Browsing by Author "Ekanayake, P"

Now showing 1 - 3 of 3

Open Access
An Analytical Study of the Subject of Education Practice in National Colleges of Education
(Faculty of Humanities and Sciences, SLIIT, 2023-11-01) Welpahala, W. A. S. D.; Ekanayake, P
National Colleges of Education, which contribute greatly to the quality development of Sri Lankan education, are engaged in the role of training teachers and provide necessary human resources for the school system. Currently, 19 National Colleges of Education have been established across the island and are the main institutions involved in pre-service teacher training. The learning teaching process of the teacher directly affects the development of the students in the school system. Therefore, teacher preparation programs greatly affect students’ learning, and as a result the quality and performance of educational practice programs implemented in National Colleges of education should be investigated. Thus, this research has been done to study how education practice subject in the National Colleges of Education contributes to the development of teaching professional skills. The main problem of this research is the analytical study of the content of the education practice subject in the National Colleges of Education and how it works. The study also aims to examine the contribution of education practice subject to the development of the professional skills of the National Colleges of Education trainees. In relation to the main research problem, the nature of the education practice subject implemented in the National Colleges of Education and its implementation status in the institutional training as well as the student perception of the subject have been investigated. The convergent parallel design has been used for this research under mixed research methodology. The teacher trainees of five national colleges of education were selected under the simple random sampling method, the lecturers were selected under the purposive sampling method and a sample size of three hundred and thirty-five has been used for this research. In this research, strategies for modernizing the educational practice subject have been discussed. This research which examines the subject of educational practice in the National Colleges of Education will be useful for the professional development of the student teachers studying in the colleges and for future research.
Open Access
Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka
(Hindawi, 2021-05) Ekanayake, P; Rankothge, W; Weliwatta, R; Jayasinghe, J. M. J. W
This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%.
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.