Browsing by Author "Varathan, N."

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    Time Series Modeling of Blood in Demand for Kurunegala District, Sri Lanka
    (Uva Wellassa University of Sri Lanka, 2020) De Zoysa, W.D.W.; Varathan, N.
    In general, blood comes into four main Groups; O, A, B, and AB. The most common and highly demanded blood is Group O. Blood can also be subdivided into its main components; red cells, white cells, platelets, and plasma. Unfortunately, red cells only have a shelf-life of 35 or 42 days, while platelet shelf life is even less, only five days. Blood cells are essential components of the human body. Blood cannot be manufactured mechanically and can only be obtained by donation. Human blood pressure and heart rate will stay close to normal as one human loss up to 30% of blood. If they lose more than 40% of blood they will die. It’s important to get to a hospital to start receiving blood transfusion to prevent this. Blood transfusion is generally required in surgeries, childbirths, organ transplants, and for patients who are receiving treatments for diseases such as cancers and anaemia. Therefore, it is essential to study the blood in demand for the near future. According to Sri Lanka, the National Blood Transfusion Service (NBTS) is the sole supplier of blood and blood products to all state hospitals and it has ninety-six blood Banks Island-wide. This study investigates to develop a suitable time series model for the monthly blood demand for Kurunegala district. The data was obtained from the NBTS Sri Lanka, which consists of the monthly demand for red blood cells from January 2011 to November 2017. The modeling has been done using the BoxJenkin’s Auto-Regressive Integrated Moving Average (ARIMA) procedure. Moreover, to identify the best fitting model, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC) and Mean Square Error (MSE) were used. Through the analysis, it was identified that ARIMA (0, 1, 1) is the most appropriate model for the monthly blood in demand for the Kurunegala district. Key words: Blood in demand, Blood groups, Red cells, ARIMA
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    Time Series Modelling of Monthly Rainfall in Kilinochchi District, Sri Lanka
    (Uva Wellassa University of Sri Lanka, 2019-02) Kirisanth, S.; Varathan, N.; Arumairajan, S.
    The amount of rainfall received over an area is an important factor in assessing availability of water to meet various demands for agriculture, industry and irrigation. Kilinochchi is one of districts in Sri Lanka and many people in Kilinochchi district are below the poverty line and mainly depend on the agriculture for their daily life. Rainfall is the main source of watering for agriculture in Kilinochchi. Forecasting rainfall in Kilinochchi district plays an important role in the planning and management of agriculture scheme and management of water resource systems. Therefore, it is essential to develop a time series model to analyze the amount of rainfall in Kilinochchi district. The main goal of this study is to find a suitable Auto Regressive Integrated Moving Average (ARIMA) model to the monthly rainfall data of Kilinochchi district. In this study, the monthly rainfall of Kilinochchi district under three different stations such as Iranamadu, Akkarayankulam, Kariyalanagapaduwan is modelled by using Box-Jenkins’ time series approach. The monthly rainfall data under three different stations in Kilinochchi district was obtained from the department of meteorology, Sri Lanka during the period of January, 1986 to December, 2015. Further, three statistical criteria such as Akaike information criteria, Bayesian information criteria, mean squared error were used in order to select best the time series model. Through the modelling, it was found that Seasonal Auto Regressive Integrated Moving Average: SARIMA (0,1,1) (0,1,3)12 is the best fitting model for all three stations in Kilinochchi district. Moreover, the adequacy of the fitted best model has been tested using Ljung- Box chi-squared statistic. The identified best model can be used to forecast the monthly rainfall of Kilinochchi district in near future.