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- Kumar Singh, Abhinav, et al.
(författare)
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An Integrated Statistical-Machine Learning Approach for Runoff Prediction
- 2022
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- Nowadays, great attention has been attributed to the study of runoff and its fluctuation over space and time. There is a crucial need for a good soil and water management system to overcome the challenges of water scarcity and other natural adverse events like floods and landslides, among others. Rainfall–runoff (R-R) modeling is an appropriate approach for runoff prediction, making it possible to take preventive measures to avoid damage caused by natural hazards such as floods. In the present study, several data-driven models, namely, multiple linear regression (MLR), multiple adaptive regression splines (MARS), support vector machine (SVM), and random forest (RF), were used for rainfall–runoff prediction of the Gola watershed, located in the south-eastern part of the Uttarakhand. The rainfall–runoff model analysis was conducted using daily rainfall and runoff data for 12 years (2009 to 2020) of the Gola watershed. The first 80% of the complete data was used to train the model, and the remaining 20% was used for the testing period. The performance of the models was evaluated based on the coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), and percent bias (PBAIS) indices. In addition to the numerical comparison, the models were evaluated. Their performances were evaluated based on graphical plotting, i.e., time-series line diagram, scatter plot, violin plot, relative error plot, and Taylor diagram (TD). The comparison results revealed that the four heuristic methods gave higher accuracy than the MLR model. Among the machine learning models, the RF (RMSE (m3/s), R2, NSE, and PBIAS (%) = 6.31, 0.96, 0.94, and −0.20 during the training period, respectively, and 5.53, 0.95, 0.92, and −0.20 during the testing period, respectively) surpassed the MARS, SVM, and the MLR models in forecasting daily runoff for all cases studied. The RF model outperformed in all four models’ training and testing periods. It can be summarized that the RF model is best-in-class and delivers a strong potential for the runoff prediction of the Gola watershed.
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| 2. |
- Dhama, Kuldeep, et al.
(författare)
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SARS-CoV-2 emerging Omicron subvariants with a special focus on BF.7 and XBB.1.5 recently posing fears of rising cases amid ongoing COVID-19 pandemic
- 2022
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Ingår i: Journal of Experimental Biology and Agricultural Sciences. - : JEBAS. - 2320-8694. ; 10:6, s. 1215-1221
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Tidskriftsartikel (refereegranskat)abstract
- The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron versions have been the sole one circulating for quite some time. Subvariants BA.1, BA.2, BA.3, BA.4, and BA.5 of the Omicron emerged over time and through mutation, with BA.1 responsible for the most severe global pandemic between December 2021 and January 2022. Other Omicron subvariants such as BQ.1, BQ.1.1, BA.4.6, BF.7, BA.2.75.2, XBB.1 appeared recently and could cause a new wave of increased cases amid the ongoing COVID-19 pandemic. There is evidence that certain Omicron subvariants have increased transmissibility, extra spike mutations, and ability to overcome protective effects of COVID-19 neutralizing antibodies through immunological evasion. In recent months, the Omicron BF.7 subvariant has been in the news due to its spread in China and a small number of other countries, raising concerns about a possible rebound in COVID-19 cases. More recently, the Omicron XBB.1.5 subvariant has captured international attention due to an increase in cases in the United States. As a highly transmissible sublineage of Omicron BA.5, as well as having a shorter incubation time and the potential to reinfect or infect immune population, BF.7 has stronger infection ability. It appears that the regional immunological landscape is affected by the amount and timing of previous Omicron waves, as well as the COVID-19 vaccination coverage, which in turn determines whether the increased immune escape of BF.7 and XBB.1.5 subvariants is sufficient to drive new infection waves. Expanding our understanding of the transmission and efficacy of vaccines, immunotherapeutics, and antiviral drugs against newly emerging Omicron subvariants and lineages, as well as bolstering genomic facilities for tracking their spread and maintaining a constant vigilance, and shedding more light on their evolution and mutational events, would help in the development of effective mitigation strategies. Importantly, reducing the occurrence of mutations and recombination in the virus can be aided by bolstering One health approach and emphasizing its significance in combating zoonosis and reversal zoonosis linked with COVID-19. This article provides a brief overview on Omicron variant, its recently emerging lineages and subvairants with a special focus on BF.7 and XBB.1.5 as much more infectious and highly transmissible variations that may once again threaten a sharp increase in COVID-19 cases globally amid the currently ongoing pandemic, along with presenting salient mitigation measures.
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| 3. |
- Islam, Md. Aminul, et al.
(författare)
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Association of household fuel with acute respiratory infection (ARI) under-five years children in Bangladesh
- 2022
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Ingår i: Frontiers In Public Health. - : Frontiers Media SA. - 2296-2565. ; 10
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Tidskriftsartikel (refereegranskat)abstract
- In developing countries, acute respiratory infections (ARIs) cause a significant number of deaths among children. According to Bangladesh Demographic and Health Survey (BDHS), about 25% of the deaths in children under-five years are caused by ARI in Bangladesh every year. Low-income families frequently rely on wood, coal, and animal excrement for cooking. However, it is unclear whether using alternative fuels offers a health benefit over solid fuels. To clear this doubt, we conducted a study to investigate the effects of fuel usage on ARI in children. In this study, we used the latest BDHS 2017-18 survey data collected by the Government of Bangladesh (GoB) and estimated the effects of fuel use on ARI by constructing multivariable logistic regression models. From the analysis, we found that the crude (the only type of fuel in the model) odds ratio (OR) for ARI is 1.69 [95% confidence interval (CI): 1.06-2.71]. This suggests that children in families using contaminated fuels are 69.3% more likely to experience an ARI episode than children in households using clean fuels. After adjusting for cooking fuel, type of roof material, child's age (months), and sex of the child-the effect of solid fuels is similar to the adjusted odds ratio (AOR) for ARI (OR: 1.69, 95% CI: 1.05-2.72). This implies that an ARI occurrence is 69.2% more likely when compared to the effect of clean fuel. This study found a statistically significant association between solid fuel consumption and the occurrence of ARI in children in households. The correlation between indoor air pollution and clinical parameters of ARI requires further investigation. Our findings will also help other researchers and policymakers to take comprehensive actions by considering fuel type as a risk factor as well as taking proper steps to solve this issue.
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