Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydr...Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season(October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression(MLR) model(a conventional measuring method) and random forest(RF) model(a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R^2 value(R^2=0.74; R^2, coefficient of determination) and a lower bias error(RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.展开更多
Let y = y（x） be a function defined by a continued fraction. A lower bound for │A│ =│β1y1 ＋β2y2 ＋α│ is given, where y1 = y（x1）, y2 = y（x2）, x1 and x2 are positive integers, α,β and β2 are algebraic ir...Let y = y（x） be a function defined by a continued fraction. A lower bound for │A│ =│β1y1 ＋β2y2 ＋α│ is given, where y1 = y（x1）, y2 = y（x2）, x1 and x2 are positive integers, α,β and β2 are algebraic irrational numbers.展开更多
This paper makes the following investigations.1. To solve the second open problem proposed by M.Morii and M.Kasahar[1];2. To prove the nonexistence of PSSP sequence with the smallest(or biggest) density;3. To find the...This paper makes the following investigations.1. To solve the second open problem proposed by M.Morii and M.Kasahar[1];2. To prove the nonexistence of PSSP sequence with the smallest(or biggest) density;3. To find the PSSP sequence with (complementary) Hamming weight m for every positive integer m;4. To propose a generalization form of the known IYM sequence.展开更多
The method of matrix continued fraction is used to investigate stochastic resonance (SR) in the biasedsubdiffusive Smoluchowski system within linear response range.Numerical results of linear dynamic susceptibility an...The method of matrix continued fraction is used to investigate stochastic resonance (SR) in the biasedsubdiffusive Smoluchowski system within linear response range.Numerical results of linear dynamic susceptibility andspectral amplification factor are presented and discussed in two-well potential and mono-well potential with differentsubdiffusion exponents.Following our observation,the introduction of a bias in the potential weakens the SR effect inthe subdiffusive system just as in the normal diffusive case.Our observation also discloses that the subdiffusion inhibitsthe low-frequency SR,but it enhances the high-frequency SR in the biased Smoluchowski system,which should reflect a'flattening' influence of the subdiffusion on the linear susceptibility.展开更多
基金financially supported by the National Key Research and Development Program of China (2017YFB0504201)the National Natural Science Foundation of China (41761014, 41401050)the Foundation of A Hundred Youth Talents Training Program of Lanzhou Jiaotong University
文摘Snowfall is one of the dominant water resources in the mountainous regions and is closely related to the development of the local ecosystem and economy. Snowfall predication plays a critical role in understanding hydrological processes and forecasting natural disasters in the Tianshan Mountains, where meteorological stations are limited. Based on climatic, geographical and topographic variables at 27 meteorological stations during the cold season(October to April) from 1980 to 2015 in the Tianshan Mountains located in Xinjiang of Northwest China, we explored the potential influence of these variables on snowfall and predicted snowfall using two methods: multiple linear regression(MLR) model(a conventional measuring method) and random forest(RF) model(a non-parametric and non-linear machine learning algorithm). We identified the primary influencing factors of snowfall by ranking the importance of eight selected predictor variables based on the relative contribution of each variable in the two models. Model simulations were compared using different performance indices and the results showed that the RF model performed better than the MLR model, with a much higher R^2 value(R^2=0.74; R^2, coefficient of determination) and a lower bias error(RSR=0.51; RSR, the ratio of root mean square error to standard deviation of observed dataset). This indicates that the non-linear trend is more applicable for explaining the relationship between the selected predictor variables and snowfall. Relative humidity, temperature and longitude were identified as three of the most important variables influencing snowfall and snowfall prediction in both models, while elevation, aspect and latitude were of secondary importance, followed by slope and wind speed. These results will be beneficial to understand hydrological modeling and improve management and prediction of water resources in the Tianshan Mountains.
基金Supported by National Natural Science Foundation of China （Grant No. 10671051）, Natural Science Foundation of Zhejiang Province （Grant No. 103060） and Foundation of Zhejiang Educational Committee （Grant No. 20061069）
文摘Let y = y（x） be a function defined by a continued fraction. A lower bound for │A│ =│β1y1 ＋β2y2 ＋α│ is given, where y1 = y（x1）, y2 = y（x2）, x1 and x2 are positive integers, α,β and β2 are algebraic irrational numbers.
文摘This paper makes the following investigations.1. To solve the second open problem proposed by M.Morii and M.Kasahar[1];2. To prove the nonexistence of PSSP sequence with the smallest(or biggest) density;3. To find the PSSP sequence with (complementary) Hamming weight m for every positive integer m;4. To propose a generalization form of the known IYM sequence.
基金Supported by the National Natural Science Foundation of China under Grant Nos.10602041 and 10972170
文摘The method of matrix continued fraction is used to investigate stochastic resonance (SR) in the biasedsubdiffusive Smoluchowski system within linear response range.Numerical results of linear dynamic susceptibility andspectral amplification factor are presented and discussed in two-well potential and mono-well potential with differentsubdiffusion exponents.Following our observation,the introduction of a bias in the potential weakens the SR effect inthe subdiffusive system just as in the normal diffusive case.Our observation also discloses that the subdiffusion inhibitsthe low-frequency SR,but it enhances the high-frequency SR in the biased Smoluchowski system,which should reflect a'flattening' influence of the subdiffusion on the linear susceptibility.
