Research Publications
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Item Open Access Development of a MEMS-based Earthquake Dataset using the Raspberry Shake Network in New Zealand(Sri Lanka Institute of Information Technology, 2026-05-21) Samiha, T.Z; Ravishan, DTraditional seismic monitoring is often limited by the high cost of instrumentation and logistical barriers, hindering the expansion of earthquake monitoring networks especially in under-resourced regions. Low-cost MEMS-based sensors offer a scalable alternative, but require specialized datasets to train machine learning models adapted to their unique noise characteristics and sensitivity profiles. To address this, we systematically collected waveforms from approximately 4,000 earthquakes (magnitude 2.7 to the highest recorded) recorded across 89 Raspberry Shake stations in New Zealand from 2020–2025. Events were matched to nearby stations based on epicentral distance criteria (100 km for M 2.7–5.5, 150 km for M>5.5). A staged filtering pipeline using PhaseNet, EQTransformer, and GPD models, cross-validated with theoretical TauP arrivals, was applied to ensure phase pick quality across three confidence tiers. The final curated dataset comprises 918 high-confidence waveforms with validated P and S wave arrivals, alongside approximately 16,035 total waveform records spanning all quality tiers. This dataset addresses the critical scarcity of labeled training data for low-cost seismic instrumentation, enabling the development of phase pickers specifically calibrated for MEMS sensors.Item Open Access Large Language Model Pipelines for Crisis Intelligence: Automated Help Request Classifier(Sri Lanka Institute of Information Technology, 2026-05-21) De Silva, L.D.R.E.This research examines the development and deployment of a Crisis Intelligence Pipeline, engineered to address the catastrophic information surge following Cyclonic Storm Ditwah in Sri Lanka [1]. The system integrates advanced prompt engineering methodologies, specifically few-shot learning, Chain-of-Thought [2], and Tree-of-Thoughts [3] to transform unstructured humanitarian data into actionable logistics strategies. Through rigorous stability testing under varying temperature gradients, the study identifies a "Safe Mode" for deterministic triage and a "Chaos Mode" that highlights the risks of model hallucination in high-stakes environments. Furthermore, the pipeline utilizes Pydantic-based schema validation [4] and token economics [5] to ensure scalability and cost-efficiency. Results indicate that structured reasoning architectures significantly enhance the precision of resource allocation and signal-to-noise discrimination, providing a vital technological framework.Item Open Access Smart-Camp Box: An Integrated IoT and Artificial Intelligence Framework for Safety, Communication, Learning Assistance, and Resource Optimization in Disaster Relief Camps(Sri Lanka Institute of Information Technology, 2026-05-21) Kashmira S S; Hansana G P; Deerasinghe A D S N S; Jayakody, A; Lokuliyana, SNatural disasters continue to inflict devastating consequences on communities across South Asia, with Sri Lanka ranking among the most frequently affected nations in the region. When disasters strike, temporary relief camps serve as critical shelters for displaced populations; however, existing systems fail to address three persistent operational challenges simultaneously: camp-level flood and landslide prediction, psychological and attentional readiness assessment for displaced children, and resilient communication under degraded network conditions. This paper presents the Smart Camp Box, an integrated, portable IoT-based framework that addresses all three dimensions through tightly coupled sub-systems. The first sub-system provides location-aware environmental risk monitoring using GPS/GNSS, IoT sensors, DEM integration, and a machine learning prediction model. The second introduces the Psycho-Attentional Gated Educational System (PAGES), an offline-first application for assessing and supporting displaced children's cognitive readiness. The third proposes a Semantic-Aware Adaptive Message Prioritization (SAAMP) framework for reliable MQTT communication under constrained network environments. Currently in active development, the Smart Camp Box represents a paradigm shift toward proacItem Open Access Space-Ground Hybrid AI System for Flood and Landslide Monitoring Using Multi-Level Data Fusion(Sri Lanka Institute of Information Technology, 2026-05-21) Withanachchi, W U; Ranasinghe, M T EFloods and landslides represent critical hydrometeorological hazards whose frequency and intensity are amplified by extreme precipitation, deforestation, unplanned urban expansion, and inadequate land-use management. Conventional Early Warning Systems (EWS) are typically constrained by reliance on single-source data streams such as satellite observations, IoT sensor networks, or community reports, resulting in fragmented situational awareness, elevated predictive uncertainty, and limited lead time. To address these limitations, this study proposes a Space-Ground Hybrid AI framework that integrates heterogeneous data sources through a hierarchical multi-level fusion architecture. The proposed system combines convolutional neural networks (CNNs) for spatial feature extraction, long short-term memory (LSTM) networks for temporal sequence modeling, and gradient-boosted ensembles for hazard-specific prediction, enabling comprehensive modeling of both large-scale environmental patterns and localized dynamic processes. These outputs are systematically fused at feature, decision, and risk levels to derive a Unified Hazard Risk Index (UHRI), which provides an interpretable and operationally actionable representation of multi-hazard risk. Experimental evaluation using multi-spectral satellite imagery, high-frequency IoT sensor measurements, and simulated community intelligence demonstrates that the proposed hybrid model achieves superior performance, with an accuracy of 91%, an F1-score of 0.89, and a substantial reduction in false alarm rates compared to singlesource baselines. Furthermore, the framework improves warning lead time and maintains robust performance under partial data degradation scenarios, highlighting its resilience and suitability for real-world deployment. Overall, the results validate the effectiveness of multi-modal data fusion and hybrid AI modeling in enhancing predictive reliability, spatial coverage, and temporal responsiveness for disaster early warning systems, providing a scalable foundation for next-generation risk monitoring and management solutions.Item Open Access Beyond Internet Dependency: LoRa for Post- Earthquake Structural Monitoring(Sri Lanka Institute of Information Technology, 2026-05-21) Rallapudi, M.O; Vautier, A; Ngan, B; Perera, P; Prasanna, RAcquiring structural response data during and after earthquakes is essential for assessing threats to critical infrastructure; however, conventional transmission systems rely on Internet connectivity that is often disrupted during major seismic events. This study evaluates Long Range (LoRa) radio as a low-power, infrastructure-independent alternative for transmitting building instrumentation data when conventional networks fail. Field experiments were conducted across three New Zealand cities, testing performance under varying radio configurations, elevations, and urban topographies. Results demonstrate reliable urban communication ranges of up to 1.6 km under optimal settings. Elevation was the dominant deployment factor, with packet delivery ratios (PDR) improving from 0–40% at ground level to near-100% at 500 m radial distance when transmitters were positioned on upper floors. Building-to-building links achieved 75–100% PDR with received signal strength values between −55 and −110 dBm, while terrain features such as hills and dense vegetation caused significant attenuation and occasional link failure at shorter distances. These findings confirm the feasibility of LoRa for post-earthquake structural monitoring and provide quantitative guidance for elevation-aware, terrain-conscious deployment in urban environments.Item Open Access Investigating the Locational Accuracy of Crowdsourced Data in the Context of NSW Wildfires Using NLP and Geocoding Techniques(Sri Lanka Institute of Information Technology, 2026-05-21) Vishwajith, D; Koswatte, SWildfires are major natural disasters, and timely monitoring is essential for effective response. Recent progress in Crowdsourced Data (CSD) and Remote Sensing (RS) has improved wildfire monitoring, however their combined use for spatial validation is still limited. This study examined the locational accuracy of Twitter-based wildfire reports during the 2019-2020 New South Wales (NSW) - Australia wildfire season by comparing them with satellite-derived hotspots. Wildfirerelated tweets were processed using Natural Language Processing (NLP), Named Entity Recognition (NER), and geocoding through the Nominatim API, while hotspot data was obtained from the VIIRS sensor via Digital Earth Australia. Advanced data cleaning and keyword filtering methods were applied to extract relevant geolocated tweets from a large raw dataset, and geocoded locations were rigorously clipped to the NSW boundary to improve spatial relevance. A 5 km grid overlay and statistical tests, including chi-square, binomial tests and Poisson-Based tests, were used to measure the spatial relationship between the two datasets. The results showed a strong positive association, indicating that Twitter reports often align with satellite-validated hotspots, particularly in densely reported areas. Although CSD can be uneven in coverage and limited by geocoding accuracy, a combined approach using NLP, spatial filtering, and statistical validation improves its reliability. The study highlighted the value of integrating social media data with RS, proposed a reproducible framework for spatial accuracy assessment, and provided practical guidance relevant to real-time wildfire monitoring and disaster management systems.Item Open Access Structural Gaps in Disaster Technology Systems: A Longitudinal Review of Post-Event Assessments in Aotearoa New Zealand(Sri Lanka Institute of Information Technology, 2026-05-21) Tan, M.L; Adikari, K.E; Prasanna, ROver the past decade, Aotearoa New Zealand has experienced multiple large-scale disasters that have exposed recurring challenges in information management, communication, coordination, and technology integration within the emergency management system. This paper analyses eleven major disaster-response review reports published between 2012 and 2024 to examine how technology-related issues are represented in official assessments and to identify structural gaps. Using a mixed-method approach combining keyword frequency analysis and qualitative thematic analysis, the study finds that information and communication dominate review discourse, while explicit references to technology, situational awareness, and common operating picture appear less frequently and inconsistently. Longitudinal synthesis reveals persistent reform themes, including calls for nationally integrated platforms, interoperable systems, professionalised capability, and shared intelligence frameworks. Yet implementation details regarding architectural ownership, standards, lifecycle management, and funding remain limited. The paper advances a structured research agenda organised around four domains: data governance and architecture, decision-support design, ICT resilience and interoperability, and organisational technology governance.Item Open Access Insights from Cyclone Ditwah: Recommendations for Addressing Linguistic Barriers in Sri Lanka’s Disaster Early Warning Systems through Language Technology(Sri Lanka Institute of Information Technology, 2026-05-21) Chandrasekara, SDisaster early warning systems are critical for reducing loss of life and ensuring timely response during emergencies. However, the effectiveness of such systems depends not only on technological capacity but also on the inclusivity of communication. In linguistically diverse contexts, language barriers can significantly limit access to life saving information. This paper presents insights on the role of technology in enabling multilingual disaster early warning and communication systems in Sri Lanka. Drawing on observations from recent disaster events, specifically the catastrophic Cyclone Ditwah in 2025, and a review of existing policy and operational practices, the paper highlights gaps in language consideration within current disaster management frameworks. By analyzing local gaps, such as the incomplete localization of platforms and evaluating global models like Japan’s J-Alert and India’s SACHET, this study identifies practical, technology driven pathways for inclusion. The paper proposes the integration of AI driven translation, multilingual cell broadcasts, and text-to-speech audio alerts as core infrastructure rather than secondary features. By embedding multilingualism into disaster communication from the planning stage, authorities can ensure that urgency is communicated effectively across all communities, ultimately saving lives through clarity and trust.Item Open Access Technology-Enabled Flood Disaster Management: Lessons from Malaysia and a Context-Aware Adaptation Framework for Sri Lanka(Sri Lanka Institute of Information Technology, 2026-05-21) Dabare, C; Herath, H; Wijesinghe, K.IFlooding remains one of the most recurrent and socio-economically damaging natural hazards in South and Southeast Asia. Sri Lanka experiences frequent flood events, particularly in low-lying urban regions such as Colombo, where flood risk is intensified by wetland encroachment, drainage limitations, and fragmented institutional responsibilities. Malaysia, which faces similar monsoonal flood patterns, has developed comparatively mature flood disaster management practices integrating early warning systems, centralized coordination, and technologyenabled decision support. This paper presents a practitioneroriented and technology-focused comparative analysis of flood disaster management approaches in Malaysia and Sri Lanka. Governance structures, early warning mechanisms, data integration, infrastructure maintenance, and public communication strategies are examined. Based on documented practices and limitations, the study extracts transferable lessons and proposes a multi-layered, technology-enabled adaptation framework tailored to Sri Lanka. The framework emphasizes decision support systems, real-time data integration, and community-aware communication mechanisms to enhance preparedness and response capabilities.Publication Open Access Construction Dynamics And Digitalization(Faculty of Engineering, 2025-09-09) Premachandra, P.NThe construction industry is at the edge of a decisive transformation, moving away from fragmented, paperbased practices toward an era defined by intelligent digitalization. At the center of this shift is the Digital Twin- a living, data rich model that synchronizes the physical and virtual realms of construction. By Integrating Building Information modeling (BIM), Internet of Things (IoT) sensors, artificial intelligence, and cloud computing, Digital Twins enable Projects to move from reactive monitoring to proactive, predictive control. This paper examines their influence on Construction dynamics, demonstrating how 4D scheduling with Primavera P6 and intuitive dashboards guided by PMBOK-7 principles elevate visibility, collaboration, and decision making. A case study of the Maldives International Airport new terminal illustrates tangible outcomes: real-time clash detection, optimized sequencing, energy efficient design, and measurable carbon emission reductions. Beyond showcasing benefits, the study outlines a pragmatic roadmap for Sri Lanka, stressing the importance of regulatory reform, academia-industry partnerships, and pilot implementations. The findings suggest that Digital Twins are not distant aspirations but present-day necessities for sustainable, data driven construction.