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Subject: search.filters.subject.Trend analysis

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Assessing Statistical Methods for Generating Forecasts for COVID-19
    (Faculty of Humanities and Sciences, SLIIT, 2024-06-09) Siriwardena, S. M. D. G. A; Dharmaratne, G.; Amaratunga, D.
    The COVID-19 pandemic, a persistent global health emergency that has affected almost all facets of daily life, was initially discovered in Wuhan, China, in December 2019. Since that time, the virus has rapidly spread over the globe, causing serious social and economic upheavals necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile real-world data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing and filtering strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. This study also discovered that the ARIMA model, when applied directly to the original data without using any smoothing or filtering approaches, failed to forecast adequately, thereby demonstrating the insufficiency of the ARIMA model on its own to provide credible forecasts when given a volatile set of data.
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    PublicationOpen Access
    Data Smoothing and Other Methods for Generating Forecasts for COVID-19 Cases in Sri Lanka
    (Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Siriwardena, G; Dharmaratne, G; Amaratunga, D
    The COVID-19 pandemic has significantly impacted global society, including Sri Lanka, necessitating the need for reliable forecasting methods. This study compares ten distinct models to predict the number of confirmed COVID-19 cases in Sri Lanka, aiming to assess the performance of statistical models using limited and volatile realworld data characterized by trends, random peaks, and autocorrelations. In addition to the classical ARIMA model, various smoothing and filtering techniques were explored to capture the unique characteristics of the data. The model consistencies in multiple-day predictions were demonstrated, and robust evaluation criteria, along with non-robust measures, were utilized to enhance the effectiveness of the evaluation process. The results highlight the effectiveness of traditional smoothing strategies such as Simple Exponential Smoothing, Holt’s Exponential Smoothing, and the Smoothing Splines technique coupled with the ARIMA model. Notably, applying the ARIMA model directly to the original data without smoothing or filtering approaches yielded inadequate forecasts, underscoring its limitations in volatile data settings.
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    PublicationOpen Access
    Climate Variation and Hydropower Generation in Samanalawewa Hydropower Scheme, Sri Lanka
    (Institution of Engineers, Sri Lanka, 2020-07) Laksiri, K; Rathnayake, U. S; Dabare, G; Gunathilake, M. B; Miguntanna, N
    Climate variation is a challenging scenario on water resources. Therefore, runoffbased hydropower development stations are at an alarming situation across the world and the hydropower industry has significantly been affected. Therefore, it would be interesting to understand the impact of climate change on hydropower development in a country, where a significant energy contribution takes place by the renewable hydropower. However, such studies in Sri Lanka are limited mainly due to data scarcity. Nevertheless, this study was carried out to understand the relationships between the rainfall and the hydropower development in one of the major hydropower developments in Sri Lanka, Samanalawewa hydropower station. Non-parametric statistical trend analyses were carried out to the monthly rainfall over 26 years for the catchment rainfall. As the initial step, the link between rainfall and hydropower development was tested using the Pearson’s correlation coefficient. Interestingly, results revealed positive rainfall trends over the catchment. The correlation coefficient suggests that there is an acceptable correlation between the rainfall and the hydropower development. However, non-linear analysis is proposed to achieve more sound conclusions. Initial results revealed that there is no adverse impact to the inflow of the reservoir due to the on-going climate change
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    PublicationOpen Access
    Impact of climate variability on hydropower generation in an un-gauged catchment: Erathna run-of-the-river hydropower plant, Sri Lanka
    (Springer International Publishing, 2019-04) Perera, A; Rathnayaka, U. S
    Impact of climate change or climate variability on water resources is an exceedingly concerned issue. Hydropower development is one of the most affected industries due to the climatic variability. Therefore, this paper presents the promising results from a study of the impact of climate variability on hydropower generation of Erathna run-of-the-river (ROR) hydropower plant located in Rathnapura district, Sri Lanka. This study was based on surrounded rain gauges outside the catchment as Erathna catchment area is an un-gauged catchment. 30-year rainfall trend analysis from 1988 to 2017 was done using Mann–Kendall and Sen’s slope estimator tests to predict the available trends. Pearson’s correlation coefficient was used to investigate the relationship between rainfall and Erathna power generation. Results show negative trends for annual rainfalls in several rain gauges, while seasonal trend analyses support that observation. July is the most critical month for most of the rain gauges around the catchment. The results also show a good correlation between the rainfalls and power generation. Therefore, the results conclude the importance of rainfall trend analysis in un-gauged catchments and its forecasting capacity of water resources usage in hydropower development.