Browsing by Author "Perera, S.S.N."

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2-Tuple Fuzzy Linguistic Model to Evaluate the Risk of Invasive Plant Species
(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Peiris, H.O.W.; Perera, S.S.N.; Chakraverty, S.; Ranwala, S.M.W.
Management of invasive species can appear to be a complicated and unending task. In order to manage the spread, these species need to be undergone any risk assessment during their introduction. The aim of this study is to evaluate the aggregate risk of Invasive Alien Species (IAS) using invasive attributes. We use the 2-tuple fuzzy linguistic representation to develop the model without loss of information in which occur in ordinary linguistic operators. These risk values are compared with the National Risk assessment scores which are in the form of Linguistic labels. The proposed model is validated using few known noninvasive species in Sri Lanka. The model gives significant predictions and it is found to be a better tracking system for identifying potential invaders than the conventional risk assessment methods.
Analysis of Factors Affecting USD/LKR Exchange Rate
(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Jayasuriya, D.P.S.H.; Perera, S.S.N.
This paper intends to investigate the factors affecting the US Dollar exchange rate in Sri Lanka, in the period of January 2009 to June 2015, by using the econometric framework of Johanson and Juselius Cointegration, Vector Auto Regressive model, Granger Causality, and Variance Decomposition analysis. The empirical results of the model indicate that the increase in previous month net foreign assets and trade balance, and a decrease in the previous month exchange rate, has a significant influence on the short run appreciation of exchange rate. Granger Causality test confirms past values of net foreign assets, trade balance, and workers’ remittance have a predictive ability in determining the present values of exchange rate while, Variance Decomposition indicate, variation in exchange rate in short term and long term time horizon is due to the exchange rate itself and net foreign assets, trade balance and workers’ remittance respectively.
COMPARISON OF PERFORMANCES OFSELECTED FORECASTING MODELS:AN APPLICATION TO DENGUE DATA IN COLOMBO, SRI LANKA
(Department of Statistics & Computer Science, Faculty of Science,& Research & Development Centre for Mathematical Modelling, Faculty of Science, University of Colombo, Sri Lanka., 2021) Attanayake, A.M.C.H.; Perera, S.S.N.; Liyanage, U.P.
Dengue is a one of the diseases in the world which has no exact treatment to recover from the disease. It is rapidly spreading throughout the world by causing large number of deaths [1]. In Sri Lanka, there is an increase of reported dengue cases over recent years. The majority of dengue cases reported in the Colombo district within the Sri Lanka. Effective dengue management and controlling strategies should be implemented to reduce the deaths from the disease. Modelling and predicting the distribution of the dengue will be useful in detecting outbreaks of the dengue and to execute controlling actions beforehand. The objective of this study is to develop an appropriate modelling technique to predict dengue cases. To accomplish this objective, we have chosen our study area as Colombo, Sri Lanka. Seven modelling techniques, namely, Na¨ıve, Seasonal Na¨ıve, Random Walk with Drift, Mean Forecasting, Autoregressive Integrated Moving Average, Exponential Smoothing and TBATS (Trigonometric, Box-Cox Transformation, ARMA errors, Trend and Seasonal components) [2] were chosen in this study to model dengue data. For model development process, monthly reported dengue cases in Colombo from January 2010 to December 2018 were used and validated using the data from January to December in 2019. Mean error, root mean squared error and mean absolute percentage error measurements were used to select the most parsimonious model to predict dengue cases in Colombo, Sri Lanka. Both Exponential and TBATS models were competed in predicting dengue cases by reporting minimum error measures. Therefore, results disclosed that among the selected methods either Exponential Smoothing model or TBATS model can be used to predict dengue cases in Colombo, Sri Lanka.
An empirical study of domestic electricity demand in Sri Lanka
(Faculty of Graduate Studies, University of Kelaniya, 2015) Dissanayake, D.M.S.E.; Perera, S.S.N.
The domestic electricity consumption in Sri Lanka shows a healthy growth in last 15 years in line with the expansion in Sri Lankan economy. It shows rapid increment after the end of civil war than the years before 2009. It is vital therefore to evaluate the factors effecting to the domestic electricity consumption of the country and to study dynamic interactions of them. The Objectives of this study are fit an Autoregressive Integrated Moving Average (ARIMA) model to compile a forecast for future domestic electricity demand for Sri Lanka and explore the dynamic interactions between domestic electricity consumption and several factors such as unit electricity cost, Gross Domestic Production (GDP), average temperature in Sri Lanka based on a Vector Error Correction model (VECM). Forecasts derived from ARIMA model is good for short term predictions since it is solely depend on the volatility of the data against time. VECM provides more comprehensive estimates incorporating the other factors than the ARIMA model. From 1997 to 2013 quarterly data are used for this study. It is found that ARIMA(3,2,0) model forecast the value with less than 5% of Mean Average Percentage Error(MAPE). Further it is noted that VECM with lag 3 shows GDP as the most affected variable to the domestic electricity demand with less than 5% MAPE.
Evaluation of Service Quality: Mathematical Modeling Approach
(University of Kelaniya, 2012) Surasinghe, N.W.V.S.C.; Perera, S.S.N.
Human behavior may lead to change in parameters, which has been used in the various models which describe different phenomena. Modeling human behavioral patterns is complex and hence a challenging task. This research is also based on an area of human behavior modeling. Here we focus on service quality evaluation. As per business entities, customer satisfaction is the growing fact towards profit maximization and competitiveness. So, it tends to increase researcher’s interest in the topic of Service Quality. It always depends on the behavior of the stakeholders of the business. Especially, as it is being used to measure the level of satisfaction of the customers. Evaluating this satisfactory level is basically finding a Mathematical model for the behavior of the customers. Here, we try to quantify the level of service quality via mathematical modeling approach. When selecting a suitable tool, the vagueness of the customers’ opinions has to be considered. The logical tools that people can rely on are generally considered the outcome of a bivalent logic, but the problems posed by real-life situations and human thought processes and approaches to problemsolving are by no means bivalent. So, fuzzy sets have to be used for the representation of human opinion. Neural network can be used to train the human behavioral patterns and it can be used to find the relationship between the respondent’s view of the service quality criterion and overall ranking of the service. Therefore, fuzzy model & Hybrid model (i.e. combine fuzzy logic with neural network) are used in this research. A case study is done to find out the validity of the proposed model. Fuzzy models have some stability with respect to the result, because it has given confidence to calculate overall quality using given criteria. According to the results of the case study the customers’ overall idea about the same category and the respective calculated value using the criterion are almost the same. Fuzzy set theory together with neural network can be used to tune the accuracy of the model.According to the results of the case study, neural networks simulate the customers’ overall impression with more accuracy. Generated numbers via neural network can be used to analyse the customers’ overall impressions. So, this hybrid model would be able to create a more powerful tool for this subject area.
A Fuzzy Linear Model Using Possibilistic Linear Regression with Least Squares Method: An Application to Dengue and Rainfall Data
(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Attanayake, A.M.C.H.; Perera, S.S.N.; Liyanage, U.P.
Fuzzy linear models deal with vague and imprecise phenomenon in order to represent better models. These type of models are especially suitable in modelling and predicting dengue disease as the disease associated with various unknown and uncontrollable factors. Further, modelling and predicting the dengue disease is important as it is one of the leading diseases in the world which reports higher number of deaths. This study focuses on modelling reported dengue cases in the Colombo district, Sri Lanka. Particularly, Possibilistic Linear Regression with Least Squares (PLRLS) Method was applied as the modelling procedure. This method was proposed by H. Lee and H. Tanaka in 1999 to deal with crisp inputs and fuzzy output. The rainfall as one of the leading climatic factors that associated with dengue disease included in the model as an independent variable. Data consists of weekly reported dengue cases and weekly average rainfall in the Colombo district from 46th week of 2009 to 12th week of 2015. 2009 to 2014 data were used for model development and rest of the data for model validation. Cross correlation analysis revealed that the rainfall with 10 lags was associated with the reported dengue cases. By considering dengue and rainfall data as crisp inputs, the upper approximation model and lower approximation model were obtained to reflect the fuzziness of the dengue count in the district. The developed coefficients of the fuzzy linear regression were in the form of non-symmetric triangular fuzzy numbers. The left and the right spreads of the central value determined the lower and upper boundary of the interval, respectively, where the corresponding degree of membership equals to 0. The predicted values from the fuzzy regression model and the actual values of the validation set were within the upper and lower approximation models which indicated the possibility of the dengue prediction through PLRLS method. The authors are in the process of testing additional fuzzy linear models by changing fuzzy input/output combinations with incorporating more independent variables.
MODELING COVID-19 CASES IN SRI LANKA USING ARIMA MODELS
(The Open University Of Srilanka., 2020) Attanayake, A.M.C.H.; Perera, S.S.N.
COVID-19 (Novel Coronavirus) is a pandemic which spread around the world at an alarming rate. As of 10th June 2020, 1,880 infections and 11 deaths were reported in Sri Lanka due to COVID-19. The number of infections increase day by day requiring research on modelling the pandemic. Modelling of COVID 19 cases will be useful to understand the behavioural patterns of the disease and hence to identify control mechanisms. The aim of this study is to model and predict the daily cumulative COVID-19 cases in Sri Lanka. Autoregressive Integrated Moving Average (ARIMA) technique was applied to model the reported COVID-19 cases in Sri Lanka. Data from 11th March - 1st of June 2020 were used for the model development and data from 2nd - 10th June 2020 (10% of data) were used for model validation. In the analysis, second order differencing removed the non-stationarity of the original series. Different candidate ARIMA models were tested based on ACF and PACF plots and the best ARIMA model was selected based on minimum AIC and BIC measures. The most appropriate ARIMA model for the COVID-19 cases in Sri Lanka is ARIMA (2,2,2). After verifying the assumptions of the model, MAPE of the validation set revealed 1.86%. Therefore, the selected most appropriate model was used to forecast the future COVID-19 cases in Sri Lanka. According to the forecasted values of the model, it can be concluded that COVID19 cases in Sri Lanka will increase slowly in the upcoming days. ARIMA technique is appropriate in only short-term forecasting. Availability of an effective prediction model will be helpful in anticipating the cases and to take timely action to control the COVID-19 incidence. Unexpected recordings cannot be modelled and predicted by the fitted models. Uncertainties limit the effectiveness of a model, specially, in an epidemic like novel coronavirus.
Visualization of Positive Semi Definite Matrices
(19th Conference on Postgraduate Research, International Postgraduate Research Conference 2018, Faculty of Graduate Studies,University of Kelaniya, Sri Lanka, 2018) Ranasinghe, L.P.; Liyanage, U.P.; Perera, S.S.N.
This paper studies on how to identify positive semi definite property of a matrix using a plot. The main difference of positive semi definite matrix and negative semi definite matrix is defined by eigen values. All the eigen values of positive semi definite matrix are non-negative. All the eigen values of negative semi definite matrix are non-positive. This study will help to determine positive semi definite property of a matrix without using matrix calculations and in this research paper, we use positive semi definite matrix, negative semi definite matrix, square and symmetric matrix, non symmetric matrix and non square matrix. 10 by 10 matrices were used for the study except non square matrix. Contour plot was used as a visualization tool. Because of the features of the contour plot, the positive semi definite property of a matrix was identified. The main difference between the contour plot of positive semi definite matrix and contour plot of negative semi definite matrix is location of contour centers. If contour plot represents positive semi definite matrix, then contour centers are all over graph. If contour plot represents negative semi definite matrix, then contour centers lie only in the diagonal. Symmetric property was implied by dividing the contour plot into two equal parts through a line along in the diagonal. If X and Y axis have same ranges in the contour plot, then the contour plot represents square matrix. Therefore, the symmetric property and the square property of matrices were identified from contour plot. If contour centers are all over graph in a contour plot of symmetric and square matrix, then the contour plot represents positive semi definite matrix. We can identify positive semi definite property, symmetric property and square property using contour plot.