Faculty of Engineering

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Open Access
Scientific Investigation of Ancient Sri Lankan Private Labor Room (Thimbiri Geya)
(Department of Archaeology, University of Kelaniya, 2020) Rathnayake, U. S; Suratissa, D. M; Hashan, T; Siriwardena, K. N. T; Udugama, D. C
Sri Lanka is a proud nation in the world for its ancient architectural and irrigational structures. Unlike today, the hospitals were not served for purpose of natural labor and delivery. Most of the houses have had a private labor room (Thimbiri Geya) for the purpose. However, the architectural plan of the labor room was different from the other bedrooms of the house. The room had provided the better quality of hygienic level and health conditions to the expecting mothers and the newborn babies. The room was sometimes used for other functions including the control of epidemic diseases, control of post childbirth psychosis and for healing wounds. It is interested to understand the scientific concepts behind this labor room and then, to learn and practice them if possible, for today’s world. Therefore, experiments were carried out using three sample labor rooms (3×4 square feet sized) under the same environmental conditions to scientifically investigate the ancient architecture. Unit A were constructed similar to the ancient labor room while unit B was constructed similar to the ancient labor room, but the walls were built by cement blocks and unit C was constructed according to the modern-day room with cement floors. These three rooms were monitored for atmospheric temperature, atmospheric humidity, dissolved oxygen (DO) of water samples of well water and pipe born water and microbial actions on some selected food (bread, meat and fish). It was found out that the room temperature and humidity levels of unit A were much lower to the other rooms and the three strata of floor in unit A could be reason for those. In addition, higher DO levels and lower microbial activities were recorded in unit A. The results suggest the usage of ancient system is a way forward approach in the path of sustainability in health care facilities in the modern world. However, it is also advised to have more experiments in a longer time span to reveal more interesting features of the ancient labor room (Thimbiri Geya) in Sri Lanka
Open Access
Regression-Based Prediction of Power Generation at Samanalawewa Hydropower Plant in Sri Lanka Using Machine Learning
(Hindawi, 2021-07-31) Ekanayake, P; Wickramasinghe, L; Jayasinghe, J. M; Rathnayake, U. S
This paper presents the development of models for the prediction of power generation at the Samanalawewa hydropower plant, which is one of the major power stations in Sri Lanka. Four regression-based machine learning and statistical techniques were applied to develop the prediction models. Rainfall data at six locations in the catchment area of the Samanalawewa reservoir from 1993 to 2019 were used as the main input variables. The minimum and maximum temperature and evaporation at the reservoir site were also incorporated. The collinearities between the variables were investigated in terms of Pearson’s and Spearman’s correlation coefficients. It was found that rainfall at one location is less impactful on power generation, while that at other locations are highly correlated with each other. Prediction models based on monthly and quarterly data were developed, and their performance was evaluated in terms of the correlation coefficient (R), mean absolute percentage error (MAPE), ratio of the root mean square error (RMSE) to the standard deviation of measured data (RSR), BIAS, and the Nash number. Of the Gaussian process regression (GPR), support vector regression (SVR), multiple linear regression (MLR), and power regression (PR), the machine learning techniques (GPR and SVR) produced the comparably accurate prediction models. Being the most accurate prediction model, the GPR produced the best correlation coefficient closer to 1 with a very less error. This model could be used in predicting the hydropower generation at the Samanalawewa power station using the rainfall forecast.
Embargo
A new hybrid fuzzy time series model with an application to predict PM10 concentration
(www.elsevier.com/locate/ecoenv, 2021-10-28) Alyousifi, Y; Othman, M; Husin, A; Rathnayake, U. S
Fuzzy time series (FTS) forecasting models show a great performance in predicting time series, such as air pollution time series. However, they have caused major issues by utilizing random partitioning of the universe of discourse and ignoring repeated fuzzy sets. In this study, a novel hybrid forecasting model by integrating fuzzy time series to Markov chain and C-Means clustering techniques with an optimal number of clusters is presented. This hybridization contributes to generating effective lengths of intervals and thus, improving the model accuracy. The proposed model was verified and validated with real time series data sets, which are the benchmark data of actual trading of Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and PM10 concentration data from Melaka, Malaysia. In addition, a comparison was made with some existing fuzzy time series models. Furthermore, the mean absolute percentage error, mean squared error and Theil's U statistic were calculated as evaluation criteria to illustrate the performance of the proposed model. The empirical analysis shows that the proposed model handles the time series data sets more efficiently and provides better overall forecasting results than existing FTS models. The results prove that the proposed model has greatly improved the prediction accuracy, for which it outperforms several fuzzy time series models. Therefore, it can be concluded that the proposed model is a better option for forecasting air pollution parameters and any kind of random parameters.
Open Access
Hydrologic utility of satellite-based and gauge-based gridded precipitation products in the Huai Bang Sai watershed of Northeastern Thailand
(https://www.mdpi.com/journal/hydrology, 2021-11-01) Gunathilake, M; Zamri, M. N. M; Alagiyawanna, T; Samarasinghe, J; Baddewela, P; Babel, M; Jha, M; Rathnayake, U. S
Accurate rainfall estimates are important in many hydrologic activities. Rainfall data are retrieved from rain gauges (RGs), satellites, radars, and re-analysis products. The accuracy of gauge-based gridded precipitation products (GbGPPs) relies on the distribution of RGs and the quality of rainfall data records obtained from these. The accuracy of satellite-based precipitation products (SbPPs) depends on many factors, including basin climatology, basin topography, precipitation mechanism, etc. The hydrologic utility of different precipitation products was examined in many developed regions; however, less focused on the developing world. The Huai Bang Sai (HBS) watershed in north-eastern Thailand is a less focused but an important catchment that significantly contributes to the water resources in Thailand. Therefore, this research presents the investigation results of the hydrologic utility of SbPPs and GbGPPs in the HBS watershed. The efficiency of nine SbPPs (including 3B42, 3B42-RT, PERSIANN, PERSIANN-CCS, PERSIANN-CDR, CHIRPS, CMORPH, IMERG, and MSWEP) and three GbGPPs (including APHRODITE_V1801, APHRODITE_V1901, and GPCC) was examined by simulating streamflow of the HBS watershed through the Soil & Water Assessment Tool (SWAT), hydrologic model. Subsequently, the streamflow simulation capacity of the hydrological model for different precipitation products was compared against observed streamflow records by using the same set of calibrated parameters used for an RG simulated scenario. The 3B42 product outperformed other SbPPS with a higher Nash–Sutcliffe Efficiency (NSEmonthly>0.55), while APHRODITE_V1901 (NSEmonthly>0.53) performed fairly well in the GbGPPs category with closer agreements with observed streamflow. In addition, the CMORPH precipitation product has not performed well in capturing observed rainfall and subsequently in simulating streamflow (NSEmonthly<0) of the HBS. Furthermore, MSWEP and CHIRPS products have performed fairly well during calibration; however, they showcased a lowered performance for validation. Therefore, the results suggest that accurate precipitation data is the major governing factor in streamflow modeling performances. The research outcomes would capture the interest of all stakeholders, including farmers, meteorologists, agriculturists, river basin managers, and hydrologists for potential applications in the tropical humid regions of the world. Moreover, 3B42 and APHRODITE_V1901 precipitation products show promising prospects for the tropical humid regions of the world for hydrologic modeling and climatological studies.
Open 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.
Open Access
Projected Moisture Index (MI) for tropical Sri Lanka
(Hindawi, 2021-12) Wickramarachchi, C; Samarasinghe, T; Alyousifi, Y; Rathnayake, U. S
Atmospheric moisture loading can cause a great impact on the performance and integrity of building exteriors in a tropical climate. Buildings can be highly impacted due to the changing climate conditions over the world. Therefore, it is important to incorporate the projected changes of moisture loads in structural designs under changing climates. The moisture index (MI) is widely used in many countries as a climate-based indicator to guide the building designs for their durability performance. However, this was hardly considered in structural designs in Sri Lanka, even though the country is one of the most affected countries under climate change. Therefore, this study investigates future climate change impacts on the environmental moisture in terms of MI, which can be used in climate zoning, investigating indoor air quality, understanding thermal comfort and energy consumption, etc. The moisture index was found as a function of the drying index (DI) and wetting index (WI) to the whole country for its four rainfall seasons. The temporal and spatial distributions were plotted as MI maps and showcased under two categories; including historical MI maps (1990–2004) and future projected MI maps (2021–2040, 2041–2070, and 2071–2100). Future projected MI maps were constructed using bias-corrected climatic data for two RCP climatic scenarios (RCP4.5 and RCP8.5). Results showed that the temporal and spatial variations of MIs are justifiable to the country’s rainfall patterns and seasons. However, notable increases of MIs can be observed for future projected MIs in two seasons, and thus a careful investigation of their impacts should be assessed in terms of the construction of buildings and various agricultural activities. Therefore, the outcome of this research can be essentially used in policy implementation in adapting to the ongoing climate changes in Sri Lanka.
Open 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
Open Access
Comparison of different analyzing techniques in identifying rainfall trends for Colombo, Sri Lanka
(Hindawi, 2020-08) Perera, A; Ranasinghe, T; Gunathilake, M. B; Rathnayake, U. S
Identifying rainfall trends in highly urbanized area is extremely important for various planning and implementation activities, including designing, maintaining and controlling of water distribution networks and sewer networks and mitigating flood damages. However, different available methods in trend analysis may produce comparable and contrasting results. Therefore, this paper presents an attempt in comparing some of the trend analysis methods using one of the highly urbanized areas in Sri Lanka, Colombo. Recorded rainfall data for 10 gauging stations for 30 years were tested using the MannKendall test, Sen’s slope estimator, Spearman’s rho test, and innovative graphical method. Results showcased comparable findings among three trend identification methods. Even though the graphical method is easier, it is advised to use it with a proper statistical method due to its identification difficulties when the data scatter has some outliers. Nevertheless, it was found herein that Colombo is under a downward rainfall trend in the month of July where the area receives its major rainfall events. In addition, the area has several upward rainfall trends over the minor seasons and in the annual scale. Therefore, the water management activities in the area have to be revisited for a sustainable use of water resources.
Open Access
Comparison of statistical, graphical and wavelet transform analyses for rainfall trends and patterns in Badulu Oya catchment Sri Lanka
(Hindawi, 2020-09) Ruwangika, A. M; Perera, A; Rathnayake, U. S
Climate change has adversely influenced many activities. It has increased the intensified precipitation events in some places and decreased the precipitation in some other places. In addition, some research studies revealed that the climate change has moved seasons in the temporal scale. Therefore, the changes can be seen in both spatial and temporal scales. Thus, analyzing climate change in the localized environments is highly essential. Rainfall trend analysis in a localized catchment can improve many aspects of water resource management not only to the catchment itself but also to some of the related other catchments. This research is carried to identify the rainfall trends in Badulu Oya catchment, Sri Lanka. The catchment is important as it is in the intermediate climate zone and rich in agricultural productions. Four rain gauges (namely, Badulla, Kandekatiya, Lower Spring Valley, and Ledgerwatte Estate) were used to analyze the rainfalls in the resolutions of monthly, seasonally, and annually. 30-year monthly cumulative rainfall data for the above four gauging stations are analyzed using various standard tests. Nonparametric tests including Mann–Kendall test and sequential Mann–Kendall test and innovative trend analysis methods are used to identify the potential rainfall trends in Badulu Oya catchment. In addition, continuous wavelet transforms and discrete wavelet transforms tests are carried out to check the patterns on rainfall to the catchment. The trend analysis methods are compared against each other to identify the better technique. The results reveal that the nonparametric Mann–Kendall test is powerful to produce the statistically significant rainfall trends in qualitative and quantitative manner. Mann–Kendall analysis shows a positive trend to Ledgerwatte Estate in monthly (3.7 mm in February and 7.4 mm in October), seasonal (6.9 mm in the 2ndintermonsoon), and annual (3 mm annually) scales. However, the analysis records one decreasing rainfall trend to Kandekatiya (8.1 mm in December) only in monthly scale. Nevertheless, it was found that the graphical method can be easily used in qualitative analysis, while discrete wavelet transformations are efficient in identifying the rainfall patterns effectively.
Open Access
Relationships between climatic factors to the paddy yeild: A case study from North-Western province of Sri Lanka
(Smart Computing and Systems Engineering, 2020, 2020-09-23) Wickramasinghe, L; Jayasinghe, J. M. J. W; Rathnayake, U. S
Climate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.

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