Conference papers

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4889

Browse

Now showing 1 - 20 of 90

Open Access
An integrated data-driven approach for Chronic Kidney Disease of Unknown Etiology (CKDu) risk profiling and prediction in Sri Lanka
(SPIE, 2025) Rajapaksha, N; Rajawasan, H; Ubeysinghe, R; Perera,S; Swarnakantha, N.H.P.R.S; Gamage, M; Nanayakkara, N; Wijayakulasooriya, J; Herath, D; Lakmali, M
Chronic kidney disease of unknown etiology is a significant public health issue in Sri Lanka, especially in rural farming communities. The exact causes remain unclear, with potential links to environmental and socio-economic factors. This research employs Biological Data and Geographic Information Systems to analyze risk factors such as water quality, agricultural practices, climatic conditions, Demographic Factors, Socio-economic Factors. This study uses data from government health records, the Centre for Research-National Hospital Kandy, and field surveys. By identifying patterns and correlations, the study aims to inform public health interventions and reduce the impact of CKDu, ultimately improving health outcomes for affected populations. This will greatly contribute to preventing the disease, reducing the risk, and identifying patients at an early stage.
Embargo
Voice from the Control Room : Government Officials' Perspectives on How Politics, Funding and Technology Shape Sri Lanka's Transport Future
(Institute of Electrical and Electronics Engineers Inc., 2025) Bandara, S; Perera, Y; Premathilaka, H; Wijethunga, J; Karunarathna, N; Dayapathirana, N
The daily struggles of Sri Lanka's public transpo rtation system affect millions of lives, yet the voices of those who run it often go unheard. This study spoke with eight senior government officials from the National Transport Commission and Ceylon Government Railways to uncover what happens behind the scenes. Through detailed interviews, three main problems weighing on their minds were identified: political interference disrupts their work, money shortages block necessary improvements, and finally, worker satisfaction has hit rock bottom. Many transport workers feel stuck with low wages and unclear career paths, which makes it hard to keep services running smoothly. However, it is not all about bad news. These officials shared smart ideas about fixing issues, from better resource management to innovative technology implementation that could help riders track their buses and trains. They believe Sri Lanka's public transport can improve with the right changes. This research study goes beyond just listing problems - we talked to people who live these challenges every day and know what needs to change. Their stories show that improving public transport is not just about new buses or trains; it is about supporting the people who keep everything moving, listening to what riders need, and Equipping transport workers with the required equipment to perform their jobs well.
Embargo
Transformative Impact of Predictive and Generative AI on Education Workforce in Developing Countries
(Institute of Electrical and Electronics Engineers Inc., 2025) Weligodapola, M; Kumarapperuma, C.U
This study investigates the transformative impact of predictive and generative artificial intelligence (AI) on the education workforce in developing countries, focusing on its challenges, opportunities, and implications. Utilizing a qualitative case study approach, we explored the AI's impacts, challenges, and opportunities on work within the education sector in ten educational institutions across three countries, through qualitative data collection methods, including semi-structured interviews, observations, informal discussions, and focus group discussions with students, lecturers, and administrative staff. The findings highlight the role of AI in revolutionizing pedagogy, enhancing student engagement, and optimizing administrative workflows. Educators reported improvements in teaching strategies driven by AI-powered adaptive learning platforms and AI-generated resources, which enable personalized and dynamic learning experiences. However, the research also revealed key challenges, such as resistance to AI adoption, concerns over data privacy, and infrastructure limitations that hinder effective integration. A cross-case analysis emphasized the evolving role of educators, highlighting shifts in teacher-student dynamics and potential increases in job satisfaction, tempered by new responsibilities. While students benefit from personalized learning paths, they also face hurdles related to digital literacy and unequal access to AI-driven resources. Ethical considerations around AI deployment emerged as critical, underscoring the need for clear guidelines and strong policies. The study concludes by offering recommendations for educational institutions, policymakers, and AI developers to collaborate in fostering an innovative, inclusive, and ethically responsible educational landscape. Future research is encouraged to expand on these findings across different educational contexts and explore the long-term effects of AI on teaching, learning, and workforce dynamics.
Embargo
The Influence of Generative AI on work-life balance among female software professionals in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Upeksha, S; Samarasinghe, D.T; Sanochana, M; Samarathunga, S.S; Rajamanthri, L; Samarakkody, T; Aluthwala, C
This study explores the role of generative artificial intelligence on work-life balance among female software professionals in Sri Lanka's software industry. This qualitative study explores the influence of Generative Artificial Intelligence (GenAI) tools on workload, productivity, and overall well-being to show how these technologies uniquely shape professional and personal lives within this demographic group. Data were gathered through semi-structured interviews with 15 female software professionals from various job roles, including software engineers, quality assurance engineers, system engineers, Development and Operations (DevOps) engineers, and project managers. Using thematic analysis, findings disclose that generative AI is mostly utilized for automation, communication and collaboration, creativity and innovation, and decision support, with ChatGPT being the most widely used tool. These tools will enable professionals to streamline the workload, increase efficiency, reduce overtime, and maintain healthy working conditions. The insights of this study yield important implications for employers and government organizations such as the Department of Labor, explicitly pointing out how generative AI can be instrumented to create a favorable work environment. Thus, by applying generative AI solutions, the key stakeholders of the Sri Lankan software industry can create work conditions crucial for the work-life balance of women to enhance organizational performance as well as the work-related well-being of female software professionals.
Embargo
Sustainability Insights: Unveiling the Impact of Business Analytics in Shaping Sustainability Practices in the Apparel Industry
(2025) Gajanayake, L; Rajapaksha, D; Rukshan, T; Pathirana, S; Thelijjagoda, S; Pathirana, G
The Sri Lankan apparels industry has a strategic importance for the national economy as the country has been one of the main exports and employers. But it has sustainability issues such as high resource consumption, increased pollution, and poor labor standards. As the consumption of sustainable and environmentally responsible clothes continues to rise around the world, such concepts as business analytics (BA) present an opportunity to tackle these issues. This study investigates the effects of BA tools and techniques in enhancing sustainability in Sri Lanka apparel industry with regards to waste reduction, efficient resource management and compliance to ethical standards for sustainable driven global business. A qualitative research design was followed and conventional interviews conducted on key informants from GOTS certified apparel factories. Data were coded and analyzed thematically using MAXQDA software, with reference to the subthemes that emerged in the study, such as waste reduction and increasing efficiency and effective decision-making. It was revealed that BA solutions such as RFID, predictive modelling and dynamic dashboards offered promising improvements to sustainability performance. Techniques like 3D sampling reduced fabric consumption during the generation of prototypes, and dashboard analytics allowed constant tracking of other forms of sustainability KPIs like power use and carbon footprint. They also increased efficiency of cross-functional coordination, integrating sustainability into functions and departments. This study demonstrates how BA enables the sustenance of development within the apparel industry, based on a strategic management of economical, ecological, and social goals. The outcomes would help industry leaders and policymakers in developing improved strategies for sustainability practice to overcome existing gaps between theory and practice and for sustainable and competitive business growth in the context of a world economy moving toward sustainability.
Open Access
How E-commerce Succeeds: The Role of Information Systems in Boosting Customer Satisfaction
(Institute of Electrical and Electronics Engineers Inc., 2025) Pathirana, S.L; Pathirana, S.J; Boyagoda, G.S.B; Thalagala, S.M.K; Wisenthige, K; Aluthwala, C
The rapid growth of e-commerce has revolutionized consumer behavior, especially among Millennials and Generation Z, who increasingly rely on online platforms for their purchases. This paper shows the impact of information systems success on individual performance outcomes in e-commerce, focusing on the DeLone and McLean information system success model and its three key dimensions: system quality, information quality, and service quality. A quantitative survey method was employed to gather data from e-commerce users in the western province of Sri Lanka, which is an economically developed region where Millennials and Generation Z are highly engaged with international and local e-commerce platforms. The study uses PLS-SEM to identify that system, information, and service quality significantly increase customer satisfaction and thereby improve individual performance in e-commerce. The study will address the significant research gap in Sri Lanka, where the rapid growth of e-commerce has not been adequately studied in terms of the impact of information and individual outcomes
Embargo
Understanding AI Chatbot Adoption in Education: The Role of Perceived Usefulness, Ease of Use, and Anthropomorphic Tendencies
(Institute of Electrical and Electronics Engineers Inc., 2025) Vidarshika, W; Dayapathirana, N; Ranasinghe, A
This paper aims to highlight the underlying factors influencing AI-based ChatGPT usage behavior, considering the role of anthropomorphic tendency. It addresses existing gaps in AI literature, which has underexplored the anthropomorphization of nonhuman agents with human features in AI-based teaching and learning. This study extends the Technological Acceptance Model (TAM) integrating anthropomorphism tendency on usage behavior of ChatGPT of undergraduates. Empirical examination with Structural Equation Modeling (SEM) revealed that perception of ease of use and usefulness positively impact on attitudes and attitudes positively impacts on AI ChatGPT usage behavior in higher education. Furthermore, the novelty brings for the study with the anthropomorphism tendency as a moderator positively moderates perception of usefulness and ease of use on AI ChatGPT usage behavior in higher education. As the main theoretical contributions of the study this study contributes for the Technological Acceptance Model (TAM) identifying perceived ease of use and usefulness towards attitude and usage behavior and bringing anthropomorphism tendency for the model as moderator as one of lack of focused area in extant literature in AI based ChatGPT. Also, this study provides valuable insights for the designers of AI based ChatGPT in embedding the humanistic feature in enhancing the usefulness and ease of use towards their attitudes and usage behavior
Embargo
Enhancing Environmental Awareness for Hard of Hearing Individuals: A Mobile Application Approach
(Springer Science and Business Media Deutschland GmbH, 2025) Dharmasiri, K.G; Rathnasooriya, C.V; Balasuriya, M.K; Yapa, L.N; De Silva, D.I; Thilakarathne, T
This research focuses on developing a mobile application to enhance environmental awareness for deaf and hard of hearing individuals. At its core is an advanced audio classification system using a convolutional neural network model optimized for recognizing environmental sounds. Extensive experimentation identified the best performing convolutional neural network architecture, trained on spectrograms to classify diverse environmental sounds accurately. The model balances accuracy and computational efficiency, making it ideal for real-time mobile deployment. The application includes a user-friendly admin interface, enabling individuals without machine learning expertise to manage and train models, ensuring adaptability to various auditory environments. Leveraging cloud technologies like Amazon Web Services for data storage, processing, and model deployment, the platform provides a scalable solution for safe interaction with surroundings. This empowers users to navigate their environments confidently, enhancing awareness of crucial auditory cues. The study demonstrates the potential of mobile technology to improve inclusivity and environmental consciousness for underserved populations through real-time, tailored sound recognition.
Embargo
The Influence of IT Infrastructure and Supply Chain Flexibility on Supply Chain Performance in the Apparel Manufacturing Sector
(Institute of Electrical and Electronics Engineers Inc., 2025) Kulasekara, D; Sandaruwan, D; Ekanayake, T; Perera, A; Wisenthige, K; Aluthwala, C
In a developing country, the apparel manufacturing sector needs to improve their performance, reduce costs, and satisfy the demands of a highly competitive global market. Thus, Supply Chain Management (SCM), particularly the influence of IT Infrastructure (ITI) and Supply Chain Flexibility (SCF) in driving Supply Chain Performance (SCP), focuses on their combine within the Sri Lankan apparel manufacturing sector. These relationships were evaluated using a quantitative research approach, with data gathered from Supply Chain (SC) professionals in apparel manufacturing companies. The study reveals that SCF mediates the relationship between ITI and SCP, indicating the importance of a flexible SC in moving ITI investments into functional performance improvements. Considering the current environment, apparel manufacturers should apply methods that relate ITI capabilities to the SC ability to respond to changing demands. Technological foundation influences SCF, enabling organizations to respond effectively to market demand and operational challenges. Consequently, SCF significantly contributes to SCP by improving flexibility, reducing costs and enhancing customer satisfaction. Some of the practical implication include the usage of advanced IT systems including prophetic analytics and enterprise resource planning tools to enhance SC responsiveness. In addition, development of collaborative relationship with suppliers and partners can enhance the effect of SCF and SCP, making Sri Lanka apparel manufacturers more competent to perform international standard. Future studies are encouraged to take these findings to other sectors and regions to explore the influence of new technologies and other external factors. In this way, it would be possible to achieve more advancements in the SCM practices in response to the current global market challenges.
Embargo
Hybrid Model-Based Automated Exterior Vehicle Damage Assessment and Severity Estimation for Insurance Operations
(Institute of Electrical and Electronics Engineers Inc., 2025) Jayagoda, N.M; Kasthurirathna, D
After a vehicle accident, insurance companies face the critical task of assessing the damage sustained by the involved vehicles, a process essential for maintaining the insurer's credibility, building consumer trust, and meeting legal and ethical obligations. This assessment is crucial for ensuring clients' financial protection and proper compensation, upholding the integrity of the insurance process. Traditionally, evaluations have been conducted through manual inspections by experienced professionals who meticulously document vehicle damage. Despite its thoroughness, this approach suffers from significant inefficiencies, high costs, and extended time requirements. Moreover, the method is vulnerable to human errors and subjective biases, which can result in inflated valuations. To overcome these challenges, this research introduces an innovative system designed to leverage technology for analyzing images of damaged vehicles uploaded by the user. This system aims to accurately identify the damaged external components, assess the severity of the damage, and determine the repair needs based on the compromised sections of the vehicle. The findings reveal that the hybrid model used in this research is capable of determining vehicle damage severity with an overall accuracy of 73.3%. This level of accuracy demonstrates the model's robust capability to effectively navigate and analyze complex damage patterns, underscoring its practical applications. By accurately determining damage levels on the first assessment, the model reduces the need for further assessments and disagreements, which frequently cause claim delays. This enhancement increases productivity, reduces administrative costs, and improves the customer experience, resulting in a more efficient, transparent, and satisfactory resolution of vehicle insurance claims.
Embargo
AI-Driven Fault-Tolerant ETL Pipelines for Enhanced Data Integration and Quality
(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramaarachchi, C.K; Perera, S.K; Thelijjagoda, S
The reliability and fault tolerance of ETL (Extract, Transform, Load) pipelines are essential for maintaining data integrity in corporate environments. Traditional ETL systems often depend on manual interventions to resolve data inconsistencies, leading to errors, inefficiencies, and increased operational costs. This study introduces an AI-driven framework designed to improve the fault tolerance of ETL processes by automating data cleaning, standardization, and integration tasks. Using machine learning models, the framework reduces the need for human intervention, enhances data quality, and supports scalability across various data formats. Using real-world data sets, the proposed solution demonstrates its ability to improve operational efficiency and reduce errors within corporate data pipelines. This research addresses a crucial gap in ETL automation, offering a scalable and proactive approach to robust data integration in large-scale corporate settings. The findings highlight the ability of the framework to improve fault tolerance, improve data quality, and offer organizations a competitive advantage in managing complex data ecosystems.
Embargo
Predictive Modeling for Identifying Early Warning Signs of Underperformance in Vocational Education
(Institute of Electrical and Electronics Engineers Inc., 2025) Hettiarachchi D.S.S; Harshanath S.M.B
This study focuses on developing a predictive modeling system to identify early signs of underperformance in vocational education, critical for building a skilled workforce. Addressing challenges like high dropout rates and inadequate graduate preparedness, the system utilizes machine learning techniques such as Neural Networks, Decision Trees, and Logistic Regression. Implemented in Python, it analyzes key features like academic records, attendance, engagement, and socioeconomic factors. Preprocessing steps, such as data cleaning and feature engineering, were implemented, and transfer learning was employed to adapt the model. This combination of feature engineering and transfer learning enables the transfer of knowledge from academic settings to vocational education by identifying and leveraging shared characteristics between the two domains. The system provides real time insights through automated reports and notifications, enabling targeted interventions to improve retention and graduation rates. This scalable approach advances educational technology and informs policies to enhance vocational education outcomes.
Open Access
Designing Culturally Adaptive Emotional Gestures to Enhance Child-Robot Interaction with NAO Robots in ASD Therapy
(Institute of Electrical and Electronics Engineers Inc., 2025) Manukalpa, C.S; Pulasinghe, K; Rajapakshe, S
Integrating social robots into human-robot interactions demands advancements in natural language processing, navigation, computer vision, and expressive gestures to foster meaningful interactions. However, a gap remains in designing culturally relevant and developmentally appropriate gestures, particularly in the Sri Lankan context. Autism Spectrum Disorder (ASD), a neurodevelopmental condition impacting early education, often remains underdiagnosed, exacerbating learning challenges. This study introduces a novel approach utilizing robot-child interactions for ASD screening to minimize such delays. Expressive gestures were developed for the NAO6 humanoid robot to engage Sinhala-speaking children aged 2 to 6 years, including those with ASD, in Sri Lanka. Using the NAOqi Python API and Choregraphe simulator, culturally aligned gestures expressing emotions like happiness, sadness, fear, anger, and more were designed and synchronized with voice and LED effects. Pilot studies with typical children demonstrated the significance of linguistic and cultural alignment in enhancing engagement, emotional response, and trust. By addressing cultural nuances and advancing early ASD screening, this framework holds potential for broader applications in education, therapy, and diagnosis, improving human-robot interactions globally.
Open Access
Evaluation of Machine Learning Models in Student Academic Performance Prediction
(Institute of Electrical and Electronics Engineers Inc., 2025) Sandeepa A.G.R.; Mohottala, S
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach.
Embargo
Intelligent Adaptive Lighting Control: Reinforcement Learning-Based Optimization for Smart Home Energy Efficiency
(Institute of Electrical and Electronics Engineers Inc., 2025) Hewakapuge M.M; Gamage W.G.T; Surendra D.M.B.G.D; Thejan K.G.T; Rajapaksha, S; Rajendran, K
This study introduces a novel research paper outlining a behavioral-based adaptive lighting system that aims to revolutionise smart home lighting by integrating user behavior tracking to enhance energy efficiency and user comfort. Unlike traditional motion-sensor-based lighting, the novelty of this approach is the ability to adapt dynamically to evolving user behaviors through reinforcement learning. The system utilises Wi-Fi-based positioning, GPS and accelerometer data to monitor user movements and classify different areas of the house. Users initially calibrate the home layout through a mobile application, marking room locations and lighting configurations. The system then collects movement data over time to predict optimal lighting schedules based on user routines and refines the predictions and updates lighting adjustments accordingly, minimising energy wastage while maximising user convenience. A serverless backend architecture ensures scalability, cost-effectiveness, and seamless data processing. The adaptive framework continuously refines lighting automation, responding to evolving behavioral patterns.
Embargo
Autonomous Water Quality Monitoring: Integrating UWB Ad-Hoc Networks, Sensor Calibration, and Kubernetes Cloud Architecture
(IEEE Computer Society, 2025) Tharindu, K; Abeysinghe, M; Karunarathne, S; Dilshan, K; Primal, D; Jayakody, A
Water quality monitoring plays a critical role in ensuring environmental sustainability and public health. Traditional methods, while accurate, are time-consuming and lack the ability to provide real-time insights. This study proposes a secure, scalable IoT-based solution utilizing autonomous sensor-equipped boats designed to measure pH, turbidity, and temperature in aquatic environments. The boats navigate predefined grid coordinates generated through a Python-based script and communicate data using UWB in a decentralized ad hoc network operating under the AODV routing protocol. Preprocessed sensor data is transmitted to a base station and securely forwarded to a Kubernetes-based cloud infrastructure for real-time processing and visualization. Communication between the base station and cloud services is secured using HTTPS/TLS encryption. Experimental trials confirm reliable navigation, high sensor accuracy, low latency, and robust security. The system remains cloud-agnostic and is compatible with a range of open-source Kubernetes distributions, enabling deployment flexibility across various environments. This research demonstrates an effective, autonomous approach to real-time water quality monitoring, advancing scalable and sustainable environmental management
Embargo
Deep Learning Based Sinhala Sign Language Recognition
(Institute of Electrical and Electronics Engineers Inc., 2025) Samarakoon, S.C; Weerasinghe, M
Deaf individuals in Sri Lanka rely primarily on Sinhala Sign Language (SSL) for communication due to hearing impairments. However, effective communication between the Deaf and hearing populations remains challenging due to the limited knowledge of SSL among hearing individuals. This research aims to address this gap by developing an SSL gesture recognition system using computer vision and deep learning techniques. Specifically, the study compares the performance of 3D Convolutional Neural Networks (3D-CNNs) and a hybrid 2D Convolutional Neural Network with Long Short-Term Memory (2D-CNN+LSTM) for classifying short-duration spatiotemporal SSL gestures. Additionally, the research emphasizes reducing computational complexity to ensure efficient operation of the system on low-end devices. These contributions advance the accessibility and practical usability of gesture recognition systems for the Sinhala Sign Language.
Embargo
TOWARDS A CIRCULAR ECONOMY: EVALUATING EFFECTIVE IMPLEMENTATION STRATEGIES FOR RECYCLING AND REUSE PROGRAMMES IN SRI LANKA’S CONSTRUCTION INDUSTRY
(Ceylon Institute of Builders, 2025) Lakshan S.D.V.; Wijekoon W.M.C.L.K; Buddhini P.H.Y
Construction waste accounts for a significant portion of the total waste generated in Sri Lanka. To promote a Circular Economy (CE) in the construction industry, it is essential to adopt building procedures that minimize waste, increase the use of recycled materials in new construction, and create markets for recycled and reused materials. However, the lack of modern recycling facilities and technologies hampers the effective processing and reuse of construction materials. Additionally, there is limited market demand for recycled construction materials, primarily due to concerns about product quality and the absence of standardized products. In order to promote sustainability and reduce the industry's reliance on new materials, it is crucial to implement recycling and reuse programs. Therefore, this research aims to evaluate the effective implementation strategies for recycling and reuse programs in Sri Lanka's construction industry. The literature review highlighted these existing programs and identified the challenges and opportunities for implementing recycling and reuse initiatives in Sri Lanka's construction industry. Additionally, semi-structured interviews were conducted with 9 experts, and a questionnaire survey was administered to 48 participants to gather data. The interviews revealed strategies to address the challenges, while thematic analysis was employed to analyze the interview data, and the Relative Importance Index (RII) method was used to evaluate the effectiveness of the identified strategies. A total of 9 strategies were identified for integrating recycling and reuse programs into Sri Lanka's construction industry. The findings of this study may enable to enhance the sustainability in the construction sector by minimizing waste and promoting sustainability goals.
Embargo
Graph Neural Network Based Surrogate Model for Design Informed Structural Optimization
(Springer Science and Business Media Deutschland GmbH, 2025) Ariyasinghe, N; Weeratunga, H; Mallikarachchi, C; Herath, S
Structural optimization of skeletal forms is crucial in weight-sensitive applications. Optimizing such structures often involves iterative, computationally intensive methods, which are inefficient under varying design parameters and constraints. This paper introduces a novel surrogate model based on Graph Neural Network (GNN) for real-time structural optimization, aimed at significantly reducing computational costs. In our approach, trusses composed of pin joints and connecting members are represented as graphs, where joints correspond to vertices and members to edges. This correspondence forms the use of Graph Neural Networks (GNNs) to predict topology and size-optimized truss structures. The GNN models the truss as a graph, with edges denoting member cross-sectional areas and nodes representing truss joints, based on input parameters such as geometry, load combinations, and boundary conditions. The resulting predicted structure reflects the optimized topology and member sizes. The proposed model bypasses the need for iterative computations by learning from a dataset comprising various problem definitions and their corresponding optimized results. This GNN-based optimization holds substantial promise for design scenarios requiring rapid and reliable optimization, demonstrating the potential for significant computational time savings while maintaining high accuracy in predicting near-optimal truss layouts. This is particularly significant in the context of sustainability, where industrial users can produce optimally designed structures with minimal material usage within a fraction of the computational power and time required for different applications. Testing results indicate that the model effectively generalizes across various design scenarios, providing near-optimal solutions with minimal computational effort. Specifically, the predicted structures exhibited a normalized root mean square error (NRMSE) of less than 10−3 and R2 values approaching unity. Additionally, predictions were made in under 0.01 s, demonstrating both accuracy and efficiency.
Open Access
A Spatial Study on the Ecological Signatures of Landscapes in Colombo
(Springer Science and Business Media Deutschland GmbH, 2025) Subasinghe J.C; Madhushani T.M.C.I.; Gomes P.I.A
Urbanization is a governing demographic feature and a significant part of global land transformation. According to the United Nations, more than half of the world’s population lives in urban areas. If not studied and managed properly, urbanization can affect negatively its residents, and in Sri Lanka this is about 20%–30% in commercial areas and residential areas. Yet, studies related to exploring functions and status quo of different landuses are rare and rather unfound in Sri Lanka. This study the variations of temperature, humidity, soil moisture, infiltration rate, shrub cover and tree richness with different landuses namely, cemeteries, parks, residential areas and institutes have been investigated to see whether the landuses actually are the landscapes people perceive. It was found that the humidity of land plots with Institutes is significantly higher than all the other landscape types. Interestingly, it was observed that parks and cemeteries possessed high humidity levels while Institutes and Residential areas possessed a comparatively lower humidity level. The soil moisture content and infiltration rates of institutal landscape significantly differed from those of other landscape types. Shrub cover variation between Residential areas and Institutes was insignificant, while shrub cover of all the other landscape types resulted in substantial differences with a significance level of 0.00. The analysis of variation of multiple ecological factors under landscape types depicted that for all the temperatures, the shrubs cover percentage of cemeteries lies higher than the rest of the landscapes. In cemeteries, initially, the shrub cover increased with the humidity and with increments of humidity level, the shrub cover decreased. Overall sense, the Institutional areas depicted relatively adverse liveable conditions, and Cemeteries depicted most favourable conditions, interestingly it was better than Parks. This study gave insights into how these landscapes be best manged and engineering interventions needed in that regard.