Research Publications

Now showing 1 - 3 of 3

Embargo
Machine Learning-Based Prediction of Settled Water Turbidity for Optimizing Alum Dosage in Drinking Water Treatment
(Association for Computing Machinery, Inc, 2026-06-17) Cooray, S; Fernando, D; Jeyachandran, P; Jayakodi, D; Sumathipala, P; Fousdeen, S; Nawarathne, M
Efficient chemical dosing is essential in drinking water treatment plants to ensure effective turbidity removal while minimizing chemical consumption and operational costs. Conventional alum dosing methods rely on manual jar tests and operator experience, which are time-consuming and often inadequate for handling rapid variations in raw water quality. Key parameters such as turbidity, pH, and electrical conductivity significantly influence the coagulation process, making accurate dosage determination a complex and dynamic challenge. This study proposes a machine learning-based approach to support optimal alum dosage selection by predicting settled water turbidity using historical treatment data. The dataset includes raw water turbidity, pH, conductivity, and applied alum dosage, which are used to train regression models capable of capturing nonlinear relationships between input parameters and treatment performance. The study used 7,975 hourly operational records collected from Ambathale Water Treatment Plant during January-December 2024, and model evaluation was performed using a time-aware 80:20 chronological train-test split. Multiple algorithms, including Gradient Boosting, Random Forest, XGBoost, and LightGBM, were explored alongside feature engineering techniques to improve model effectiveness. Experimental results demonstrate that the Random Forest model achieves superior performance, with an R² score of 0.7909, RMSE of 0.5197, and MAE of 0.3166, indicating strong predictive reliability under varying water quality conditions. The trained model enables simulation of different dosing scenarios, allowing identification of the alum dosage that produces the lowest predicted turbidity under varying raw water conditions.The proposed approach provides a practical decision- support solution for improving treatment efficiency, reducing chemical overuse, and supporting more reliable operation of drinking water treatment processes.
Embargo
Supply and Demand Planning of Electricity Power: A Comprehensive Solution
(IEEE, 2019-12-06) Perera, S; Dissanayake, S; Fernando, D; De Silva, S; Rankothge, W
Electrical energy is one of the fastest growing energy demands in the world. Uncertainty in supplying the demand can threaten the social economic aspects of a country. The biggest driver of electrical demand is weather. Climatic changes not only affect the demand but also renewable energy supply. Wind and Solar are two alternative energy sources with less pollution. We have proposed a platform which helps energy providers, energy traders with services related to electricity supply and demand planning, with following modules. (1) Forecasting electricity consumption patterns (2) Forecasting wind power generation (3) Optimizing Load Shedding. Our platform has been implemented using statistical and machine learning techniques: Multi-Linear Regression for consumption prediction, Random forest regression for wind power forecast, and genetic algorithm to optimize load shedding. Our results show that, using our proposed module, we can minimize the imbalance between the supply and demand of electricity by predicting the consumption patterns of consumers, predicting the wind power generation and by selecting the best feeder to be selected for load shedding under given constraints.
Open Access
Testing the Validity of Purchasing Power Parity: A Comparison of Sri Lanka and Pakistan
(SSRN, 2021-05) Nagendrakumar, N; Madhavika, W. D. N; Abusaly, H; Nawarathna, N. M. D; Yohan, H. P. Y. S; Attanayaka, L. G; Fernando, D
This study investigates the strong and the weak relationship between macroeconomic variables and the purchasing power parity of Sri Lanka and Pakistan. Purchasing power parity is compared with the relative price level of identical product available in both countries. This paper includes 20 years of macroeconomic annual data from 1997 to 2016. These data have been analyzed using descriptive statistic, reliability test and time series multiple regression. Result reveals that real exchange rate is not constant in both economies of Sri Lanka and Pakistan, and this illustrates Sri Lanka has weak relationship between the purchasing power parity and exchange rate, inflation, interest rate, money supply, gross domestic product, foreign direct investment, whereas Pakistan has strong relationship between the selected macroeconomic variables and the purchasing power parity. This study helps enhance knowledge about how purchasing power parity affects the growth of the economies

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