Stream network and stream segment temperature models software

10.3133/96233 ◽  
2010 ◽  
Keyword(s):  
Stream Network ◽  
Stream Segment

scholarly journals Geomorphology and Landslide-Prone Area in Cisolok District, Sukabumi Regency

2019 ◽  
Vol 125 ◽  
pp. 01005 ◽  
Author(s):  
Mochamad Seandy Alfarabi ◽  
Supriatna ◽  
Masita Dwi Mandini Manessa ◽  
Andry Rustanto ◽  
Yoanna Ristya
Keyword(s):  
Land Use ◽  
Road Network ◽  
Geological Structure ◽  
Overlay Analysis ◽  
Stream Network ◽  
Prone Area ◽  
Steep Topography ◽  
Physical Variables ◽  
Natural Characteristics ◽  
Very High

Sukabumi District located in Southern West Java known as a region that has diverse natural characteristics, however, it is vulnerable to disasters, especially landslides. Moreover, this study focuses on Cisolok District because this region always occurred landslides every year due to topography aspect. The aim of this study is to analyze the influence of geomorphology to landslide-prone area in Cisolok District to reduce landslides. This study used overlay analysis for geomorphology mapping, while the Frequency Ratio (FR) method used for landslide-prone area mapping. Several physical variables used in this study such as slope, elevation, lithology, geological structure, road network, stream network, land use, soil type, rainfall, and landslide location. The result shows that the study areas have diverse geomorphology units dominated by volcanic slope with steep topography. While landslide-prone area consist of four classes : namely 17,03% low, 62,05% medium, 14,4% high, and 6,51% very high. Variety of landslide vulnerability in study area influenced by terrain form, land genesis, and geomorphic process.

scholarly journals Explicit-implicit dual stream network for image quality assessment

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Guangyi Yang ◽  
Xingyu Ding ◽  
Tian Huang ◽  
Kun Cheng ◽  
Weizheng Jin
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Frequency Domain ◽  
Image Quality Assessment ◽  
Feature Fusion ◽  
Learning Model ◽  
Stream Network ◽  
Perception System ◽  
Deep Learning Model

Abstract Communications industry has remarkably changed with the development of fifth-generation cellular networks. Image, as an indispensable component of communication, has attracted wide attention. Thus, finding a suitable approach to assess image quality is important. Therefore, we propose a deep learning model for image quality assessment (IQA) based on explicit-implicit dual stream network. We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features. Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features. On this basis, the number of network layers of VGGNet is reduced by adding the parallel wavelet kurtosis value frequency domain features. Thus, the training parameters and the sample requirements decline. We verified, by cross-validation of different databases, that the wavelet kurtosis feature fusion method based on deep learning has a more complete feature extraction effect and a better generalisation ability. Thus, the method can simulate the human visual perception system better, and subjective feelings become closer to the human eye. The source code about the proposed EI-IQA model is available on github https://github.com/jacob6/EI-IQA.

scholarly journals Combining hydrologic simulations and stream-network models to reveal flow-ecology relationships in a large Alpine catchment

2020 ◽  
Author(s):  
Stefano Larsen ◽  
Bruno Majone ◽  
Patrick Zulian ◽  
Elisa Stella ◽  
Alberto Bellin ◽  
...  
Keyword(s):  
Network Models ◽  
Stream Network
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