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Reliable and accurate prediction model capturing the changes in solar radiation is essential in the power generation and renewable carbon-free energy industry. Malaysia has immense potential to develop such an industry due to its location in the equatorial zone and its climatic characteristics with high solar energy resources. However, solar energy accounts for only 2–4.6% of total energy utilization. Recently, in developed countries, various prediction models based on artificial intelligence (AI) techniques have been applied to predict solar radiation. In this study, one of the most recent AI algorithms, namely, boosted decision tree regression (BDTR) model, was applied to predict the changes in solar radiation based on collected data in Malaysia. The proposed model then compared with other conventional regression algorithms, such as linear regression and neural network. Two different normalization techniques (Gaussian normalizer binning normalizer), splitting size, and different input parameters were investigated to enhance the accuracy of the models. Sensitivity analysis and uncertainty analysis were introduced to validate the accuracy of the proposed model. The results revealed that BDTR outperformed other algorithms with a high level of accuracy. The funding of this study could be used as a reliable tool by engineers to improve the renewable energy sector in Malaysia and provide alternative sustainable energy resources. [ABSTRACT FROM AUTHOR]

In this study, Centella asiatica and surface soils were collected from 12 sampling sites in Peninsular Malaysia, and the barium (Ba) concentrations were determined. The Ba concentration [μg/g dry weight (dw)] was 63.72 to 382.01 μg/g in soils while in C. asiatica, Ba concentrations ranged from 5.05 to 21.88 μg/g for roots, 3.31 to 11.22 μg/g for leaves and 2.37 to 6.14 μg/g for stems. In C. asiatica, Ba accumulation was found to be the highest in roots followed by leaves and stems. The correlation coefficients (r) of Ba between plants and soils were found to be significantly positively correlated, with the highest correlation being between roots-soils (r=0.922, p<005), followed by leaves-soils (r=0.890, p<005) and stems-soils (r=0.848, p<005). This indicates that these three parts of C. asiatica are good biomonitors of Ba pollution. For the transplantation study, four sites were selected as unpolluted [(Universiti Putra Malaysia (UPM)], semi-polluted (Seri Kembangan and Balakong) and polluted sites (Juru). Based on the transplantation study under experimental field and laboratory conditions, Ba concentrations in C. asiatica were significantly (p<0.05) higher after three weeks of exposure at Seri Kembangan, Balakong and Juru. Thus, these experimental findings confirm that the leaves, stems and roots of C. asiatica can reflect the Ba levels in the soils where this plant is found. Three weeks after back transplantation to clean soils, the Ba levels in C. asiatica were still higher than the initial Ba level even though Ba elimination occurred. In conclusion, the leaves, stems and roots of C. asiatica are good biomonitors of Ba pollution. [ABSTRACT FROM AUTHOR]