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empirical models are important tools for relating field-measured biophysical variables to remotely sensed data. regression analysis has been a popular empirical method of linking these two types of data to estimate variables such as biomass, percent vegetation canopy cover, and bare soil. this study was conducted in a semi-arid rangeland ecosystem of qazvin province, iran. this paper presents t...

the climate and weather patterns of buffalo (new york, u.s.a.) are strongly influenced by thecity’s proximity to lake erie. total monthly snowfall in buffalo is forecasted using neural network techniques(multi-layer perceptron = mlp) and a multiple linear regression (lr) model. the period of analysis comprises 28 years from january 1982 to december 2009. input data include: zonal wind speed (u-...

many health studies were interested to find the association between multiple predictors with a dependent variable. the popular method to analyze these studies was multiple regressions that assumed the predictors were independent. while in practice some predictors may depend on each other and they could not be presented in a regression model simultaneously, as a result the appropriate models suc...

aim : this study compared pr and nb in predicting hcv patient costs. the objective of this study was to predict the direct cost of the hcv patient in iran. background : hepatitis c virus (hcv) is a common and expensive infectious disease in iran. cost associated with hcv and its complications has not been well characterized. analysis of cost data is important in providing consistent information...

this study examined the relationship of the personality factors (openness, conscientiousness, extraversion, agreeableness, and neuroticism) with each of the three workaholism components i.e. working compulsively, working excessively and combined workaholics. as the study is of exploratory in nature, a sample of 145 academicians was drawn from the two universities of jammu region (india), namely...

abstract reference evapotranspiration (eto) is an essential parameter required for proper management of agricultural crop irrigation. eto is influenced by many different hydrological variables and as a result is a very complex procces. eto is usually estimated by empirical or process-orinented models (mathematical relationships) from historical weather data. the need for accurate estimates of e...

because of the scarcity of in situ measurements, estimation of soil temperature by other means is very indispensable, as for irrigation management and scheduling when in different field conditions. so far, many regression models have been developed for an estimation of soil temperature, using meteorological data under bare soil. throughout this study, the performance of coup simulation model an...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...