Developing Machine Learning‐Based Snow Depletion Curves and Analyzing Their Sensitivity over Complex Mountainous Areas

2021 ◽  
Author(s):  
Jinliang Hou ◽  
Chunlin Huang ◽  
Weijing Chen ◽  
Ying Zhang
Keyword(s):  
Machine Learning ◽  
Mountainous Areas

scholarly journals Geo-Object-Based Vegetation Mapping via Machine Learning Methods with an Intelligent Sample Collection Scheme: A Case Study of Taibai Mountain, China

2021 ◽  
Vol 13 (2) ◽  
pp. 249
Author(s):  
Tianjun Wu ◽  
Jiancheng Luo ◽  
Lijing Gao ◽  
Yingwei Sun ◽  
Wen Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Expert Knowledge ◽  
Vegetation Mapping ◽  
Geospatial Data ◽  
Vegetation Types ◽  
Mountainous Areas ◽  
Sample Collection ◽  
Object Based ◽  
Taibai Mountain ◽  
Object Level

Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.

scholarly journals Avalanches Forecasting Using Machine Learning Methods

2021 ◽  
Vol 19 (2) ◽  
pp. 92-101
Author(s):  
N. A. Radeev
Keyword(s):  
Machine Learning ◽  
Snow Cover ◽  
Predictive Power ◽  
Meteorological Data ◽  
Meteorological Conditions ◽  
Snow Avalanches ◽  
Mountainous Areas ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Machine Learning Models

The occurrence of snow avalanches is mainly influenced by meteorological conditions and the configuration of snow cover layers. Machine learning methods have predictive power and are capable of predicting new events. From the trained machine learning models, an ensemble is obtained that predicts the possibility of avalanches. The model obtained in the article uses avalanche data, meteorological data and generated data on the state of snow cover for training. This allows the resulting solution to be used in more mountainous areas than solutions using a wider range of less available data.Snow data is generated by the SNOWPACK software package.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions

Understanding Machine Learning

2009 ◽  
Author(s):  
Shai Shalev-Shwartz ◽  
Shai Ben-David
Keyword(s):  
Machine Learning
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