In the present study, the PUK kernel was used due to its high accuracy, highest correlation coefficient and lowest error (0.94 and 0.84).