TAILIEUCHUNG - Impact of metrological parameters on reference evapotranspiration using multiple linear regressions

The reference evapotranspiration (ET0) of Morena station was estimated using multiple linear regression (MLR), The climatological data such as maximum temperatures, minimum temperature, mean relative humidity, wind speed and solar radiation were collected for the morena station and district of Madhya Pradesh state of India for the period of thirty years and the missing value of that data series was also determine using SPSS-21 software. The observed reference evapotranspiration (ET0) values were estimated using the Penman monteith (FAO56-PM) equation. Multiple Linier Regression is carried out using ET0 as predictor variable and maximum temperatures, minimum temperature, relative humidity, solar radiation and wind speed as independent variable to find out predominant factor on ET0. This whole procedure is done for three different variable based Models. In model-1, Maximum temperature and minimum temperature speed are correlated with ET0. In model-2, Maximum temperature, minimum temperature and solar radiation are correlated with ET0. In model-3, Maximum temperature, minimum temperature, mean relative humidity and wind speed are correlated with ET0. In case of model 3 the value of R, R2 and RMSE for 70% dataset is , and respectively and for 30% dataset it is , and respectively. As the value of R and R2 are nearer to 3 and the value of RMSE is low, which is good. As the model-3 gives the best correlation values as compared to model-2 and model-1, it can be accepted as the best fit model for prediction of ET0. Considering maximum temperature the model gives good correlation values hence maximum temperature is accepted as predominant factor and the presence of relative humidity does not play an important role in prediction of ET0 for this study area. | Impact of metrological parameters on reference evapotranspiration using multiple linear regressions

• Vật lý
• Multiple linear regression
• Performance evaluation
• Climate change
• Reference evapotranspiration
• Impact of metrological parameters
• Lntil yield management
• Fuzzy linear regression
• Lens culinaris
• Pedrycz models
• Lentil genotypes
• Area of tunnel face
• Artificial neural network
• Multiple linear regression analysis method
• Rock mass rating
• BMC Bioinformatics
• B cell
• Linear epitope
• Antibody production
• Immunological application
• Basic statistics
• Statistics for business
• Describing data
• Multiple regression analysis
• Multiple determination
• Econometrics for Finance
• Introductory Econometrics for Finance
• Financial data
• Financial modelling
• Classical linear regression model
• Statistical methods
• Data analysis
• Linear regression
• Multiple regression
• The general linear model
• Further regression topics
• The analysis of covariance
• Simple linear regression
• Certain nonlinear regression models
• Factorial experiments
• Nonparametric statistics
• Regression analysis
• Iteratively reweighted least squares
• Civil engineering
• Civil engineering data modeling
• Ebook Probability and statistics for engineers
• Statistics for engineers
• Probability for engineers
• Basic business statistics
• Analysis of variance
• Multiple regression model building
• Statistical applications
• Analyzing data
• Regression model
• Purpose of econometrics
• Applied statistics and probability
• Continuous random variables
• Statistical quality control
• Applied statistics
• Several factors
• Domestic water demand
• Forecast model
• Demande dutilisation de leau par les personnes
• Principles of econometrics
• Using stata
• Using stata for principles of econometrics
• Interval estimation
• The behavioral sciences
• Testing means
• The related samples t Test
• Confidence intervals
• Nonparametric tests
• Tests of hypotheses
• Model building
• Categorical data analysis
• Even you can learn statistics
• Learn statistics
• Hypothesis testing
• Sigma applications of statistics
• Business statistics
• Time series
• Decision analysis
• Sampling methods
• Multivariate analysis
• Bayesian statistics
• Index numbers
• Nonparametric methods
• Sample survey
• Statistics for managers using Microsoft excel
• Time series forecasting
• Quality management
• Probability statistics
• Inference from small samples
• The analysis of variance
• Categorical data
• Time series analysis
• Quality control
• Practical research
• Two sample tests of hypothesis