scholarly journals Synchronous-Asynchronous Encounter Probability Analysis of High-Low Runoff for Jinsha River, China, using Copulas*

2018 ◽  
Vol 246 ◽  
pp. 01094 ◽  
Author(s):  
CHEN Jing ◽  
GU Shixiang ◽  
ZHANG Tianli

Synchronous-asynchronous encounter probability analysis of high-low runoff, which requires a description of the probabilistic properties of hydrological variables, is important in regional water resources management. This study aims to investigate this encounter probability for Jinsha River and its tributary Yalong River in southwest China. A bivariate distribution is used to model the runoff variables of the two rivers based on Copula theory. The Copula is a function that links the univariate marginal distributions to form the bivariate distribution. The bivariate distribution is then employed to determine joint and conditional probabilities. The study results indicate the encounter probability of mainstream runoff and tributary runoff in different periods, also illustrate the mainstream runoff distribution under the condition of knowing the tributary runoff distribution.

Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.


2018 ◽  
Vol 34 (3) ◽  
pp. 774-776 ◽  
Author(s):  
K. Shao ◽  
Y. F. Que ◽  
M. H. Xiong ◽  
W. T. Li ◽  
D. Yu ◽  
...  

Author(s):  
Yanwen Xu ◽  
Pingfeng Wang

Abstract Analysis of rare failure events accurately is often challenging with an affordable computational cost in many engineering applications, and this is especially true for problems with high dimensional system inputs. The extremely low probabilities of occurrences for those rare events often lead to large probability estimation errors and low computational efficiency. Thus, it is vital to develop advanced probability analysis methods that are capable of providing robust estimations of rare event probabilities with narrow confidence bounds. Generally, confidence intervals of an estimator can be established based on the central limit theorem, but one of the critical obstacles is the low computational efficiency, since the widely used Monte Carlo method often requires a large number of simulation samples to derive a reasonably narrow confidence interval. This paper develops a new probability analysis approach that can be used to derive the estimates of rare event probabilities efficiently with narrow estimation bounds simultaneously for high dimensional problems. The asymptotic behaviors of the developed estimator has also been proved theoretically without imposing strong assumptions. Further, an asymptotic confidence interval is established for the developed estimator. The presented study offers important insights into the robust estimations of the probability of occurrences for rare events. The accuracy and computational efficiency of the developed technique is assessed with numerical and engineering case studies. Case study results have demonstrated that narrow bounds can be built efficiently using the developed approach, and the true values have always been located within the estimation bounds, indicating that good estimation accuracy along with a significantly improved efficiency.


2010 ◽  
Vol 53 (12) ◽  
pp. 3331-3340 ◽  
Author(s):  
SiYi Hu ◽  
ZongZhi Wang ◽  
YinTang Wang ◽  
HaoYun Wu ◽  
JuLiang Jin ◽  
...  

2017 ◽  
Vol 29 (4) ◽  
pp. 991-999
Author(s):  
QIN Yu ◽  
◽  
YANG Boxiao ◽  
LI Zhe ◽  
HE Bin ◽  
...  

2020 ◽  
Author(s):  
Yaomei Qiao ◽  
Jian Liu ◽  
Xun Gong

Abstract Background: The eastern Sino-Himalayan region of southwest China, as one of the world’s biodiversity hotspots, has undergone dramatic geomorphological and climatic changes during the Late Tertiary/Quaternary. The dry-hot valleys in southwest China is overlapping with the Hengduan Mountains. However, when this endemism was assembled in dry-hot valleys and how the climatic oscillations and/or tectonic movements influence their phylogeographical patterns remained largely unknown. Himalrandia lichiangensis is a shrub of Rubiaceae, endemically distributes in dry-hot valleys of southwest China. By integrating evidences from phylogeography, population dynamic and ecological niche modelling, we are aiming to trace the evolutionary history and explain the origin of biodiversity and endemism in the dry-hot valleys of southwest China. Results: Based on sequencing four chloroplastic non-coding regions (psbM-trnD, trnD-trnT, atpB-rbcL and accD-psaI) and two single-low copy nuclear genes CAMX (Calmodulin) and ITS of 423 individuals from 23 populations, we found high genetic variation mainly existed between the populations (cpDNA: 89.80%, ITS: 84.55%, CAMX: 95.68%). Haplotypes in different river basins showed significant phylogeographical structure. The geographical distribution of haplotypes indicated that there is a high degree of genetic differentiation and this differentiation is associated with altitude discrepancy. BARRIER analysis detects a strong geographic barrier between the Nanpan River and Jinsha River. The MaxEnt result shows that the suitable distribution area was the largest in the LGM. The future climate warming will lead to the niche expansion for H. lichiangensis but will also cause fragmented distribution.Conclusions: Our study highlighted the importance of altitude in explaining the genetic differentiation. The current phylogeographical pattern of H. lichiangensis may be shaped by long-term geographical isolation resulted from the uplifting of Himalaya, which gives rise to the barriers from the Hengduan Mountain or multiple river systems, and the vertical altitude discrepancies that both limited gene flow among regions. The Middle Jinsha River valley is most likely to be a main refuge for H. lichiangensis till now, and the glaciation retreat may account for the high endemism of plants in the dry-hot valleys of southwest China.


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