http://ir.lib.ruh.ac.lk/handle/iruor/7375 Tue, 07 Apr 2026 01:40:15 GMT 2026-04-07T01:40:15Z http://ir.lib.ruh.ac.lk/handle/iruor/18459 The Role of planned townships for regional Development (a case study of Thambuttegama township. Withanage, W.K.N.C. Over the past few decades, the role of small towns as service centers in regional development was a controversial subject of debate, whether they perform key functions required for regional development, both in developed and developing environs. These centers are at the lower order of urban hierarchy, and they have been planned purposefully in many countries of Asia, Africa and Latin America, through "UFRD Approach" to gear development of their backward rural areas. Small towns are seen as centers of innovation & modernization that trickle down to rural neighborhoods. Thus, most effective and rational spatial and location planning strategy is essential to promote small centers within well-articulated, integrated and balanced urban hierarchy. Objectives of the study were to evaluate the role of Tambuththegama Township for developing surrounding areas, demarcated as the sphere of influence, and also assess the existing spatial and functional structure of the township. Through Mahaweli Development Programme, Township become the growth foci in NCP while attracting more central functions and also consumers from rural neighborhoods. The primary data on commercial and non-governmental functions of township were collected in the field and secondary data and other related information were gathered from official statistics, other published materials, unpublished reports and internal records etc. After pre coding, editing and coding collected Data was tabulated in order to analyze applying appropriate statistical techniques. The GIS based analytical tools such as overly, buffering and network analysis were applied to visualize the findings revealed through this study. Wed, 01 Jan 2014 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/18459 2014-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/18458 A Rethinking to Integrate Indigenous Knowledge and Natural Resources Management of Sri Lanka. Withanage, W.K.N.C.; Gunathilaka, M.D.K.L.; Mishra, P.K. Indigenous knowledge (IK) is the special information that is restricted to a specific culture or civilization. It is sometimes referred 10 as traditional science, folk knowledge, local knowledge, and people's knowledge. It establishes a connec­tion between each person's survival and the entirety of nature and the components that make up life. Indigenous knowledge incorporates all aspects of life-spiritu­aljty, history, cultural practices, social interactions, language, and healing. It presents real-world examples of how com.munjties interact with the environment and offers workable solutions to people's issues. Inrugenous peoples make sigruficanl contribu­tions to the management of sustainable resources. Recent studies demonstrate bow natural resource managers might enhance their conservation strategies by consid­ering the requirements and viewpoints of indigenous people. Indigenous knowl­edge and natural resource management create many environmental, social, cultural, and economic benefits for all. Indigenous people can contribute to the preservation of natural resources by safeguarrung historic structures, mjnimizing environmental degradation, and malting handicrafts that will draw tourists and sustainably improve the local economy. Sri Lanka is one of the countries that possess indigenous people whose history dates back to the fifth century BC. However, Sri Lankan local knowl­edge is limited only to very few parts of the country as in dry zone, and in parts of smal.1 groups have been identified the applicability of local knowledge 10 sustafoable Mon, 01 Jan 2024 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/18458 2024-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/18457 An application of the remote sensing derived indices for drought monitoring in a dry zone district, in tropical island. Wijesinghe, W.M.D.C.; Withanage, W.K.N.C.; Mishra, P.K.; Ranagalage, M.; Abdelrahman, K.; Fnais, M.S. Recent research has shown that droughts have intensified in South Asia over the past two decades. As a natural disaster, this has severely impacted people’s livelihoods, especially in the dry zones of Sri Lanka. Thus, to ensure human well-being, security of water resources, and ecosystem health, it is essential to minimize the impacts of droughts using reliable information. Remote sensing (RS) data and techniques help bridge the gap by enabling the analysis of drought phenomena through a diverse array of indices developed in the fields. However, until now, there has been a lack of systematic monitoring and reliable data for accurately characterizing droughts in the study area. As the first comprehensive analysis, we tried to evaluate the spatial–temporal dynamics of drought conditions in two Divisional Secretariat Divisions of the Anuradhapura district, Sri Lanka using eight standardized remote sensing-derived indices over a decade (2013–2023) including the Standardized Precipitation Index (SPI). SPI values indicated that the region has experienced notably below-average precipitation. According to SPI results, a significant portion of the Medawchchiya area experienced arid conditions in 2023. All other indices proved that 2018 was the driest year and 2013 was the wettest year among the three time points, as reflected by their low and high index values. However, according to NVSMI and LST, the wettest year is 2023, with only 1.78 % of areas experiencing severe drought and a maximum LST of 31.4 ◦C. LULC change detection revealed that 14.3 % of agricultural lands and 5.1 % of forest areas were converted into barren lands over a decade. Overall, this conversion may be another leading factor contributing to increased LST and dryness in the area during the concerned period while increasing the mean LST of barren land by around 4.7 ◦C per decade. Land surface-related and vegetation-related indices, such as NDWI, NDMI, LST, and NDVI, exhibited a more pronounced impact on short-term drought occurrences. The findings revealed that average precipitation coincides with short-term drought episodes in the area, with 2018 standing out as having the least rainfall and the driest year. The study’s findings may provide additional insights for planning authorities, supporting environmental protection and enhancing agricultural production by mitigating droughts’ impacts through short- and long-term strategies. Although, the study focused on a small area, a similar approach could be extended to other areas by incorporating advanced machine learning techniques and additional drought indices in the future. Mon, 01 Jan 2024 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/18457 2024-01-01T00:00:00Z http://ir.lib.ruh.ac.lk/handle/iruor/18456 A performance evaluation of random forest, artificial neural network, and support vector machine learning algorithms to predict spatio-temporal land use-land cover dynamics: a case from lusaka and colombo. Mutale, B.; Withanage, W.K.N.C.; Mishra, P.K.; Shen, J.; Abdelrahman, K.; Fnais, M.S. Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision-making, it is imperative to assess the performance and reliability of classification algorithms in detecting LULC changes. While research on the application of machine learning algorithms in LULC evaluation is widespread in many countries, it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the reliability and performance of support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms for detecting changes in land use and land cover taking Lusaka and Colombo City as the study area from 1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager (OLI). The results reveal that the RF and ANN models exhibited superior performance, both achieving Mean Overall Accuracy (MOA) of 96% for Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM model yielded Overall Accuracy (OA) ranging between 77% and 94% for the years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA and kappa coefficients, ranging between 0.92 and 0.97, when compared to both the ANN and SVM models, across both study areas. A predominant land use change was observed as the expansion of vegetation by 11,990 ha (60.4%), primarily through the conversion of 1,926 ha of bare lands into vegetation in Lusaka during 1995–2005. However, a noteworthy shift was observed as built-up areas experienced significant growth from 2005 to 2023, with a total increase of 25,110 ha (71%). However, despite the conversion of vegetation to built-up areas during the entire period from 1995 to 2023, there was still a net gain of over 11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas(62.3%) during concerned period. LULC simulation also indicated a 160-ha expansion of built-up areas during the 2023–2035 period in Lusaka. Likewise, Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the RF algorithm outperformed the ANN and SVM algorithms. Additionally, the prediction and simulation results indicate an upward trend in built-up areas in both scenarios. The resultant land cover maps provide a crucial baseline that will be invaluable for urban planning and policy development agencies in both countries. expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha Mon, 01 Jan 2024 00:00:00 GMT http://ir.lib.ruh.ac.lk/handle/iruor/18456 2024-01-01T00:00:00Z