Browsing by Author "Devindi, R. P. D. C. S."

Now showing 1 - 2 of 2

Economic determinants of suicides in Sri Lanka over the period 1975 – 2015.
(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Devindi, R. P. D. C. S.; Kulatunga, D. D. S.
Suicide is a major public health problem in the world. According to the World Health Organization, every 40 seconds, a person commits suicide somewhere in the world. In Sri Lanka a steady increase of suicides has been reported and it stands in the fourth place among the countries with high occurrences of suicides in 2015. Further suicide is one of the major causes of death in Sri Lanka. There are many studies done in Sri Lanka to investigate the variations in suicide rates in relation to age, gender, method and reason specific trends and geographical distributions. But there are no previous studies done to identify the economic determinants using a dynamic econometric model of suicides on the basis of time series data. This study investigates the association of economic conditions with the suicide rates among male, female and general population in Sri Lanka over the period 1975-2015.The data for suicides from 1975 to 2015 was taken from the Registrar General Department and Police Department of Sri Lanka. The effective suicide rates were computed as the number of suicides per 100,000 population. The unemployment rate, GDP growth rate, inflation and fertility rates for 1975-2015 were taken from the Department of Census and Statistics. In our analysis, overall suicide rate, male suicide rate and female suicide rate are taken as dependent variables, and unemployment rate, GDP growth rate, inflation and fertility rate as independent variables in each case. Because of the time series nature of the variables, Autoregressive Distributed Lag (ARDL) regression model is employed to identify the long run relationship between the dependent variable and explanatory variables. The analysis reveals that, for the general population the incidence and rate of suicide increase with the increase in unemployment rate and inflation. Further, the male suicide rate increases with the increase in unemployment and inflation. The positive coefficient of the unemployment rate supports the increase in female suicides. In general, the ARDL models reveal the association between suicide and three economic variables; inflation, unemployment and GDP growth. The inflation and unemployment enhance the incidence of overall suicides and male suicides, while only inflation supports the incidence of female suicides in the long run in Sri Lanka. There are some limitations in the analysis due to the unavailability of information of several economic factors. The results obtained in this analysis would be helpful for a comprehensive work on economic determinants of suicides in Sri Lanka.
The energy efficiency of buildings estimation by OLS & AIC
(Faculty of Science, University of Kelaniya Sri Lanka, 2022) Devindi, R. P. D. C. S.
Efficient building design and the accurate computation of the heating and cooling loads of the heating and cooling equipment are required in order to ensure comfortable indoor air conditioning. In order to estimate the required cooling and heating capacities, architects and building designers need information about the characteristics of the building and the conditioned space. We are focusing on the calculation of the energy efficiency of the existing buildings with the study of the UC-Irvine energy efficiency dataset. The dataset has two response variables (heating load and cooling load) and eight explanatory variables (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution). First, the correlation between these two-response variables and other variables was calculated and the correlation among all the variables was studied. As obtained in the correlation matrix, the correlation between the two response variables is very high (i.e., 0.976). Hence by studying one of the response variables, we can predict the values of other response variables. Therefore, we investigated the effect of eight explanatory variables on heating load. The graphical and tabular analysis is used to analyse the features of the data set. Linear regression is handled to grope the relationship between response and explanatory variables. The Box-Cox method is used to find the optimal transformation for the response variable and Ordinary Least Squares (OLS) and Akaike Information Criteria (AIC) are used to select the best-fitted model. From recorded data, the correlation between the two response variables is very high (i.e., 0.976). When considering heating load as the only response variable, the variable “Overall Height” has a perfect correlation with the response variable heating load (i.e., 0.889). Hence, we can say that the variable “Overall Height” is a good predictor of the response variable heating load. Also, the variable “Relative Compactness” has a good relationship with the response variable, (i.e., 0.622), so it is also a good predictor of the response variable. The scatterplot concurs with the fact that there is a linear relationship between the two response variables. In the variable visualization, there might be an interaction between variables glazing area and “Overall Height”. The variable “Overall Height” is the highest positive correlated feature of the data set. The regression model reveals that the weighted least squared model is the best model for energy-efficiency data. The model indicates that the surface area, wall area, overall height, glazing area and glazing area distribution are most important for heating load.