scholarly journals A Snow Distribution Model Based on Snowfall and Snow Drifting Simulations in Mountain Area

2018 ◽  
Author(s):  
G. Li ◽  
Z. S. Wang ◽  
N. Huang
Keyword(s):  
Distribution Model ◽  
Mountain Area ◽  
Model Based ◽  
Snow Distribution ◽  
Snow Drifting

Research on 3D Temperature Distribution Model based on Affine Projection and Feature Registration

2018 ◽  
Author(s):  
HongYu Wang ◽  
Chengdong Wu ◽  
Tong Jia ◽  
Dongxue Li ◽  
Yang Jiang ◽  
...  
Keyword(s):  
Temperature Distribution ◽  
Distribution Model ◽  
Affine Projection ◽  
Model Based ◽  
Feature Registration

Exploring the use of gpr and satellite-based snow data for snowmelt runoff predictions

2021 ◽  
Author(s):  
Ilaria Clemenzi ◽  
David Gustafsson ◽  
Jie Zhang ◽  
Björn Norell ◽  
Wolf Marchand ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Distribution ◽  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Distribution Model ◽  
Accurate Estimation ◽  
Snowmelt Runoff ◽  
Snow Water ◽  
Snow Distribution ◽  
The Impact

<p>Snow in the mountains is the result of the interplay between meteorological conditions, e.g., precipitation, wind and solar radiation, and landscape features, e.g., vegetation and topography. For this reason, it is highly variable in time and space. It represents an important water storage for several sectors of the society including tourism, ecology and hydropower. The estimation of the amount of snow stored in winter and available in the form of snowmelt runoff can be strategic for their sustainability. In the hydropower sector, for example, the occurrence of higher snow and snowmelt runoff volumes at the end of the spring and in the early summer compared to the estimated one can substantially impact reservoir regulation with energy and economical losses. An accurate estimation of the snow volumes and their spatial and temporal distribution is thus essential for spring flood runoff prediction. Despite the increasing effort in the development of new acquisition techniques, the availability of extensive and representative snow and density measurements for snow water equivalent estimations is still limited. Hydrological models in combination with data assimilation of ground or remote sensing observations is a way to overcome these limitations. However, the impact of using different types of snow observations on snowmelt runoff predictions is, little understood. In this study we investigated the potential of assimilating in situ and remote sensing snow observations to improve snow water equivalent estimates and snowmelt runoff predictions. We modelled the seasonal snow water equivalent distribution in the Lake Överuman catchment, Northern Sweden, which is used for hydropower production. Simulations were performed using the semi-distributed hydrological model HYPE for the snow seasons 2017-2020. For this purpose, a snowfall distribution model based on wind-shelter factors was included to represent snow spatial distribution within model units. The units consist of 2.5x2.5 km<sup>2</sup> grid cells, which were further divided into hydrological response units based on elevation, vegetation and aspect. The impact on the estimation of the total catchment mean snow water equivalent and snowmelt runoff volume were evaluated using for data assimilation, gpr-based snow water equivalent data acquired along survey lines in the catchment in the early spring of the four years, snow water equivalent data obtained by a machine learning algorithm and satellite-based fractional snow cover data. Results show that the wind-shelter based snow distribution model was able to represent a similar spatial distribution as the gpr survey lines, when assessed on the catchment level. Deviations in the model performance within and between specific gpr survey lines indicate issues with the spatial distribution of input precipitation, and/or need to include explicit representation of snow drift between model units. The explicit snow distribution model also improved runoff simulations, and the ability of the model to improve forecast through data assimilation.</p>

scholarly journals SIS dynamic network model based on product semantics from the perspective of infectious disease dynamics

2021 ◽  
Vol 235 ◽  
pp. 03035
Author(s):  
jiaojiao Lv ◽  
yingsi Zhao
Keyword(s):  
Network Model ◽  
Purchase Intention ◽  
Recommendation System ◽  
Optimal Algorithm ◽  
Dynamic Network ◽  
Distribution Model ◽  
Recommendation Algorithm ◽  
Model Based ◽  
Dynamic Network Model ◽  
Recommendation Algorithms

Recommendation system is unable to achive the optimal algorithm, recommendation system precision problem into bottleneck. Based on the perspective of product marketing, paper takes the inherent attribute as the classification standard and focuses on the core problem of “matching of product classification and recommendation algorithm of users’ purchase demand”. Three hypotheses are proposed: (1) inherent attributes of the product directly affect user demand; (2) classified product is suitable for different recommendation algorithms; (3) recommendation algorithm integration can achieve personalized customization. Based on empirical research on the relationship between characteristics of recommendation information (independent variable) and purchase intention (dependent variable), it is concluded that predictability and difference of recommendation information are not fully perceived and stimulation is insufficient. Therefore, SIS dynamic network model based on the distribution model of SIS virus is constructed. It discusses the spreading path of recommendation information and “infection” situation of consumers to enhance accurate matching of recommendation system.

Performance of a species distribution model based on presence/absence for various population sizes in the amphidromous fish species Sicyopterus japonicus

2013 ◽  
Vol 61 (1) ◽  
pp. 90-96 ◽  
Author(s):  
Norio Onikura ◽  
Ryutei Inui ◽  
Shin Oikawa ◽  
Tomonori Kawamoto
Keyword(s):  
Fish Species ◽  
Species Distribution ◽  
Species Distribution Model ◽  
Distribution Model ◽  
Model Based ◽  
Population Sizes

Research on the Pre-distribution Model Based on Seesaw Model

2017 ◽  
pp. 201-213 ◽  
Author(s):  
Mingshan Xie ◽  
Yanfang Deng ◽  
Yong Bai ◽  
Mengxing Huang ◽  
Zhuhua Hu
Keyword(s):  
Distribution Model ◽  
Seesaw Model ◽  
Model Based

scholarly journals DGA and Weibull Distribution Model-based Transformer Fault Early Warning

2019 ◽  
Vol 569 ◽  
pp. 032072
Author(s):  
Pei Yang ◽  
Sen Pan ◽  
Jing Jiang ◽  
Wei Rao ◽  
Junfeng Qiao
Keyword(s):  
Weibull Distribution ◽  
Early Warning ◽  
Distribution Model ◽  
Model Based ◽  
Transformer Fault

Reliability Analysis of Vehicle Equipment Based on Bayesian Method

2012 ◽  
Vol 253-255 ◽  
pp. 2091-2096
Author(s):  
Yan Feng Tang ◽  
Hui Mei Li ◽  
Xiang Kai Liu ◽  
Shao Qing Liu
Keyword(s):  
Parameter Estimation ◽  
Data Processing ◽  
Reliability Analysis ◽  
Bayesian Method ◽  
Distribution Model ◽  
Optimal Distribution ◽  
Model Based ◽  
Selection Of

Bayesian method was introduced and leaded into the vehicle fault data processing. The parameter estimation and the selection of the optimal distribution model based on Bayesian method were studied, and an example was given. The references are provided for the application of Bayesian method in the large complicated systems, such as vehicle equipments.

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