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Drinking Water Quality in the Sagarmatha National Park, Nepal 被引量:1
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作者 Kirsten Nicholson Emily Hayes +2 位作者 klaus neumann Carolyn Dowling Subodh Sharma 《Journal of Geoscience and Environment Protection》 2016年第4期43-53,共11页
In 2014 we began the first systematic study of water quality, specifically fecal contamination of drinking water in the Khumbu Valley, Sagarmatha National Park (SNP, Mt. Everest region), Nepal. Our goal was to identif... In 2014 we began the first systematic study of water quality, specifically fecal contamination of drinking water in the Khumbu Valley, Sagarmatha National Park (SNP, Mt. Everest region), Nepal. Our goal was to identify coliform bacteria and E. coli in drinking water and groundwater-fed springs to generate a data set that will function as a base for potable water supplies and further monitoring. Sampling occurred in May (pre-monsoon summer) and early November (post-monsoon early winter) 2014. Sample sites were selected based on proximity to villages and primary use as a drinking water source. Overall, the data presented a predictable correlation between fecal contamination and both elevation and increasing population/tourist traffic. Drinking water within the study area met current World Health Organization drinking water standards for the physical properties of temperature (2.8°C - 13°C), pH (5.27 - 7.24), conductivity (14.5 - 133 mS) and TDS (7.24 - 65.5 ppm). Samples from the more populated, lower altitude areas had higher levels of E. coli. Samples collected and analyzed in May (pre-monsoon summer) had a higher level of E. coli and coliform bacteria than samples collected in November (post-monsoon early winter) suggesting a seasonal dependence overlaid on the population signature. Surface water typically had higher E. coli values than groundwater-fed springs. Temperature, total dissolved solids and conductivity generally decreased with increasing elevation, whereas pH increased with increasing elevation. There appears to be significant presence of fecal contamination of water sources due to a combination of tourism, elevation and seasons. 展开更多
关键词 Fecal Coliform E. coli Mt. Everest Drinking Water
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The Impact of Precipitation on Drinking Water Resources in the Sagarmatha National Park (Mt. Everest Region), Nepal 被引量:1
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作者 Kirsten Ngaire Nicholsona klaus neumann Subodh Sharma 《Journal of Water Resource and Protection》 2019年第11期1351-1368,共18页
This study focuses on the link between precipitation, the bacteriological characteristics, and the physical parameters of drinking water sources from 2016 to 2018 in the Sagarmatha National Park (Mt. Everest region), ... This study focuses on the link between precipitation, the bacteriological characteristics, and the physical parameters of drinking water sources from 2016 to 2018 in the Sagarmatha National Park (Mt. Everest region), Nepal. Surface water shows a positive correlation between bacteria content, altitude and corresponding temperature, whereas water from springs shows no correlation between bacteria content and altitude and corresponding temperature. Correlation between precipitation data and both pH and conductivity suggests a link between drinking water quality and precipitation whereby high precipitation rates result in increased contamination of both surface water and springs used for drinking water. This data also indicates that during periods of low precipitation, water handling is likely to contribute to water contamination. These results highlight vulnerability to climate change as melting glacial ice and changing precipitation patterns are key factors for safe drinking water. 展开更多
关键词 HIMALAYA MOUNTAINS DRINKING Water PRECIPITATION FECAL Contamination Climate Change
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Regularization by Intrinsic Plasticity and Its Synergies with Recurrence for Random Projection Methods 被引量:1
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作者 klaus neumann Christian Emmerich Jochen J. Steil 《Journal of Intelligent Learning Systems and Applications》 2012年第3期230-246,共17页
Neural networks based on high-dimensional random feature generation have become popular under the notions extreme learning machine (ELM) and reservoir computing (RC). We provide an in-depth analysis of such networks w... Neural networks based on high-dimensional random feature generation have become popular under the notions extreme learning machine (ELM) and reservoir computing (RC). We provide an in-depth analysis of such networks with respect to feature selection, model complexity, and regularization. Starting from an ELM, we show how recurrent connections increase the effective complexity leading to reservoir networks. On the contrary, intrinsic plasticity (IP), a biologically inspired, unsupervised learning rule, acts as a task-specific feature regularizer, which tunes the effective model complexity. Combing both mechanisms in the framework of static reservoir computing, we achieve an excellent balance of feature complexity and regularization, which provides an impressive robustness to other model selection parameters like network size, initialization ranges, or the regularization parameter of the output learning. We demonstrate the advantages on several synthetic data as well as on benchmark tasks from the UCI repository providing practical insights how to use high-dimensional random networks for data processing. 展开更多
关键词 Extreme Learning Machine Reservoir Computing MODEL SELECTION Feature SELECTION MODEL Complexity INTRINSIC PLASTICITY REGULARIZATION
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