Faculty of Engineering
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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 Embargo 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 Anthocyanin (ATH)-incorporating polyvinylpyrrolidone-ethyl cellulose-(2-hydroxypropyl)-β-cyclodextrin (PVP–EC–BCD) nanofiber-based pH sensor for ocular pH detection during accidental chemical spills(Royal Society of Chemistry, 2026-02-03) Sandaruwan, B; Liyanage, R; Costha, P; Dassanayake, R.S; Wijesinghe, R.E; Herath H.M.L.P.B; Nalin de S.K.M; de Silva, R.M; Rajapaksha, S.M; Wijenayake, UThe existing ocular pH detection methods encounter numerous limitations, including low accuracy, poor sensitivity across a wide pH range, and patient discomfort, highlighting the need for innovative approaches. A novel biosensor for ocular pH detection has been developed to assess ocular health and chemical injuries in clinical settings. This study uses the pH-sensitive properties of anthocyanins (ATHs), natural pigments extracted from butterfly pea flowers, to develop a novel pH-responsive nanofiber mat. ATHs are integrated into a polymer blend containing polyvinylpyrrolidone (PVP), ethyl cellulose (EC), and (2-hydroxypropyl)-β-cyclodextrin (BCD) to fabricate electrospun nanofibers. The acquired characterization, employing scanning electron microscopy (SEM), Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA), confirmed the successful fabrication of the ATH-infused nanofibers with a mean diameter ranging from 121 to 396 nm. Four formulations were tested: PVP:EC:BCD:ATH (18 ppm), PVP:EC:BCD:ATH (25 ppm), PVP:EC:BCD:ATH (35 ppm), and PVP:EC:BCD:ATH (50 ppm). Among them, the 50 ppm ATH-incorporating nanofiber mat exhibited the best performance in terms of color clarity, response time, and pH sensitivity. The fabricated 50 ppm ATH incorporating nanofiber mat demonstrated a rapid pH response time of less than 5 seconds (s) while exhibiting a color variation from pink to blue to green across the pH range of 1 to 12, providing a rapid and accurate method for visual pH detection. Based on the color performance of the 50 ppm ATH-incorporating system, a standardized color reference chart was developed to serve as a practical and visual guide for estimating pH levels in clinical applications. Zebrafish toxicity assays were conducted further to validate the safety and biocompatibility of the developed sensor, revealing no significant toxic effects across the range of ATH concentrations.Publication Open Access Self-starting characteristics and dynamic response of a free-spinning cross-flow air turbine for oscillating water columns under irregular wave conditions(Elsevier Ltd, 2026-02-24) Baddegamage B.H.B.P.D; Bae, S.J; Gunawardane S.D.G.S.P.; Lee, Y.H; Kim, K; Yoon, MThe cross-flow air turbine (CFAT) has been proposed as a self-rectifying device for oscillating water column (OWC) wave energy converters as an alternative to conventional Wells and impulse turbines. While previous studies have primarily focused on steady or regular flow conditions, the self-starting behavior and transient response of a free-spinning CFAT under irregular, bidirectional inflow representative of realistic sea states have not yet been investigated. This study presents a fully transient computational fluid dynamics analysis of a free-spinning CFAT operating under irregular airflow conditions derived from the JONSWAP spectrum. The simulations were performed under no-load conditions to isolate the intrinsic aerodynamic torque generation and evaluate self-starting capability. The effects of significant wave height and spectral peak period on turbine startup and unsteady aerodynamic response were systematically examined in both the time and frequency domains. The CFAT consistently initiates rotation without external assistance and reaches quasi-steady operation within 25–30 oscillation cycles. For significant wave heights ranging from 0.0375 m to 0.05 m, the mean instantaneous efficiency varies between 0.24 and 0.52, while efficiencies between 0.30 and 0.59 are obtained for spectral peak periods from 1.50 s to 1.88 s. Furthermore, wave-grouping effects play a decisive role in accelerating the turbine toward its equilibrium speed. Torque and pressure fluctuations closely follow the inflow velocity profile, with hysteresis-like behavior observed during flow reversals. These findings confirm the CFAT's suitability for practical OWC applications, demonstrating robust self-starting and stable performance under irregular conditions.Publication Embargo Evaluating and prioritizing delay factors in naval ship maintenance using the analytic hierarchy process: a Sri Lanka navy shipyard case study(Taylor and Francis Ltd., 2026-02-18) Fernando, W. J; Silva, N; Perera, CTimely maintenance of ships and craft is critical for ensuring operational readiness, safety, and economic sustainability in the maritime sector. However, scheduled docking delays remain a persistent challenge globally, incurring significant financial losses and reducing fleet availability. This study presents a systematic, quantitative approach to identify and prioritize 22 critical factors causing delays in scheduled docking. Using the Analytic Hierarchy Process (AHP), the study evaluates the relative importance of these factors to support informed decision-making. A case study of the Sri Lanka Navy (SLN) demonstrates the application of the proposed framework, revealing that 97% of docking delays occur before vessels enter the dock, with 31.8% of these delays attributable to deficiencies in the procurement of materials and spare parts. While the findings are based on a single case study of the SLN shipyard, they offer context-specific insights into the unique challenges faced by naval maintenance operations in developing regions.Publication Embargo Evaluating and prioritizing quality culture elements in the tire manufacturing industry: A case-based DEMATEL approach(Taylor and Francis Ltd., 2026-01-16) Silva, N; Bandara, S; Perera, CIn today’s competitive manufacturing environment, developing a robust quality culture that supports continuous improvement and defect prevention is critical for long-term operational excellence and customer satisfaction. This study applies the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to evaluate and prioritize the key elements shaping quality culture in a leading tire manufacturing company in Sri Lanka, offering a rare empirical application of DEMATEL in this context. Using a two-phase approach, Phase 1 involved a survey of 127 employees to identify seven critical elements, while Phase 2 analyzed expert evaluations from 16 professionals to establish causal relationships among them. The findings reveal that leadership (R + C = 14.40) and employee empowerment (R + C = 13.31) are the most influential drivers, followed by teamwork (R + C = 12.67), while focus on customer satisfaction, planning for quality, improvements and innovation, and standardized processes approach are primarily dependent elements. The built-in quality maturity framework and relative importance index were applied to assess the current implementation level of each element and identify performance gaps. The gap analysis highlights that leadership and employee empowerment are underperforming relative to expected levels, requiring immediate strategic enhancement. Managers should prioritize leadership and empowerment development to strengthen organizational quality culture and sustain competitiveness.Publication Embargo Lean maturity model for the Sri Lankan construction industry: investigation of key model assessing components(Taylor and Francis Ltd., 2026-02-27) Jayanetti J.K.D.D.T.; Perera B.A.K.S; Waidyasekara K.G.A.S.; Siriwardana, M; Ranadewa K.A.T.OImplementing lean practices in the construction industry remains challenging, particularly due to the lack of effective mechanisms to assess lean construction maturity. Despite the presence of limited literature on lean maturity models, no model has been developed specifically for the Sri Lankan construction sector. Addressing this gap, the present study takes an initial step toward developing a Lean Construction Maturity Model tailored to the Sri Lankan context by identifying the essential components required for its assessment. Adopting a pragmatic stance, the research employed the qualitative Delphi technique, involving 73 expert interviews conducted over three iterative rounds, followed by five validation interviews. Directed Content analysis was used to extract key elements for the model. The study identified three core components necessary for assessing lean construction maturity: attributes, process areas, and indicators. Specifically, eight attributes were revealed including Production Efficiency, Waste Elimination, Quality Management, People, Customer Focus, Lean Leadership, Transparency, and Lean Philosophy. These attributes are supported by 28 process areas and 140 indicators. Together, these elements form a structured, layered framework for assessing lean maturity. The study contributes original insights by considering the cultural, economic, and institutional dynamics influencing lean implementation in Sri Lanka. While the findings establish foundational components, further research is needed to develop and validate a complete maturity model. Practically, the study enables a more systematic and locally relevant approach to lean adoption, supporting improved industry performance. Socially, it promotes resource efficiency and project success, contributing to more responsible and sustainable construction practices in the Sri Lankan context.Publication Embargo Long-term recovery from the 2004 Indian ocean tsunami in two Sri Lankan east coast municipalities(Elsevier Ltd, 2026-01) Thamboo, J; Josiah, R; Saja, A; Salah, P; Rossetto, T; Dias, PSri Lanka was the second most affected country after Indonesia, in the 2004 Boxing Day Indian Ocean tsunami (IOT). A study mission was therefore carried out twenty years after the 2004 IOT to assess the recovery of the affected regions, especially in the Eastern region of Sri Lanka, focusing on two of the most affected municipalities, i.e. Kalmunai and Batticaloa. The social and infrastructure characteristics of resettlements/relocations/new settlements in the affected regions, presence of critical infrastructure, preparedness and early warning systems installed have been assessed. It was observed that similar approaches have been adopted to plan the community relocation in both of these municipalities, while the significant reemergence of residential and commercial developments in the coastal stretches of Kalmunai municipality have been noted. Exposure analyses have revealed that there are still some critical infrastructure situated in the tsunami hazard zones. It can be construed that these municipalities have recovered from the physical losses incurred, and spatial planning is in place for future developments considering the tsunami risk. Challenges and opportunities from their differing geographical contexts appear to have been judiciously handled. However, shortcomings are noted in actual implementation due to various reasons, such as limited resources, availability of funding and preference of communities to live close to their original lands. Improving the resilience of infrastructure by designing against the expected tsunami hazard and multi-hazards, regular verification of the early warning systems and evacuation procedures are emphasized to mitigate the impacts from future tsunami.Publication Embargo Receiver-Centric Waveform Design: A New Frontier in SWIPT(Institute of Electrical and Electronics Engineers Inc., 2026-01-15) Vithanage, G. S; Jayakody, D. N.K; Krikidis, IIn this work a receiver-centric waveform design technique for simultaneous wireless information and power transfer (SWIPT) is proposed, eliminating the traditional trade-off between energy harvesting (EH) efficiency and information transfer (IT) integrity. By injecting pulses into the receiver, the peak-to-average power ratio (PAPR) of the received signal is increased, using diode nonlinearity to enhance EH without affecting IT. Particle swarm optimization (PSO) is used to tune the pulse parameters to obtain the maximum harvest power under practical constraints. The Monte Carlo simulation results demonstrate superior EH performance compared to existing waveform optimization schemes. The method remains robust under common IT optimizations, such as selective mapping (SLM) and partial transmit sequence (PTS), confirming its compatibility and scalability for real-world SWIPT systems.Publication Open Access Advancing Object Detection: A Narrative Review of Evolving Techniques and Their Navigation Applications(Institute of Electrical and Electronics Engineers Inc., 2025-03-17) Tennekoon, S; Wedasingha, N; Welhenge, A; Abhayasinghe, N; Murray Am, IObject detection plays a pivotal role in advancing computer vision systems by enabling machines to perceive and interact intelligently with their environments. Despite significant advancements, comprehensive exploration of its evolution and applications in navigation remains underrepresented. This review paper examines the evolution of object detection technologies, from early methodologies to contemporary advancements, and their critical role in navigation tasks. The emphasis was on the significance of contextual learning in enhancing object detection performance by leveraging spatial and temporal information. Furthermore, the limitations of conventional approaches that rely heavily on hand-engineered features are examined. It is then demonstrated that contextual learning facilitates automated feature extraction, resulting in improved accuracy exceeding a 50% increase and adaptability in diverse applications. The review concludes by outlining future trends and opportunities for further advancements in object detection and, underscoring its transformative impact on autonomous navigation and beyond. In summary, this review contributes to a comprehensive understanding of object detection technologies by offering insights into their evolution, highlighting their applications in navigation, and providing guidance for future research in context-aware systems.