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您的检索式：作者名="Subodh Ranjan"
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| 1 | Study of Substrate and Physico-Chemical Base Classification of the Rivers of Nepal显示文摘The rivers in Nepal are classified in terms of geographical regions but a more scientific classification such as on the ba-sis of morphology is clearly lacking. This study was done in 9 rivers namely Jhikhukhola of the Koshi system, Aandhikhola, Arungkhola, East Rapti, Karrakhola, Seti and main channel Narayani of the Gandaki system, and two independent systems within Nepal, Bagmati and Tinau. Among the morphologies, river bed or the substratum was taken as the main variable for the analysis which was categorized into 7 types as rocks, boulders, cobbles, pebbles, gravels, sand and silt. There were 23 sampling sites each with 2 stretches of around 100m in those rivers. The data were taken as a percentage, and to avoid biases it was observed visually by the same person for a complete year in every season. With 23 sites each with 2 stretches and 4 replicates corresponding to 4 seasons, there are altogether 184 observations, each termed as a case, that constitute this work. Canonical Discrimination Analysis (CDA) which is most suitable when the data pool is huge was applied to see if the rivers studied distinguish themselves in terms of its morphology. The result was remarkably successful and was close to the established regional classification of the rivers. This kind of river classification has great application in the utilization, conservation and restoration of the most important natural re-source of the country. | JHA Bibhuti Ranjan WAIDBACHER Herwig SHARMA Subodh STRAIF Michael | 2010 | Geo-Spatial Information Science2010，13，1： | 0 |
| 2 | Are fails more common than road traffic accidents in pediatric trauma? Experience from a Level 1 trauma centre in New Delhi, India显示文摘 | Annu Babu Amulya Rattan Piyush Ranjan Maneesh Singhal Amit Gupta Subodh Kumar Biplab Mishra Sushma Sagar | 2016 | Chinese Journal of Traumatology2016，19，2： | 0 |
| 3 | Exploring the potential of artificial intelligence techniques in prediction of the removal efficiency of vortex tube silt ejector显示文摘A vortex tube silt ejector is a curative hydraulic structure used to remove sediment deposits from canals and is recognized as one of the most efficient substitutes for physically removing canal sediment.The spatially varied flow in the channel and the rotational flow behavior in the tube make the silt removal process complex.It is even harder to accurately predict the silt removal efficiency by traditional models accurately.However,artificial intelligence(AI)and machine learning approaches have emerged as robust substitutes for studying complex processes.Therefore,this research makes use of AI approaches;support vector machine(SVM),random forest(RF),random tree(RT),and multivariate adaptive regression spline(MARS)to compute the vortex tube silt ejection efficiency using the laboratory data sets.The outcomes of the artificial intelligence(AI)-based techniques also were compared with traditional models.It was found that the RT model(root mean square error,RMSE=2.165,Nash Sutcliffe efficiency,NSE=0.98)outperforms the other applied approaches which had relatively more significant result errors.The sensitivity analysis of the process depicts the extraction ratio as the key parameter in the computation of vortex tube silt ejector removal efficiency.The findings of the AI-based approaches discussed in the current study might be helpful for hydraulic engineers as well as researchers in the assessment of the removal efficiency of vortex tube silt ejectors. | Sanjeev Kumar Chandra Shekhar Prasad Ojha Nand Kumar Tiwari Subodh Ranjan | 2023 | International Journal of Sediment Research2023，38，4： | 0 |
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