Scopus Index Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/2162
This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.
Browse
33 results
Search Results
Publication Open Access Nature-Based Urban Drainage Solutions Using Industrial Waste-Incorporated Pervious Concrete Pavements(Multidisciplinary Digital Publishing Institute (MDPI), 2026-03-11) Ratnapala, N; Miguntanna, Nandika; Miguntanna, Nadeeka; Rathnayake, UPervious concrete pavements have gained increasing attention as a sustainable stormwater management solution due to their ability to reduce runoff volume and improve water quality through infiltration. This study investigates the stormwater runoff treatment potential and performance efficiency of pervious concrete pavements incorporating industrial waste materials, namely recycled concrete aggregate (RCA), ceramic waste (C), and waste tires (T), as partial replacements for natural coarse aggregates. Concrete mixes were prepared by replacing 10%, 20%, and 30% of the coarse aggregate volume with each waste material, and the results were compared with normal pervious concrete. Stormwater runoff treatment performance was evaluated by analyzing key water quality parameters, including total suspended solids (TSSs), pH, turbidity, color, and electrical conductivity (EC), using collected urban runoff samples. In addition, mechanical properties (compressive, tensile, and flexural strength) and hydraulic properties (porosity and infiltration rate) were assessed to ensure structural and functional suitability. The results demonstrate that pervious concrete pavements incorporating industrial waste materials exhibit effective pollutant removal while maintaining acceptable mechanical performance in accordance with ASTM standards. Among the investigated pervious concrete types, pavements containing 10% recycled concrete aggregate and 10% ceramic waste showed superior reductions in TSS, turbidity, and color compared to other waste-based and normal pervious concrete mixes. This study demonstrated significant reductions in particulate pollutants (TSS, turbidity, and color), while increases in pH and electrical conductivity highlighted early-age ion leaching from the concrete matrix, underscoring both the treatment benefits and the need for long-term monitoring under realistic deployment conditions. Overall, the findings highlight the potential of industrial waste-based pervious concrete pavements as an environmentally sustainable and effective solution for urban stormwater management.Publication Open Access Uncertainty Reduction in Near Real-time Satellite Precipitation Estimates by Integrating Soil Moisture and Potential Evapotranspiration Using a Machine Learning Approach(Springer Science and Business Media, 2026) Wanniarachchi, S; Sarukkalige, R; Hapuarachchi, H. A. P; Gomes, P.I.A; Rathnayake, UNear-real-time (NRT) satellite precipitation data inherits complex and random errors due to various reasons. The primary objective of this research is to utilize satellite-based precipitation data for hydrological modelling in ungauged areas. The novelty of this study lies in the development of a hybrid stacking-based machine learning framework that integrates hydrologically meaningful predictors: root-zone soil moisture, potential evapotranspiration (PET), and their time-lagged representations to reduce uncertainty in near-real-time satellite precipitation (GSMaP-NRT). Unlike conventional bias-correction approaches that rely primarily on statistical adjustment between satellite and gauge rainfall, this study incorporates physically relevant catchment-state variables to improve predictive skill, with a focus on the Ovens River basin in Australia. A calibrated GR4H hydrological model was used to simulate the runoff of the catchment. Six objective functions were used to evaluate the performance of the approach. The results demonstrate that stacking machine learning algorithms reduces the Mean Absolute Error of GSMaP-NRT satellite precipitation data by 36% and the corresponding modelled streamflow error by 44% for lower precipitation events (< 2 mm/hour). All six objective functions achieved optimal performances within the low precipitation events. However, RMSE remained high for intermediate and heavy precipitation events. The model-estimated major streamflow peaks for the years 2010 and 2016, based on gauged precipitation and ML-corrected satellite precipitation, are 41% and 48% lower than the observed streamflow peaks, respectively. The reasons were the inability of the GR4H model to capture the perfect initial conditions and the x4 time parameter during the calibration process.Publication Open Access QPred: A Lightweight Deep Learning-Based Web Pipeline for Accessible and Scalable Streamflow Forecasting(Tech Science Press, 2026) Makumbura, R.K; Wijesundara, H; Sajindra, H; Rathnayake, U; Kumar, V; Duraibabu, D; Sen, SAccurate streamflow prediction is essential for flood warning, reservoir operation, irrigation scheduling, hydropower planning, and sustainable water management, yet remains challenging due to the complexity of hydrological processes. Although data-driven models often outperform conventional physics-based hydrological modelling approaches, their real-world deployment is limited by cost, infrastructure demands, and the interdisciplinary expertise required. To bridge this gap, this study developed QPred, a regional, lightweight, cost-effective, web-delivered application for daily streamflow forecasting. The study executed an end-to-end workflow, from field data acquisition to accessible web-based deployment for on-demand forecasting. High-resolution rainfall data were recorded with tipping-bucket gauges and loggers, while river water depth in the Aglar and Paligaad watersheds was converted to discharge using site-specific rating curves, resulting in a daily dataset of precipitation, river water level and discharge. Four DL architectures were trained, including vanilla Long Short-Term Memory (LSTM), stacked LSTM, bidirectional LSTM, and Gated Recurrent Unit (GRU), and evaluated using Nash-Sutcliffe Efficiency (NSE), Coefficient of Determination (R2), Root-Mean-Square-Error-Standard-Deviation Ratio (RSR), and Percentage Bias (PBIAS) metrics. Performance was watershed-specific, as the vanilla LSTM demonstrated the best generalisation for the Aglar watershed (R2 = 0.88, NSE = 0.82, RMSE = 0.12 during validation), while the GRU achieved the highest validation accuracy in Paligaad (R2 = 0.88, NSE = 0.88, RMSE = 0.49). All models achieved satisfactory to excellent performance during calibration (R2 > 0.91, NSE > 0.91 for both watersheds), demonstrating strong capability to capture streamflow dynamics. The highest performing models were selected and embedded into the QPred application. QPred was developed as a lightweight web pipeline, utilising Google Colab as the primary execution environment, Flask as the backend inference framework, Google Drive for artefact storage, and Ngrok for secure HTTPS tunnelling. A user-friendly front end utilises range sliders (bounded by observed minima and maxima) to gather inputs and provides discharge data along with metadata, thereby enhancing transparency. This work demonstrates that accurate, context-aware deep learning models can be delivered through low-cost, web-based platforms, providing a reproducible and scalable pipeline for hydrological applications in other watersheds and for practitioners. CopyrightPublication Unknown Enhancing the effectiveness of satellite precipitation products with topographic and seasonal bias correction(Elsevier B.V., 2026-02) Wanniarachchi, S; Sarukkalige, R; Hapuarachchi, H.A. P; Gomes, P.I.A; Rathnayake, UEstimating precipitation distribution across large regions is crucial for understanding water availability, planning infrastructure, and forecasting flood hazards. Traditional gauge-based methods face challenges, particularly with sparse gauge networks. In response, satellite-based, near-real-time (NRT) precipitation data has gained popularity, especially in poorly gauged watersheds. However, satellite precipitation data quality is often compromised by latency, atmospheric complexities, and topographic effects, resulting in nonlinear errors. To overcome the research gap, this study introduces the Heavy Rain Peak Adjustment (HRPA) method alongside the well-established Seasonal Autoregressive Integrated Moving Average (SARIMA) model for satellite precipitation bias correction. The analysis utilised Global Satellite Mapping of Precipitation (GSMaP-NRT) data and hourly precipitation records from 31 rain gauges in the Ovens River region of Australia. On average, the mean residual of observed and GSMaP-NRT precipitation was −0.02 mm. Additionally, the HRPA method yielded better linear regression R2(0.911), NSE (log) (−0.847), and RMSE (0.628) compared to SARIMA. The results indicate that HRPA outperforms SARIMA, particularly at lower elevations, whereas SARIMA struggles at higher elevations, underscoring its limitations in those areas. Additionally, autocorrelation and partial autocorrelation plots for some stations in hilly areas show significant wave-like patterns, indicating greater uncertainty in satellite precipitation estimates over complex terrain. For several stations, autocorrelations at 24 and 48-hour lags suggest a systematic influence of past residuals on future ones, emphasizing the need for further refinement in satellite precipitation correction methods for these regions.Publication Open Access A comprehensive review to evaluate the synergy of intelligent food packaging with modern food technology and artificial intelligence field(Springer link, 2024-07-22) Abekoon A; Sajindra, H; Samarakoon, E. R. J.; Jayakody, J.A.D.C.A; Kantamaneni, K; Rathnayake, U; Buthpitiya, B. L. S. K.This study reviews recent advancements in food science and technology, analyzing their impact on the development of intelligent food packaging within the complex food supply chain. Modern food technology has brought about intelligent food packaging, which includes sensors, indicators, data carriers, and artificial intelligence. This innovative packaging helps monitor food quality and safety. These innovations collectively aim to establish an unbroken chain of food safety, freshness, and traceability, from production to consumption. This research explores the components and technologies of intelligent food packaging, focusing on key indicators like time–temperature indicators, gas indicators, freshness indicators, and pathogen indicators to ensure optimal product quality. It further incorporates various types of sensors, including gas sensors, chemical sensors, biosensors, printed electronics, and electronic noses. It integrates data carriers such as barcodes and radio-frequency identification to enhance the complexity and functionality of this system. The review emphasizes the growing influence of artificial intelligence. It looks at new advances in artificial intelligence that are driving the development of intelligent packaging, making it better at preserving food freshness and quality. This review explores how modern food technologies, especially artificial intelligence integration, are revolutionizing intelligent packaging for food safety, quality, reduced waste, and enhanced traceability.Publication Open Access Data exploration on the factors associated with cost overrun on social housing projects in Trinidad and Tobago(Elsevier Ltd, 2024-02) Chadee, A. A; Allis, C; Rathnayake, U; Martin, H; Azamathulla, H. MThis data article explores the factors that contribute to cost overrun on public sector projects within Trinidad and Tobago. The data was obtained through literature research, and structured questionnaires, designed using open-ended questions and the Likert scale. The responses were gathered from project actors and decision-makers within the public and private construction industry, mainly, project managers, contractors, engineers, architects, and consultants. The dataset was analysed using frequency, simple percentage, mean, risk impact, and fuzzy logic via the fuzzy synthetic evaluation method (FSE). The significance of the analysed data is to determine the critical root causes of cost overrun which affect public sector infrastructure development projects (PSIDPs), from being completed on time and within budget. The dataset is most useful to project and construction management professionals and academia, to provide additional insight into the understanding of the leading factors associated with cost overrun and the critical group in which they occur (political factors). Such understanding can encourage greater decisions under uncertainty and complexity, thus accounting for and reducing cost overrun on public sector projects. © 2023Publication Open Access A novel deep learning model to predict the soil nutrient levels (N, P, and K) in cabbage cultivation(Elsevier, 2024-03) Sajindra, H; Abekoon, T; Jayakody, J.A.D.C.A.; Rathnayake, UCabbage (Brassica oleracea) is a green cruciferous vegetable. Major nutrients (nitrogen, phosphorus, and potassium) are frequently applied to the soil due to low fertility levels. However, optimizing required fertilizer levels are extremely important to avoid any overuse and underuse. Therefore, it is important to develop a comprehensive methodology for evaluating the major nutrients in the soil. In this research, a deep learning model was introduced to predict the nitrogen, phosphorus, and potassium content of the soil by analyzing the growing characteristics of the plants, such as plant height, the number of leaves, and the average leaf area of the plant. To achieve this, the growing characteristics of the cabbage plants were recorded weekly along with the respective soil nitrogen, phosphorus, and potassium content of the nearby soil. After the data was trained using the Levenberg–Marquardt algorithm and tested with different transfer functions such as logarithmic sigmoid, pure linear, and tangent sigmoid, better predictions were obtained through the model. According to the Pearson correlation values, pure linear and tangent sigmoid showed higher values, ranging from 0.99 for training, testing, validation, and all data points from the model, indicating a strong relationship between the actual and predicted values. According to the Mean Square Error values, the tangent sigmoid transfer function outperformed the others, giving a value of 1.0813, indicating better predictions of the soil nitrogen, phosphorus, and potassium content from the modelPublication Open Access Image processing techniques to identify tomato quality under market conditions(Elsevier B.V., 2024-03) Abekoon, T; Sajindra, H; Jayakody, J．A．D．C．A．; Samarakoon, E．R．J; Rathnayake, UTomatoes are essential in both agriculture and culinary spheres, demanding rigorous quality assessment. It is highly advantageous to discern the maturity level and the time range post-harvesting of tomatoes in the market through visual analysis of their images. This research endeavors to forecast tomato quality by accurately determining the maturity level and the duration post-harvest, specifically tailored to Sri Lankan market conditions, with a particular focus on Padma tomatoes. It identifies maturity stages (Green, Breakers, Turning, Pink, Light Red, Red) and post-harvest dates using image processing techniques. Greenhouse-grown Padma tomatoes mimic market conditions for image capture, and Convolutional Neural Networks facilitate this analysis. Model 1, using ReLU and sigmoid activation functions, accurately classifies tomatoes with 99 % training and validation accuracy. Model 2, with seven classes, achieves 99 % training and 98 % validation accuracy using ReLU and softmax activation functions. Integration of the IPGRI/IITA 1998 classification method enhances tomato categorization. Efficient tomato image screening optimizes resource use. This study successfully determines Padma tomato post-harvest dates based on maturity stages, a significant contribution to tomato quality assessment under market conditions.Publication Open Access Vertically constructed wetlands for greywater reuse: Performance analysis of plants(Elsevier, 2023-10-02) Siriwardhana, K.D; Miguntanna, N; Jayaneththi, D. I.; Kantamaneni, K; Rathnayake, UVertical Flow constructed wetlands (VFCWs) are environmentally feasible engineered systems that mimic the functions of natural wetlands. They are alternative engineering systems that are economical, and simple in structure with reduced land area compared to Horizontal Flow Constructed Wetlands (HFCW). Thus provides a sustainable solution for greywater treatment to a considerable extent. However, VFCWs feasibility and plant performance were not tested in the context of Sri Lanka for the greywater treatment. Therefore, the purpose of this study is to evaluate the potential of household greywater treatment using a pilot-scale VFCW and examine the performance characteristics of different types of plants. Three types of plants, the Canna plant (Canna indica), Ferns plant (Matteuccia struthiopteris), and Cattail plant (Typha latifolia) were used as emergent plants and a retention tank was constructed to retain solid particles in the greywater as primary treatment. The experiments were carried out for two months using a Completely Randomized Design (CRD) for three replicates. The quality of the influent and effluent was tested fortnight for a number of water quality parameters. Results revealed that the removal efficiency of contaminants was increased. Cattail plants showed higher removal efficiency for dissolved oxygen (DO), chemical oxygen demand (COD), nitrates (NO3 1-), turbidity, and electrical conductivity. In addition, Canna plants had higher efficiencies for the removal of total dissolved solids (TDS) and phosphates (PO4 3-). Furthermore, Ferns plants presented higher efficiency only for removing sulphate (SO4 3-). Conclusively, Cattail plants presented the overall best performance in treating greywater. This can be attributed to the ability of the Cattail’s dense fibrous root system to absorb more contaminants from greywater. This research also discussed the importance of microplastic analysis in greywater treatment which is a vital part of the current day research. The results of this study will be helpful to the further advanced research. Furthermore, this methodology can be implemented to other similar plants across the globe irrespective of geographical area.Publication Open Access Risk Evaluation of Cost Overruns (COs) in Public Sector Construction Projects: A Fuzzy Synthetic Evaluation(MDPI, 2023-04-22) Chadee, A.A; Martin, H.H; Gallage, S; Banerjee, K.S; Roopan, R; Rathnayake, U; Ray, IIn the Small Island Developing States (SIDS), public sector infrastructure projects (PSIPs) fail to both meet targeted performance metrics and deliver on the intended benefits to society. In terms of the cost performance metric, cost overruns (COs) beyond the initial contract value are more of a norm than a unique occurrence. Therefore, to ensure economic sustainability for SIDS, and value for money on PSIPs, there is a need to investigate and evaluate the risk impacts on COs. The purpose of this research was to identify and evaluate the perceived cost overrun risk factors that are within the primary project stakeholders’ sphere of control, and to reduce the ongoing ambiguities that exist in the prioritization of these risks. This was achieved by extracting critical risk factors from selected comparative studies in developing countries to formulate a closed-ended questionnaire to be administered to construction professionals in Trinidad and Tobago. Thereafter, the process of fuzzy synthetic evaluation (FSE) was used to develop a risk model based on three tiers of risks: 11 critical risk factors, 3 critical risk groupings (CRGs) and an overall risk level (ORL). The results showed that the two highest-ranked critical risks were project funding problems and variations by client. The leading critical risk grouping was client-related risk (5.370), followed by professionalrelated risk (4.815) and physical risk (4.870). The ORL was 5.068. Based on the FSE’s linguistic scaling, the CRGs and the ORL are perceived to be high risks in PSIPs. This research adds to the CO body of knowledge in primarily three ways. Firstly, the study extends the comparative assessment previously undertaken in scholarship into the context of SIDS to build on the generalizability of this context-specific phenomenon. Secondly, the FSE evaluation undertaken provides a practical tool to be promoted for use in SIDS’ construction industry among practitioners to focus and prioritize the critical risks in the planning phases and improve on contemporary risk practices in the execution phases of projects. Finally, this quantitative model approach is recommended to supplement the traditional qualitative risk management practices adopted in SIDS, thus contributing towards the overall improved economic sustainability and viability of PSIPs.