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| 1 | Impact of climate change on the streamflow in the glacierized Chu River Basin, Central Asia显示文摘Catchments dominated by meltwater runoff are sensitive to climate change as changes in precipitation and temperature inevitably affect the characteristics of glaciermelt/snowmelt, hydrologic circle and water resources. This study simulated the impact of climate change on the runoff generation and streamflow of Chu River Basin(CRB), a glacierized basin in Central Asia using the enhanced Soil and Water Assessment Tool(SWAT). The model was calibrated and validated using the measured monthly streamflow data from three discharge gauge stations in CRB for the period 1961–1985 and was subsequently driven by downscaled future climate projections of five Global Circulation Models(GCMs) in Coupled Model Inter-comparison Project Phase 5(CMIP5) under three radiative forcing scenarios(RCP2.6, RCP4.5 and RCP8.5). In this study, the period 1966–1995 was used as the baseline period, while 2016–2045 and 2066–2095 as the near-future and far-future period, respectively. As projected, the climate would become warmer and drier under all scenarios in the future, and the future climate would be characterized by larger seasonal and annual variations under higher RCP. A general decreasing trend was identified in the average annual runoff in glacier(–26.6% to –1.0%), snow(–21.4% to +1.1%) and streamflow(–27.7% to –6.6%) for most of the future scenario periods. The projected maximum streamflow in each of the two future scenarios occurred one month earlier than that in the baseline period because of the reduced streamflow in summer months. Results of this study are expected to arouse the serious concern about water resource availability in the headwater region of CRB under the continuously warming climate. Changes in simulated hydrologic outputs underscored the significance of lowering the uncertainties in temperature and precipitation projection. | MA Changkun SUN Lin LIU Shiyin SHAO Ming'an LUO Yi | 2015 | Journal of Arid Land2015，7，4： | 8 |
| 2 | Matching preclusion for k-ary n-cubes显示文摘 | Wang Shiyin Wang Ruixia Lin Shangwei | 2010 | Discrete Applied Mathematics2010，158，18： | 1 |
| 3 | Continuous experiment regarding hydrogen production by coal/CaO reaction with steam (Ⅰ):gas products显示文摘 | Lin Shiyin Harada M Suzuki Y | 2004 | Fuel2004，83，78： | 1 |
| 4 | Developing an innovative method,HyPr-RING,to produce hydrogen from hydrocarbons显示文摘 | Lin Shiyin Suzuki Y Hatano H | 2002 | Energy Conversion and Management2002，43，912： | 1 |
| 5 | Hydrogen production from hydrocarbon by integration of water-carbon reaction and carbon dioxide removal (HyPr-RING Method)显示文摘 | Lin Shiyin Suzuki Y Hatano H | 2001 | Energy & Fuels2001，15，2： | 1 |
| 6 | Observed changes ofcryosphere in China over the second half of the 20th century:an overview显示文摘 | Xiao Cunde Liu Shiyin Zhao Lin | 2007 | Annals of Glaciology2007，46，： | 1 |
| 7 | Hydrogen production from hydrocarbon by integration of water-carbon reaction and carbon dioxide removal (HyPrring method) 显示文摘 | LIN Shiyin SUZUKI Y HATANO H | 2001 | Energy & Fuels2001，15，2： | 1 |
| 8 | Hydrogen production from coal by separating carbon dioxide during gasification 显示文摘 | LIN Shiyin HARADAA M SUZUKIB Y | 2002 | Fuel2002，81，16： | 1 |
| 9 | Continuous experiment regarding hydrogen production by coal/CaO reaction with steam: gas products 显示文摘 | LIN Shiyin HARADAA M SUZUKIB Y | 2004 | Fuel2004，83，78： | 1 |
| 10 | Changes of climate and seasonally frozen ground over the past 30 years in Qinghai–Xizang (Tibetan) Plateau, China显示文摘 | Lin Zhao Chien-Lu Ping Daqing Yang Guodong Cheng Yongjian Ding Shiyin Liu | 2004 | Global and Planetary Change2004，，1： | 1 |
| 11 | Cooperative prediction method of gas emission from mining face based on feature selection and machine learning显示文摘Collaborative prediction model of gas emission quantity was built by feature selection and supervised machine learning algorithm to improve the scientifc and accurate prediction of gas emission quantity in the mining face.The collaborative prediction model was screened by precision evaluation index.Samples were pretreated by data standardization,and 20 characteristic parameter combinations for gas emission quantity prediction were determined through 4 kinds of feature selection methods.A total of 160 collaborative prediction models of gas emission quantity were constructed by using 8 kinds of classical supervised machine learning algorithm and 20 characteristic parameter combinations.Determination coefcient,normalized mean square error,mean absolute percentage error range,Hill coefcient,mean absolute error,and the mean relative error indicators were used to verify and evaluate the performance of the collaborative forecasting model.As such,the high prediction accuracy of three kinds of machine learning algorithms and seven kinds of characteristic parameter combinations were screened out,and seven optimized collaborative forecasting models were fnally determined.Results show that the judgement coefcients,normalized mean square error,mean absolute percentage error,and Hill inequality coefcient of the 7 optimized collaborative prediction models are 0.969–0.999,0.001–0.050,0.004–0.057,and 0.002–0.037,respectively.The determination coefcient of the fnal prediction sequence,the normalized mean square error,the mean absolute percentage error,the Hill inequality coefcient,the absolute error,and the mean relative error are 0.998%,0.003%,0.022%,0.010%,0.080%,and 2.200%,respectively.The multi-parameter,multi-algorithm,multi-combination,and multijudgement index prediction model has high accuracy and certain universality that can provide a new idea for the accurate prediction of gas emission quantity. | Jie Zhou Haifei Lin Hongwei Jin Shugang Li Zhenguo Yan Shiyin Huang | 2022 | International Journal of Coal Science & Technology2022，9，4： | 0 |
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