Browsing by Author "Chaminda, S.P."

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    Assessment of seasonal and spatial water quality changes in Kelani river, sri lanka
    (Division of Sustainable Resources Engineering, Hokkaido University, Japan, 2024) Rathnayake, A.G.S.N.; Luxman, R.; Udayanga, N.A.P.; Chaminda, S.P.; Ishankha, W.C.A.; Gunawardhana, L.; Iresha, H.; Elakneswaran, Y.; Dassanayake, A.; Jayawardena, C.
    The deteriorating water quality of the Kelani River over time has negatively affected environmental health and sustainability. This study aims to determine the relationship between land use types and its impact on the water quality within the Kelani River basin. The analysis utilized a dataset comprising 23 parameters related to water quality, spanning 17 sampling locations along both the primary river and its tributaries from 2003 to 2023. IBM SPSS Statistics (Version 26), was utilized for data analysis, focusing on 7 water quality parameters (pH, dissolved oxygen (DO), chemical oxygen demand (COD), biological oxygen demand (BOD), nitrate, phosphate, and turbidity) that were influenced by land use. From this analysis, 6 sampling locations were selected to represent various segments of the stream, including Aguruwella and Nakkawita for the upstream segment, Pugoda Ela and Wak Oya for the middle stream, and Rakgahawatte Ela and Maha Ela for the downstream segment. This study utilized a combination of GIS and statistical methods over 4 years with a 6-year time interval (2004, 2010, 2016, and 2022). The land use maps were generated by categorizing area into 4 land use types as agricultural area, vegetation area, built-up area, and others, using maximum likelihood supervised classification. Accuracy assessment using the kappa coefficient revealed that overall accuracy was greater than 85 %, for all six sub-catchments across all four years. From the analysis, it shows that the water quality parameters are significantly varied spatially and temporally. From upstream to downstream and over time, water quality has declined. Regression analysis shows the relationship between land use types and 7 water quality parameters. pH, DO, COD, BOD, and nitrate show a correlation with built-up lands, pH, DO, COD, nitrate and turbidity with vegetation areas, and phosphate with agricultural areas. Moreover, this study highlighted, built-up lands and agricultural lands negatively influenced the water quality, while vegetation areas positively influenced. By identifying the correlation between land use types and water quality, this study helps to preserve and enhance the water quality of the Kelani River basin by implementing proper land use management strategies.
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    Enhancing stockpile inventory management through UAV- based volume estimation: a case study of salt stockpiles in Hambantota mahalewaya
    (Division of Sustainable Resources Engineering, Hokkaido University, Japan, 2024) Perera, M.T.R.D.; Wijesundara, K.K.G.I.; Jayawarna, M.D.; Chaminda, S.P.; Madhurshan, R.; Samarakoon, K.G.A.U.; Iresha, H.; Elakneswaran, Y.; Dassanayake, A.; Jayawardena, C.
    Accurate volume estimation of stockpiles is crucial in industries such as Mining, Construction, salt, and Agriculture to optimize resource utilization. This study evaluates the effectiveness of Unmanned Aerial Vehicles (UAVs) compared to Differential Global Positioning System (DGPS) and Total Station (TS) methods for volume estimation of outdoor salt stockpiles in Hambantota Mahalewaya, Southern province of Sri Lanka. The inventory identified two stockpiles, stockpile 1 and stockpile 2, with volumes of 1832.25 m3 and 819 m3, respectively. An optimal elevation of 55m was utilized for UAV surveys, and the results were compared with DGPS and TS measurements. UAV surveying factors affecting errors, including image resolution, Ground Control Points (GCPs), and image processing software, were assessed for both stockpiles. Survey time and cost for each method were also analyzed. Pix4dMapper and Agisoft Metashape software processed UAV images, while Civil3D software processed DGPS and TS data. Results indicated that increasing UAV survey elevation reduced volume error percentages for both stockpiles, with and without GCPs. For Stockpile 1, UAV volume estimation showed a 0.88% difference from the actual volume, compared to 4.81% for DGPS and 3.35% for TS. Conversely, for Stockpile 2, UAV estimation differed by 0.95%, while DGPS and TS showed differences of 0.56% and 0.10%, respectively. UAV surveys proved efficient in terms of survey time and labor intensity. Despite technological advancements, challenges remain, particularly in addressing topographical variations for accurate volume estimation. To improve UAV-based estimation, addressing bottom elevation discrepancies by establishing fixed benchmarks on flat terrains was suggested. Nonetheless, UAV-based approaches offer fast and relatively reliable results, indicating their potential for widespread adoption.