Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
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Assessing & mitigating urban heat island in Colombo to achieve SDG 11 and SDG 13
(Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka, 2025) Bandara, RMRK; De Silva, MRR; Gunapravin, T; Dissanayake, DMDOK
Rapid urbanization in Colombo, Sri Lanka, has intensified the Urban Heat Island (UHI) effect, raising local temperatures, increasing energy use, and stressing urban infrastructure. This study assesses spatial and temporal UHI dynamics in Colombo from 2001 to 2024 using multi-temporal satellite data, remote sensing, and GIS-based analysis. Land Surface Temperature (LST), land cover, and ecological indices (NDVI, NDBI, NBUI, UTFVI) were analyzed to identify trends and hotspots. Results show a rise in built-up areas, a decline in vegetation, and increased LST, with 39% of the city experiencing extreme heat stress, especially in dense urban cores. The study recommends increasing urban green cover, using high-albedo materials, and implementing climate-sensitive planning to align with SDGs 11 and 13. These findings provide a replicable framework for UHI mitigation and support sustainable, climate-resilient urban development in Colombo.
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Performance evaluation of machine learning pipelines for pore pressure prediction
(Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka, 2025) Naweed, MNM; Suheerman, S; Dilkushan, SMDKR; Thiruchittampalam, S; Wickrama, MADMG
Accurate pore pressure prediction is critical for safe drilling operations. Conventional prediction methods, which rely on simplified empirical assumptions, often fail to capture the multivariate and non-linear relationships present in complex geological settings. Machine learning (ML) provides a data-driven approach that can model these complexities directly from well log data without relying on predefined physical equations. However, the practical application of ML is often inconsistent due to a lack of systematic understanding of how data preprocessing choices impact final model performance. This study aims to resolve this uncertainty by identifying the optimal combination of preprocessing strategy and ML algorithm for this task. A comparative analysis was conducted across four scenarios: raw data, outlier-capped data, feature-selected data, and combined preprocessing (outlier capping and feature selection) using six ML algorithms to systematically evaluate the effects of outlier capping and the removal of multicollinear features. The findings identify a tuned XGBoost model as the top performer (R² = 0.9789), achieving this optimal result on the raw, unprocessed dataset. This key finding, when analyzed in the context of the other experimental scenarios, demonstrates that removing linearly correlated features can be detrimental to advanced models and that the necessity of outlier treatment is algorithm dependent. This study concludes that while the data preparation strategy is universal, it is closely tied to algorithm choice, offering a context-aware framework to enhance model reliability and support interpretability in future research.
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A Web-based system for rock classification leveraging RGB and hyperspectral imaging
(Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka, 2025) Takizawa, K; Okada, N; Muacanhia, O; Owada, N; Mathews, GP; Ohtomo, Y; Kawamura, Y
This study introduces a novel scientific approach that integrates hyperspectral imaging and artificial intelligence to enhance rock type classification. A core contribution of this work is the development of an original segmentation algorithm capable of identifying subtle mineralogical variations in core samples. This algorithm enables the automated classification of diverse rock types with high accuracy and interpretability. The original algorithms were implemented into a user-friendly application that streamlines the image analysis process, thereby reducing dependency on expert geological interpretation. This enables rapid and reliable evaluation, even by non-specialist users. To validate the application's performance, a case study was conducted. Comparing the segmentation-based rock type classification with conventional visual inspection and Python-based scripts, confirming comparable accuracy. The findings demonstrate that the proposed system offers both scientific novelty and practical value, contributing to the advancement of non-contact, efficient, and accurate geotechnical analysis in both research and field environments.
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Value addition options for Sri Lankan low-grade iron ores: the critical role of goethite-to-magnetite conversion
(Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka, 2025) Peiris, NG; Kishaan, J; Wickramarachchi, ND; Dissanayake, DMV; Ratnayake, NP; Abeysinghe, AMKB; Premasiri, HMR; Rohitha, LPS
Sri Lanka possesses approximately 2.2 million tonnes of iron ore deposits, predominantly as hydrated iron oxides in regions like Dela and Pelpitigoda. This study investigates value addition pathways for these low-grade ores through strategic beneficiation. Four iron ore samples were characterized using XRD and ICP-MS, revealing goethite as the dominant phase in hydrated deposits with iron contents of 58.52% (Dela) and 31.23% (Pelpitigoda). Roasting of goethite samples at 450°C for 4 hours successfully transformed them into magnetite with 85-90% and 70-75% phase conversion efficiency, respectively. The converted magnetite showed iron enrichment to 64.83% (Dela) and 31.76% (Pelpitigoda) through structural water removal and exhibited strong ferromagnetic properties essential for downstream processing. As iron ore deposits in the country aren’t utilized in the steel industry due to low grades or low resource content, the converted magnetite enables multiple value-added opportunities, including ferrosilicon production for import substitution, iron oxide pigment synthesis, cement manufacturing applications, ceramic tile production, and emerging nanotechnology applications. This pre-processing step transforms underutilized hydrated iron ores into versatile industrial feedstock. The study demonstrates that goethite-to-magnetite conversion is essential for unlocking the economic potential of Sri Lanka's low-grade iron ore resources, supporting sustainable industrial development.
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Recovery performance of sodium cocoate (NaCo) as a collector in froth flotation
(Department of Earth Resources Engineering, University of Moratuwa, Sri Lanka, 2025) Kumarasinghe, RDMM; Sajidh, JM; Madhushan, AGM; Rohitha, LPS; Rathnayake, NP; Abeysinghe, AMKB; Premasiri, HMR; Vijitha, AVP; De Silva, KBA
Sri Lanka consists of high-grade quartz deposits with a purity level of 99.5%. However, a huge amount of low-grade quartz is abandoned in mine sites without any processing, causing resource underutilization and environmental damage. Froth flotation has proven its efficiency for processing low-grade quartz in previous studies. Conventional collectors with more than 90% of high recovery used in those studies show environmental hazards and low biodegradability. These collectors are usually fatty acids or fatty amines. In this study, the quartz flotation potential of Sodium Cocoate, (a naturally derived fatty acid salt), as a flotation collector was evaluated under controlled conditions: pH 11, temperature of 28-30 °C, particle size range of 90-150 microns, slurry density of 1007 kg/m³, impeller speed 280 rpm and bubble rate 0.004 m3/s. The highest average recovery obtained was 28.19±1.28% out of 100 g of the initial quartz sample, which was comparatively low, suggesting that NaCo is a weak collector.