2023

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Author: search.filters.author.Rathnayake, NHas files: Yes

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    PublicationOpen Access
    Sustainability practices and organizational performance during the COVID-19 pandemic and economic crisis: A case of apparel and textile industry in Sri Lanka
    (NLM (Medline), 2023-07-04) Weerasinghe, N; Weerasinghe, A; Perera, Y; Tennakoon, S; Rathnayake, N; Jayasinghe, P
    The apparel and textile industry is the backbone of the Sri Lankan economy, contributing significantly to the country's gross domestic product (GDP). The coronavirus (COVID-19) pandemic, which also triggered the ongoing economic crisis in Sri Lanka, has a profound effect on the organizational performance of apparel sector firms in Sri Lanka. In this context, the study examines the impact of multi-dimensional corporate sustainability practices on organizational performance in the said sector. The study employed the partial least squares structural equation modelling (PLS-SEM) technique for analysing and testing the hypothesis of the study while using Smart PLS 4.0 software as the analysis tool. Relevant data were collected through a questionnaire from 300 apparel firms registered with the Board of Investment of Sri Lanka (BOI). The study results indicated that "economic vigour," "ethical practices," and "social equity" have a significant impact on organizational performance, while "corporate governance" and "environmental performance" have an insignificant impact. Unique discoveries from this study would be useful to prosper organizational performance and formulate novel sustainable future strategies not limited to the garment industry even during harsh economic conditions. Copyright: © 2023 Weerasinghe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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    PublicationOpen Access
    Projected Water Levels and Identified Future Floods: A Comparative Analysis for Mahaweli River, Sri Lanka
    (IEEE, 2023-01) Rathnayake, N; Rathnayake, U; Chathuranika, I; Dang, T. L; Hoshino, Y
    The Rainfall-Runoff (R-R) relationship is essential to the hydrological cycle. Sophisticated hydrological models can accurately investigate R-R relationships; however, they require many data. Therefore, machine learning and soft computing techniques have taken the attention in the environment of limited hydrological, meteorological, and geological data. The accuracy of such models depends on the various parameters, including the quality of inputs and outputs and the used algorithms. However, identifying a perfect algorithm is still challenging. This study develops a fuzzy logic-based algorithm called Cascaded-ANFIS to accurately predict runoff based on rainfall. The model was compared against three regression algorithms: Long Short-Term Memory, Grated Recurrent Unit, and Recurrent Neural Networks. These algorithms have been selected due to their outstanding performances in similar studies. The models were tested on the Mahaweli River, the longest in Sri Lanka. The results showcase that the Cascaded-ANFIS-based model outperforms the other algorithms. The correlation coefficient of each algorithm’s predictions was 0.9330, 0.9120, 0.9133, 0.8915, 0.6811, 0.6811, and 0.6734 for the Cascaded-ANFIS, LSTM, GRU, RNN, Linear, Ridge, and Lasso regression models respectively. Hence, this study concludes that the proposed algorithm is 21% more accurate than the second-best LSTM algorithm. In addition, Shared Socio-economic Pathways (SSP2-4.5 and SSP5-8.5 scenarios) were used to generate future rainfalls, forecast the near-future and mid-future water levels, and identify potential flood events. The future forecasting results indicate a decrease in flood events and magnitudes in both SSP2-4.5 and SSP5-8.5 scenarios. Furthermore, the SSP5-8.5 scenario shows drought weather from May to August yearly. The results of this study can effectively be used to manage and control water resources and mitigate flood damages.