Research Papers - Department of Civil Engineering
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Publication Open Access Impact of urbanization on earth resources in suburbs of Colombo, Sri Lanka(NSF: Colombo, 2019) Rathnayake, U. SClimate change is believed to be a critical issue and there is enough evidence to identify the impact of climate change. Sri Lanka is expected to be one of the most affected countries from adverse impact of climate change. Various climatic models propose a rise of rainfall intensity to south Asian region while the number of rainy days are to be reduced. Therefore, the necessity is raised to find the clear trends in climatic factors in the region. However, a very few research work was carried out to see the climatic changes over the last few decades in Sri Lanka. Temporal variation of precipitation (rainfall) can be a good indicator to identify the trends in climate. In addition, these rainfall variations are used in many engineering aspects, including design of massive civil engineering structures like dams, design of water supply networks, etc. Furthermore, the rainfall variations are not only important in engineering aspects but also heavily in agriculture. Therefore, this research work targets to find the temporal variations of rainfall n Sri Lanka and then, to project the results to the available water resources.Publication 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. SThis 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.Publication Open Access Hydrological assessment of flow in Uma Oya, Sri Lanka(Faculty of Engineering, Sri Lanka Institute of Information Technology, 2015-01-29) Rathnayake, U. SA current meter is usually used in river flow measurements. However, if someone is interested in obtaining the temporal variation of a particular river, it may not be the easiest method in the world to use a current meter (i.e. daily) to measure the flow rate. In such events, the stage measurements can be taken and then, they can be converted to the flow rates (USGS, 2014). One can use the stage-discharge relationship to find the corresponding flow rate (Mortuza et al., 2011; Raj and Azeez, 2009; Gupta and Chakrapani, 2005). However, this method still requires some flow measurements to produce the stage-discharge relationship. Therefore, a current meter should be there to measure the velocities and then, to calculate the flow rates. In case of absence of a current meter, one has to think another way of obtaining the flow hydrograph. This paper presents a simple approach in obtaining the flow hydrograph for a river in Sri Lanka: Uma Oya. Uma Oya catchment is being modeled and this study shows some preliminary results. The detailed flow hydrograph for Uma Oya for a longer period is being developed for the frequency analysis. The developed flow hydrograph is being used to model the Uma Oya catchment in Sri LankaPublication Open Access Urbanization and initial groundwater quality investigation in Malabe, Sri Lanka(https://www.tci-thaijo.org/index.php/easr/article/view/91589, 2018-06) Rajapakshe, A; Rathnayake, U. SMalabe, an eastern suburb of the capitalcity of Colombo, is one of the most rapidly urbanized and industrialized areasin Sri Lanka. Groundwater is avaluable resource inMalabe and itis being polluted. Malabe is located in a wet climatic zone with alateritic aquifer thatnormally contains water with avery low pH thatcan cause quality problems. Our objective was to investigate and analyzethe Malabe groundwater quality to understand the characteristics of significant parameters and their correlation so that policy planning can be correctly done. Groundwater samples from 16 water wells were collected and evaluated for eightphysicochemical parameters,i.e.,pH, turbidity, electrical conductivity (EC), color, nitrate (NO3), nitrite (NO2), sulfate (SO4), and phosphate (PO4). Two biological parameters were also determined for four wells. The essence of this finding is that the groundwater is very acidic, has a very low EC, but high coliform counts. Multivariable statistics of the data were performed usingPearson’scorrelation and principal components analysis (PCA) using the Princom package intheR statistical package. The first four principal components (PCs) explained 79.8 % of the total observed variance inthe data. The most significant parametersfrom the first principalcomponent, PC1, were the positive correlationsof turbidity andPO4, and negative correlationsof EC andNO3. Asignificant positive loading of pH with a negative loading of SO4was illustrated in PC2. These findings were similar to the correlation results. We concludesthat the high acidity of the groundwater is primarily caused by industrial waste. The groundwater pollution of the Malabe area was not cause by inorganic fertilizer but by anthropogenic waste runoff. Our finding is crucial for groundwater quality management inthe study area.Publication 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, NClimate 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 changePublication 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. SIdentifying 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.Publication 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. SClimate 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.Publication Open Access Development of wind power prediction models for Pawan Danavi wind farm in Sri Lanka(Hindawi, 2021-05) Peiris, A. T; Jayasinghe, J. M. J. W.; Rathnayake, U. SThis paper presents the development of wind power prediction models for a wind farm in Sri Lanka using an artificial neural network (ANN), multiple linear regression (MLR), and power regression (PR) techniques. Power generation data over five years since 2015 were used as the dependent variable in modeling, while the corresponding wind speed and ambient temperature values were used as independent variables. Variation of these three variables over time was analyzed to identify monthly, seasonal, and annual patterns. The monthly patterns are coherent with the seasonal monsoon winds exhibiting little annual variation, in the absence of extreme meteorological changes during the period of 2015–2020. The correlation within each pair of variables was also examined by applying statistical techniques, which are presented in terms of Pearson’s and Spearman’s correlation coefficients. The impact of unit increase (or decrease) in the wind speed and ambient temperature around their mean values on the output power was also quantified. Finally, the accuracy of each model was evaluated by means of the correlation coefficient, root mean squared error (RMSE), bias, and the Nash number. All the models demonstrated acceptable accuracy with correlation coefficient and Nash number closer to 1, very low RMSE, and bias closer to 0. Although the ANN-based model is the most accurate due to advanced features in machine learning, it does not express the generated power output in terms of the independent variables. In contrast, the regression-based statistical models of MLR and PR are advantageous, providing an insight into modeling the power generated by the other wind farms in the same region, which are influenced by similar climate conditions.Publication Open Access Relationships between hydropower generation to rainfall – gauged and un-gauged catchments from Sri Lanka(hindawi.com, 2020-07) Rathnayake, U. S; Perera, AThe relationship between the rainfall and minihydropower generation in a catchment is highly nonlinear. Therefore, the prediction of minihydropower generation is complex. However, the prediction is important in optimizing the control of electricity generation under various environmental conditions. Ongoing climate variabilities have completely changed the minihydropower generation to some parts of the world, and it is significant. Therefore, this paper presents results from two soft-computing studies in searching the relationships between rainfall and the generated hydropower. The first study was carried out for a gauged catchment; however, the second was carried for an ungauged catchment. Results revealed that there is an acceptable correlation in between the rainfall and hydropower generation for the gauged catchment and a marginal contribution to the ungauged catchment.Publication Open Access Ecosystem-Based Adaptation for the Impact of Climate Change and Variation in the Water Management Sector of Sri Lanka(Hindawi, 2021-02-25) Khaniya, B; Gunathilake, M. B; Rathnayake, U. SThe climate of Sri Lanka has been fluctuating at an alarming rate during the recent past. These changes are reported to have pronounced impacts on the livelihoods of the people in the country. Water is central to the sustainable functioning of ecosystems and wellbeing of mankind. It is evident that pronounced variations in the climate will negatively impact the availability and the quality of water resources. The ecosystem-based adaptation (EbA) approach has proved to be an effective strategy to address the impact of climate change on water resources in many parts of the world. The key aim of this paper is to elaborate the wide range of benefits received through implementation of EbAs in field level, watershed scale, and urban and coastal environments in the context of Sri Lanka. In addition, this paper discusses the benefits of utilizing EbA solutions over grey infrastructure-based solutions to address the issues related to water management. The wide range of benefits received through implementation of EbAs can be broadly classified into three categories: water supply regulation, water quality regulation, and moderation of extreme events. This paper recommends the utilization of EbAs over grey infrastructure-based solutions in adaptation to climate change in the water management sector for the developing region due its cost effectiveness, ecofriendliness, and multiple benefits received on long-term scales. The findings of this study will unequivocally contribute to filling existing knowledge and research gaps in the context of EbAs to future climate change in Sri Lanka. The suggestions and opinions of this study can be taken into account by decision makers and water resources planning agencies for future planning of actions related to climate change adaptation in Sri Lanka.