Browsing by Author "Kirisanth, S."

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Identify the Best model to Forecast the Monthly Rainfall in Jaffna District, Sri Lanka by Using Time Series Analysis
    (Uva Wellassa University of Sri Lanka, 2021) Selvabavitha, V.; Anuthrika, T.; Kirisanth, S.; Yogaraja, B.
    Rainfall is an important component of the water cycle and is the main of giving fresh water to the earth. Also, rainfall is one of the most significant climatic elements that has a direct impact on agriculture. The Jaffna district is in the Dry Zone of Sri Lanka and the major source of water for agricultural production in the district is rainfall which receives mainly during October to December. However, in real-world practice, rainfall data have a seasonal pattern with short-term and long-term fluctuations; and therefore, forecasting monthly rainfall is important for making decisions in daily human activities and agriculture. The main purpose of this study was to find a suitable Seasonal Auto Regression Integrated Moving Average (SARIMA) model to the monthly rainfall data of the Jaffna district. In this study, the monthly rainfall of the Jaffna district is modelled by Box-Jenkins‟ time series approach. The 228 monthly rainfall data were gathered from the Department of Meteorology, Sri Lanka during the period of January 2002 to December 2020. Further, three statistical criteria; Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Mean Squared Error (MSE) were used in order to select the best model. According to minimum AIC, BIC, and MSE, it was found that Seasonal Auto Regressive Integrated Moving Average: is the best fitting model for the Jaffna district. Finally, the Ljung-box test was used to determine whether this fitted best model is adequate. Hence, the identified model can be used to assist scientists and policymakers in developing strategies for effective monitoring and mitigation of flood, urban planning, irrigation water management and other environmental management purposes. Keywords: Box-Jenkins‟ Approach; Ljung- Box Chi-squared statistics; Monthly rainfall; SARIMA model
  • Thumbnail Image
    Item
    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.