Faculty of Engineering

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Author: search.filters.author.Perera, HStart date: 2022

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Now showing 1 - 5 of 5
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    PublicationEmbargo
    Bi-directional long short-term memory based ensemble deep learning framework for non-linear steam turbine power forecasting: a biomass fuelled case study
    (Elsevier Ltd, 2026-04-10) Perera, H; Jayasekara, S; Wijesinghe, R.E; Silva, B. N; Cha, H
    In palm oil manufacturing, steam turbines powered by biomass fuel are central to energy generation. However, fluctuating load demands and temporal variations lead to inefficiencies, while limited and variable supply of biomass waste constrains boiler feed flexibility. Current index-based boiler feeding methods overlook actual load demands and waste availability, resulting in significant energy wastage. This study presents a novel ensemble deep learning model combining Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) with Attention Layers, trained on an eight-year operational dataset with structured preprocessing and feature selection, to forecast steam turbine power generation. The model captures complex non-linear temporal patterns more effectively than conventional and standalone ML models, achieving a Root Mean Square Error (RMSE) of 0.0684, Mean Absolute Error (MAE) of 0.0414, and an R-squared (R2) value of 0.9832, which outperformed eight benchmark models by approximately 25% in prediction accuracy. Additionally, the framework incorporates operational parameters such as kVA, total energy, and Fresh Fruit Bunch (FFB) production to dynamically optimise biomass feed rates, balancing energy output with resource availability. This approach minimises energy wastage, reduces grid reliance, and promotes both sustainability and profitability.
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    PublicationOpen Access
    Statistical Evaluation and Trend Analysis of ANN Based Satellite Products (PERSIANN) for the Kelani River Basin, Sri Lanka
    (Hindawi, 2022-08-31) Perera, H; Gunathilake, M. B; Panditharathne, R; Al-mahbashi, N; Rathnayake, U
    Satellite-based precipitation products, (SbPPs) have piqued the interest of a number of researchers as a reliable replacement for observed rainfall data which often have limited time spans and missing days. The SbPPs possess certain uncertainties, thus, they cannot be directly used without comparing against observed rainfall data prior to use. The Kelani river basin is Sri Lanka’s fourth longest river and the main source of water for almost 5 million people. Therefore, this research study aims to identify the potential of using SbPPs as a different method to measure rain besides using a rain gauge. Furthermore, the aim of the work is to examine the trends in precipitation products in the Kelani river basin. Three SbPPs, precipitation estimation using remotely sensed information using artificial neural networks (PERSIANN), PERSIANN-cloud classification system (CCS), and PERSIANN-climate data record (CDR) and ground observed rain gauge daily rainfall data at nine locations were used for the analysis. Four continuous evaluation indices, namely, root mean square error (RMSE), (percent bias) PBias, correlation coefficient (CC), and Nash‒Sutcliffe efficiency (NSE) were used to determine the accuracy by comparing against observed rainfall data. Four categorical indices including probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and proportional constant (PC) were used to evaluate the rainfall detection capability of SbPPs. Mann‒Kendall test and Sen’s slope estimator were used to identifying whether a trend was present while the magnitudes of these were calculated by Sen’s slope. PERSIANN-CDR performed well by showing better performance in both POD and CSI. When compared to observed rainfall data, the PERSIANN product had the lowest RMSE value, while all products indicated underestimations. The CC and NSE of all three products with observed rainfall data were also low. Mixed results were obtained for the trend analysis as well. The overall results showed that all three products are not a better choice for the chosen study area.
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    PublicationOpen Access
    Appraisal of Satellite Rainfall Products for Malwathu, Deduru, and Kalu River Basins, Sri Lanka
    (MDPI, 2022-10-20) Perera, H; Senaratne, N; Gunathilake, M. B; Mutill, N; Rathnayake, U
    Satellite Rainfall Products (SRPs) are now in widespread use around the world as a better alternative for scarce observed rain gauge data. Upon proper analysis of the SRPs and observed rainfall data, SRP data can be used in many hydrological applications. This evaluation is very much necessary since, it had been found that their performances vary with different areas of interest. This research looks at the three prominent river basins; Malwathu, Deduru, and Kalu of Sri Lanka and evaluates six selected SRPs, namely, IMERG, TRMM 3B42, TRMM 3B42-RT, PERSIANN, PERSIANNCCS, PERSIANN-CDR against 15+ years of observed rainfall data with the use of several indices. Four Continuous Evaluation Indices (CEI) such as Root Mean Square Error (RMSE), Percentage Bias (PBIAS), Pearson’s Correlation Coefficient (r), and Nash Sutcliffe Efficiency (NSE) were used to evaluate the accuracy of SRPs and four Categorical Indices (CI) namely, Probability of Detection (POD), Critical Success Index (CSI), False Alarm Ratio (FAR) and Proportion Correct (PC) was used to evaluate the detection and prediction accuracy of the SRPs. Then, the Mann–Kendall Test (MK test) was used to identify trends in the datasets and Theil’s and Sens Slope Estimator to quantify the trends observed. The study of categorical indicators yielded varying findings, with TRMM-3B42 performing well in the dry zone and IMERG doing well in the wet zone and intermediate zone of Sri Lanka. Regarding the CIs in the three basins, overall, IMERG was the most reliable. In general, all three basins had similar POD and PC findings. The SRPs, however, underperformed in the dry zone in terms of CSI and FAR. Similar findings were found in the CEI analysis, as IMERG gave top performance across the board for all four CEIs in the three basins. The three basins’ overall weakest performer was PERSIANN-CCS. The trend analysis revealed that there were very few significant trends in the observed data. Even when significant trends were apparent, the SRP projections seldom captured them. TRMM-3B42 RT had the best trend prediction performance. However, Sen’s slope analysis revealed that while the sense of the trend was properly anticipated, the amplitude of the prediction significantly differed from that of the observed data.
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    PublicationOpen Access
    Satellite Rainfall Products for analysis of Rainfall trends for Mahaweli River Basin
    (SLIIT, 2022-02-11) Perera, H; Gunathilake, M. B; Rathnayakea, U
    The presence of accurate and spatiotemporal data is of utmost importance in hydrological studies for river basins. However, limited ground-measured rainfall data restrict the accuracy of these analyses. Data scarcities can often be seen not only in many developing countries but also in the developed world. Therefore, much attention is given to alternative techniques to accomplish the data requirement. Precipitation data extraction from satellite precipitation products is one of the frequently used techniques in the absence of ground-measured rainfall data. The Mahaweli River Basin (MRB) is the largest river basin in Sri Lanka and it covers 1/6th of the total land area of the country. Mahaweli River is the heart of the country and the water of it is being used for many activities, including hydropower development, water supply, irrigation, etc. Therefore, analyzing rainfall trends of MRB is interesting and worthwhile for many stakeholders of the river basin. Therefore, this research investigates the suitability of Satellite Rainfall Products (SRP’s) as an alternative for Rain Gauge measured data in the MRB by performing trend analysis between the two datasets. Six precipitation products, namely PERSIANN, PERSIANNCCS, PERSIANN-CDR, GPM IMERG V06, TRMM-3B42 V7, TRMM-3B42RT V7 were extracted for 10-35 years for 14 locations of the MRB spatially distributed in the three climatic zones of the catchment. Non-parametric tests, including the Mann-Kendall test and Sen’s slope estimator tests, were used to detect the possible rainfall trends in precipitation products. Significant increasing trends were observed for both ground-measured and SRP’s in the annual scale while mixed results were observed in monthly and seasonal scales. The trends from ground-measured rainfall and SRP’s were compared and the suitability of SRP’s as an alternative technique was stated.
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    PublicationOpen Access
    Evaluation of Satellite Rainfall Products over the Mahaweli River Basin in Sri Lanka
    (Hindawi, 2022-04) Perera, H; Fernando, S; Gunathilake, M. B; Sirisena, J; Rathnayake, U
    e availability of accurate spatiotemporal rainfall data is of utmost importance for reliable predictions from hydroclimatological studies. Challenges and limitations faced due to the absence of dense rain gauge (RG) networks are seen especially in the developing countries. erefore, alternative rainfall measurements such as satellite rainfall products (SRPs) are used when RG networks are scarce or completely do not exist. Noteworthy, rainfall data retrieved from satellites also possess several uncertainties. Hence, these SRPs should essentially be validated beforehand. e Mahaweli River Basin (MRB), the largest river basin in Sri Lanka, is the heart of the country’s water resources contributing to a signi cant share of the hydropower production and agricultural sector. Given the importance of the MRB, this study explored the suitability of SRPs as an alternative for RG data for the basin. Daily rainfall data of six types of SRPs were extracted at 14 locations within the MRB. ereafter, statistical analysis was carried out using continuous and categorical evaluation indices to evaluate the accuracy of SRPs. Nonparametric tests, including the Mann-Kendall and Sen’s slope estimator tests, were used to detect the possibility of trends and the magnitude, respectively. Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) outperformed among all SRPs, while Precipitation Estimation from Remotely Sensed Information using Arti cial Neural Networks (PERSIANN) products showed dire performances. However, IMERG also demonstrated underestimations when compared to RG data. Trend analysis results showcased that the IMERG product agreed more with RG data on monthly and annual time scales while Tropical Rainfall Measurement Mission Multisatellite Precipitation Analysis–3B42 (TRMM-3B42) agreed more on the seasonal scale. Overall, IMERG turned out to be the best alternative among the SRPs analyzed for MRB. However, it was clear that these products possess signi cant errors which cannot be ignored when using them in hydrological applications. e results of the study will be valuable for many parties including river basin authorities, agriculturists, meteorologists, hydrologists, and many other stakeholders.