Research Publications Authored by SLIIT Staff
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195
This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open Access Analyzing relationships between rainfall and paddy harvest using artificial neural network (ANN) approach: case studies from North-western and North-central provinces, Sri Lanka(The Faculty of Agricultural Sciences of the Sabaragamuwa University of Sri Lanka, 2022-01-04) Ranasinghe, T; Rathnayake, U. S; Gunawardena, G; Wimalasiri, E. MPurpose: Food and agriculture are frequently affected from on-going climate change. A significant percentage of annual harvest is lost due to extreme climatic conditions in different parts of the world. Sri Lanka is considered as a country which is vulnerable to climate change. Therefore, this research presents a detailed analysis to find out the non-linear relationships between the rainfall and paddy harvest in two major provinces of Sri Lanka. Research Method: North-central and North-western provinces as two major agricultural areas were selected for the study. Rainfall trends were identified using non-parametric Mann-Kendall and Sen’s slope estimator tests. The artificial neural network (ANN) approach was used to establish non-linear relationships between rainfall and paddy yield. Findings: There was no significant (p > 0.05) linear correlation between rainfall amount and the rainfed paddy yield in tested locations. However, no clear relationship between the rainfall and rain fed yield were found in the 14 predefined functions (polynomial, logarithmic, exponential and trigonometric) derived using ANN where the calculated coefficients of determination were less than 0.3. Research Limitations: Due to lack of other climate variables such as temperatures, a significant relationship was not observed in this study. Originality/value: We have shown that non-linear artificial neural network approach can be used to study the impact of climate on agricultural production in Sri Lanka.Publication Embargo Impact of climate variability on hydropower generation: A case study from Sri Lanka(Taylor & Francis Group, 2018-06-18) Khaniya, B.; Priyantha, H. G; Baduge, N; Azamathulla, H. M; Rathnayake, U. SHydropower accounts for 16.4% of world’s electricity demand. The key element in hydropower generation is the runoff and this runoff totally depends on the precipitation. However, the future climate is predicted to be debatable and can severely affect the water resources around the world. Therefore, a critical question to answer by the research community is, what would be the impact of climate change/variability on hydropower development? Hence, this paper aims to study the impingement of climate change on hydropower generation for Denawaka Ganga mini-hydropower located in Ratnapura district, Sri Lanka. Multi-year rainfall trend analysis for 30 years along with power generation trend study for 6 years have been carried out to evaluate the performance of the hydropower station under possible shifting precipitation pattern. Mann–Kendall test and Sen’s slope estimator tests were used to culminate the trend analysis. Seasonal and monthly trend analysis did not render negative trends (except one rain gauge) in rainfall. However, positive rainfall trends were found in several rain gauging stations for several months. Power generation trend study showcased a decreasing trend in electricity generation for January and November. Nevertheless, the results elucidate that the catchment area is not under an intense threat due to the climate variability.