Research Papers - Department of Civil Engineering

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Author: search.filters.author.Rathnayake, UStart date: 2025

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    PublicationOpen 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, S
    Accurate 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. Copyright
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    PublicationEmbargo
    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, U
    Estimating 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.
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    PublicationOpen Access
    Reviving Urban Landscapes: Harnessing Pervious Concrete Pavements with Recycled Materials for Sustainable Stormwater Management
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025-10-29) Gunathilake, T.A; Siriwardhana,K.D; Miguntanna,N; Miguntanna, Nadeeka; Rathnayake, U; Muttil, N
    This study examines the effectiveness of pervious concrete pavements as a sustainable and cost-effective stormwater management technique, particularly by incorporating locally sourced recycled materials into their design. It evaluates the stormwater treatment potential of three pervious concrete pavement types incorporating recycled plastic, glass, and crushed concrete aggregates, with six design variations produced using 25% and 50% replacements of coarse aggregates from these materials. The key properties of pervious concrete, namely compressive strength, porosity, unit weight, and infiltration, and key water quality indicators, namely pH, electrical conductivity (EC), total suspended solids (TSS), colour, turbidity, chemical oxygen demand (COD), nitrate (NO3−), and orthophosphate (PO43−), were analysed. Results indicated an overall improvement in the quality of the stormwater runoff passed through all pervious concrete pavements irrespective of composition. Notable reductions in turbidity, TSS, colour, COD, PO43−, and NO3− underscored the effectiveness of pervious concrete containing waste materials in the treatment of stormwater runoff. Pervious concrete pavements with 25% recycled concrete exhibited optimal performance in reducing TSS, COD, and PO43− levels, while the 50% recycled concrete variant excelled in diminishing turbidity. However, the study found that the use of recycled materials in pervious concrete pavements affects properties like compressive strength and infiltration rate differently. While incorporating 25% and 50% recycled concrete aggregates did not significantly reduce compressive strength, the effectiveness of stormwater treatment varied based on the mix design and type of recycled material used. Thus, this study highlights the potential of utilizing recycled waste materials in pervious concrete pavements for sustainable stormwater management.