Multi-step ahead soil temperature forecasting at different depths based on meteorological data: Integrating resampling algorithms and machine learning models     

Khabat KHOSRAVI  Ali GOLKARIAN  Rahim BARZEGAR  Mohammad T. AALAMI  Salim HEDDAM  Ebrahim OMIDVAR  Saskia D. KEESSTRA  Manuel L&#;PEZ-VICENTE 《土壤圈》2023,33(3):479-495

Direct soil temperature (ST) measurement is time-consuming and costly;thus,the use of simple and cost-effective machine learning (ML) tools is helpful.In this study,ML approaches,including KStar,instance-based K-nearest learning (IBK),and locally weighted learning (LWL),coupled with resampling algorithms of bagging (BA) and dagging (DA)(BA-IBK,BA-KStar,BA-LWL,DA-IBK,DA-KStar,and DA-LWL) were developed and tested for multi-step ahead (3,6,and 9 d ahead) ST forecasting.In addition,a linear regress...  相似文献   

Comparing the Soil Conservation Service model with new machine learning algorithms for predicting cumulative infiltration in semi-arid regions     

Khabat KHOSRAVI  Phuong T. T. NGO  Rahim BARZEGAR  John QUILTY  Mohammad T. AALAMI  Dieu T. BUI 《土壤圈》2022,32(5):718-732

Water infiltration into soil is an important process in hydrologic cycle; however, its measurement is difficult, time-consuming and costly. Empirical and physical models have been developed to predict cumulative infiltration (CI), but are often inaccurate. In this study, several novel standalone machine learning algorithms (M5Prime (M5P), decision stump (DS), and sequential minimal optimization (SMO)) and hybrid algorithms based on additive regression (AR) (i.e., AR-M5P, AR-DS, and AR-SMO) and weighted instance handler wrapper (WIHW) (i.e., WIHW-M5P, WIHW-DS, and WIHW-SMO) were developed for CI prediction. The Soil Conservation Service (SCS) model developed by the United States Department of Agriculture (USDA), one of the most popular empirical models to predict CI, was considered as a benchmark. Overall, 154 measurements of CI (explanatory/input variables) were taken from 16 sites in a semi-arid region of Iran (Illam and Lorestan provinces). Six input variable combinations were considered based on Pearson correlations between candidate model inputs (time of measuring and soil bulk density, moisture content, and sand, clay, and silt percentages) and CI. The dataset was divided into two subgroups at random:70% of the data were used for model building (training dataset) and the remaining 30% were used for model validation (testing dataset). The various models were evaluated using different graphical approaches (bar charts, scatter plots, violin plots, and Taylor diagrams) and quantitative measures (root mean square error (RMSE), mean absolute error (MAE), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS)). Time of measuring had the highest correlation with CI in the study area. The best input combinations were different for different algorithms. The results showed that all hybrid algorithms enhanced the CI prediction accuracy compared to the standalone models. The AR-M5P model provided the most accurate CI predictions (RMSE=0.75 cm, MAE=0.59 cm, NSE=0.98), while the SCS model had the lowest performance (RMSE=4.77 cm, MAE=2.64 cm, NSE=0.23). The differences in RMSE between the best model (AR-M5P) and the second-best (WIHW-M5P) and worst (SCS) were 40% and 84%, respectively.  相似文献   

Virus‐specific and common transcriptomic responses of potato (Solanum tuberosum) against PVY,PVA and PLRV using microarray meta‐analysis     

Zhila Osmani  Mohammad Sadegh Sabet  Masoud Shams‐Bakhsh  Ahmad Moieni  Khabat Vahabi 《Plant Breeding》2019,138(2):216-228

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Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple‐trait random regression models     

Khabat Kheirabadi  Amir Rashidi  Sadegh Alijani  Ikhide Imumorin 《Animal Science Journal》2014,85(11):925-934

We compared the goodness of fit of three mathematical functions (including: Legendre polynomials, Lidauer‐Mäntysaari function and Wilmink function) for describing the lactation curve of primiparous Iranian Holstein cows by using multiple‐trait random regression models (MT‐RRM). Lactational submodels provided the largest daily additive genetic (AG) and permanent environmental (PE) variance estimates at the end and at the onset of lactation, respectively, as well as low genetic correlations between peripheral test‐day records. For all models, heritability estimates were highest at the end of lactation (245 to 305 days) and ranged from 0.05 to 0.26, 0.03 to 0.12 and 0.04 to 0.24 for milk, fat and protein yields, respectively. Generally, the genetic correlations between traits depend on how far apart they are or whether they are on the same day in any two traits. On average, genetic correlations between milk and fat were the lowest and those between fat and protein were intermediate, while those between milk and protein were the highest. Results from all criteria (Akaike's and Schwarz's Bayesian information criterion, and ?2*logarithm of the likelihood function) suggested that a model with 2 and 5 coefficients of Legendre polynomials for AG and PE effects, respectively, was the most adequate for fitting the data.  相似文献