Research Papers - Department of Civil Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/598

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Subject: search.filters.subject.Innovative trend analysis

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    PublicationOpen Access
    Spatiotemporal rainfall variability and trend analysis over Mahaweli Basin, Sri Lanka
    (Springer, Cham, 2022-02-07) Rathnayake, U; Pawar, U
    The hydrometeorological characteristics of the Mahaweli Basin are infuenced by rainfall distribution. For that reason, it is signifcant to identify spatiotemporal rainfall fuctuations and trends over the Mahaweli Basin. Accordingly, rainfall data from 1990 to 2019 available for the 15 raingauge stations were analyzed for rainfall variability and trends. Serial autocorrelation was checked before applying rainfall time series data to Mann–Kendall (MK) test. The result exhibited no serial autocorrelation in the data. The MK test, Sen’s slope estimator (SSE), and innovative trend analysis (ITA) were applied to recognize rainfall trends. The inverse distance weighting (IDW) interpolation method was applied to show the spatial pattern of rainfall characteristics with the support of ArcGIS 10.1. Some fuctuations were observed in the rainfall over the 30 years with decreasing and increasing trends. Nevertheless, signifcant trends in the annual rainfall were noted for Bandarawela (+15.7 mm), Ledgerwatta (+40.3 mm), Duckwari (−36.3 mm), and Bakamuna (24.3 mm). At the basin scale, no signifcant trends were noted in rainfall of the Mahaweli Basin. The rainfall trend analysis results obtained by ITA have validated the results of the nonparametric test. Therefore, the analysis showed that despite the seasonal variations in rainfall over the Mahaweli Basin, rainfall is regular, and results acquired by MK test, SSE, and ITA methods are reliable.
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    PublicationOpen Access
    Trend analysis and change point detection of air pollution index in Malaysia
    (Springer Berlin Heidelberg, 2021-11-21) Alyousifi, Y; Ibrahim, K; Zin, W. Z. W; Rathnayake, U. S
    Parametric methods are commonly used to conduct the trend analysis of air pollution. These methods require certain statistical assumptions, such as stationarity and normality of the data. However, such assumptions are usually not applicable to trends in Air pollution index (API). In addition, the change points in the time series have not been taken into consideration by most of the analysis of API. Therefore, this study presents a comprehensive investigation of the trend analysis and change point detection of the mean and maximum of API series in Malaysia. The hourly, daily, weekly, monthly, seasonal, and annual API data series were considered in the analysis. The fner time intervals were used to detect any signifcant increasing or decreasing trends of the API series for Malaysia. The API data were collected from 37 air monitoring stations in Peninsular Malaysia. The nonparametric tests, including Mann–Kendall test, Pettitt test, and innovative trend analysis were used to examine the contribution presented herein. Various aspects of API data were studied, including upward trends, downward trends, and change points. Several signifcant monotonic trends and changing points in some of the API measuring stations were found from the Mann–Kendall test results. Signifcant increasing trends of the monthly and seasonal mean, as well as maximum API, were found in the years 2013 and 2014 for some stations. In addition, the magnitudes of the increasing trends in maximum API are larger than the mean API. The detection points captured by the Pettitt test are possibly related to the El-Nino events. In general, the results of the study provide comprehensive information on air quality trends and their atmospheric aspects, which can help in developing strategies to address air quality problems and provide meaningful opportunities to mitigate air pollution problems in Peninsular Malaysia.