Length-weight relationships of eleven fish species from the middle reaches of Jinsha River, southwest China

2015 ◽  
Vol 31 (3) ◽  
pp. 549-551 ◽  
Author(s):  
L. Pan ◽  
W. T. Li ◽  
J. J. Xie ◽  
Y. F. Que ◽  
N. Zhao ◽  
...  
Keyword(s):  
Fish Species ◽  
Southwest China ◽  
Jinsha River

Length-weight and length-length relationships of thirteen fish species from the lower Jinsha River, southwest China

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

scholarly journals The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

2021 ◽  
Author(s):  
Leijin Long ◽  
Feng He ◽  
Hongjiang Liu
Keyword(s):  
Remote Sensing ◽  
Southwest China ◽  
Influence Factors ◽  
Classification Error ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Remote Sensing Images ◽  
Jinsha River ◽  
Detection Model ◽  
High Level

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.

Length-weight relationships of five fish species from the Nujiang River (Salween basin), southwest China

2018 ◽  
Vol 34 (3) ◽  
pp. 698-699
Author(s):  
F. J. Huang ◽  
M. D. Liu ◽  
L. X. Yu ◽  
S. P. Liu
Keyword(s):  
Fish Species ◽  
Southwest China ◽  
Nujiang River

Length-weight relationships of eight fish species from the upper reaches of the Yangtze River, southwest China

2018 ◽  
Vol 34 (4) ◽  
pp. 1068-1070
Author(s):  
R. Yi ◽  
G. M. Tan ◽  
Z. Yang ◽  
Q. Han ◽  
J. Chang
Keyword(s):  
Fish Species ◽  
Yangtze River ◽  
Southwest China ◽  
The Yangtze River

Length-weight relationships of 6 fish species from the lower reaches of the YarlungZangbo River, Southwest China

2018 ◽  
Vol 34 (4) ◽  
pp. 1062-1064
Author(s):  
L. Li ◽  
B. Ma ◽  
J. Gong ◽  
Z. Chen ◽  
L. Cheng ◽  
...  
Keyword(s):  
Fish Species ◽  
Southwest China

scholarly journals CO2 and CH4 flux across water-air interface in summer in the downstream of Jinsha River, Southwest China

2017 ◽  
Vol 29 (4) ◽  
pp. 991-999
Author(s):  
QIN Yu ◽  
◽  
YANG Boxiao ◽  
LI Zhe ◽  
HE Bin ◽  
...  
Keyword(s):  
Southwest China ◽  
Jinsha River ◽  
Air Interface

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
Keyword(s):  
Southwest China ◽  
Bivariate Distribution ◽  
Probability Analysis ◽  
Conditional Probabilities ◽  
Jinsha River ◽  
Copula Theory ◽  
Resources Management ◽  
Encounter Probability ◽  
Study Results ◽  
Regional Water

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.

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