Search published articles


Showing 2 results for Meteorological Parameters
Deriving Regression Relationships between Soil Temperature at Different Depths and Meteorological Parameters (Case Study: Hamedan Station)
N. Parsafar , S. Marofi,
Volume 16, Issue 62 (3-2013)
Abstract
In this research, we estimated soil shallow depths temperatures using regression methods (Linear and Polynomial). The soil temperatures at soil depths (5, 10, 20, 30, 50 and 100 cm) were correlated with meteorological parameters. For this purpose, temperature data of Hamedan station (in the period 1992-2005) were employed. Soil temperature data were measured on a daily basis at 3 PM, 9 PM and 3 AM. MS Excel was used for deriving the regressions between soil temperature and meteorological parameters (air temperature, relative humidity and sunshine hours). The results showed that the highest coefficient of determination (R2) of the linear regression was between soil temperature in 20 cm soil depth and air temperature at 3 AM (R2= 98.15%) and the lowest value in 100 cm soil depth at 3PM (R2= 83.96%). Also, the highest R2 of non-linear regression was observed between soil temperature in 10 cm soil depth and air temperature at 3 AM (R2= 98.45%) and lowest value in 100 cm soil depth at 3PM (R2= 84.11%). The results showed that the highest and lowest values of R2 of linear relations between meteorological parameters (relative humidity and sunshine hours) and soil temperature were observed in 10 cm soil depth (at 3 AM) and in 100 cm soil depth, respectively. Correlations of soil temperature with air temperature were greater than those with the other two parameters. Moreover, R2 values of non- linear relation were higher than linear relation.
Comparing of Generalized Linear Models, Random Forest and Gradient Boosting Trees in Estimation of Reference Crop Evapotranspiration (Case Study: The Sistan Plain)
H. Siasar, T. Honar, M. Abdolahipour,
Volume 23, Issue 4 (2-2020)
Abstract
The estimation of reference crop evapotranspiration (ETo) is one the important factors in hydrological studies, irrigation planning, and water resources management. This study attempts to explore the possibility of predicting this key component using three different methods in the Sistan plain: Generalized Linear Models (GLM), Random Forest (RF) and Gradient Boosting Trees (GBT). The maximum and minimum temperature, mean temperature, maximum and minimum humidity, mean humidity, rainfall, sunshine hours, wind speed, and pan evaporation data were applied for years between 2009 to 2018. Using various networks, the ETo as output parameter was estimated for different scenarios including the combination of daily scale meteorological parameters. In order to evaluate the capabilities of different models, results were compared with the ETo calculated by FAO Penman-Monteith as the standard method. Among studied scenarios, M1 covering the maximum number of input parameters (10 parameters) showed the highest accuracy for GBT model, with the lowest RMSE (0.633) and MAE (0.451) and the maximum coefficient of regression (R = 0.993). Air temperature was found as the most sensitive parameters during sensitivity analysis of studied models. It indicated that accuracy and precision of temperature data can improve the results. Application of the GBT model could decrease the time consumed to run the model by 70%. Therefore, the GBT model is recommended for estimation of ETo in the Sistan plain.

Page 1 from 1     

© 2024 CC BY-NC 4.0 | JWSS - Isfahan University of Technology

Designed & Developed by : Yektaweb