Research Publications

Now showing 1 - 3 of 3

Open Access
Forecasting Global Annual Average CO2 Concentrations
(Faculty of Humanities and Sciences, SLIIT, 2024-12-04) Rasanjali, R.P.B; Tharupathi, M.D.G; Dharmarathne, S.R.J.M; Weerakoon, M.M; Peris, T.S.G
This study aims to enhance the accuracy of CO₂ level forecasts, compare the effi cacy of diff erent predicti ve models, and provide insights for policy development. Employing ti me series and regression analysis techniques, the study uses historical data from global monitoring stati ons (1979- 2022) to model the annual mean concentrati on of atmospheric CO2 The results reveal that the ARIMA (1,1,1) model outperforms the simple linear regression model in predicti ve accuracy. Nevertheless, the regression model came across a technical problem as residuals are signifi cantly autocorrelated. The Augmented Dickey-Fuller test was applied to ensure stati onarity of the fi rst diff erence of the original series. The model was trained using data from 1979 to 2022 and validated for 2023. The errors of the ARIMA(1,1,1) was found to be white noise. The ARIMA model projected CO₂ concentrati ons of 419.5, 421.8 and 424.2 for the years 2023, 2024, and 2025 respecti vely, with a percentage error of just 0.048% for the 2023. In contrast, the corresponding percentage of error for the simple linear regression model was -1.236%. These fi ndings underscore the ARIMA model’s superior performance in forecasti ng future CO₂ levels and its suitability for environmental monitoring and climate change miti gati on strategies. This research provides a valuable methodological framework for future atmospheric science studies and informs policy decisions aimed at addressing rising CO₂ concentrations.
Embargo
CricSquad: A System to Recommend Ideal Players to a Particular Match and Predict the Outcome of the Match
(IEEE, 2023-06-12) Lekamge, E. L.; Wickramasinghe, K. R.; Gamage, S. E.; Thennakoon, T. M. K. L.; Haddela, P.S; Senaratne, S
Selection of the cricket squad plays a very important role in the outcome of the match. This work is about selecting ideal players for a cricket match and predicting the outcome of the match according to the selected cricket team. A cricket squad consist of around 15 to 16 players, with different expertise in batting, bowling, fielding. To select players for the squad, points were calculated using a statistical approach considering player’s overall career data. And then for the further use of selecting players for the squad next match performance of each and every player were predicted using Machine Learning techniques. Association rule mining was used to find frequent winning player combinations with day/night, home/away, batting first/second, against different opponent combinations. Finally calculate points for each player in both teams, then predict the outcome of the match with classification algorithms by considering the calculated total points of each team and other factors such as toss outcome, batting inning, day night conditions and venue. As for the results, XG boost regressor has produced the highest R2 score of 0.92 for batsman runs prediction model while random forest regressor has produced the highest R2 score of 0.66 for bowler wickets prediction model. The Gradient Boost Classifier predicted the Outcome of a match with the highest accuracy of 0.92 while the K Nearest Neighbor achieved the lowest accuracy of 0.82 score.
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.

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