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What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection
Article describes how the low-cost sensor has changed the air quality monitoring paradigm with the capacity for efficient network expansion and community engagement. This study comprehensively assessed ten widely used data techniques, namely AdaBoost, Bayesian ridge, gradient tree boosting, K-nearest neighbors, Lasso, multivariable linear regression, neural network, random forest, ridge regression, and support vector machine.
Date:
June 27, 2022
Creator:
Liang, Lu & Daniels, Jacob
Object Type:
Article
System:
The UNT Digital Library
Extracting Information on Rocky Desertification from Satellite Images: A Comparative Study
This article uses three common open-access satellite image datasets (Sentinel-2B, Landsat-8, and Gaofen-6) for extracting information on rocky desertification in a typical karst region (Guangnan County, Yunnan) of southwest China, using three machine-learning algorithms implemented in the Python programming language: random forest (RF), bagged decision tree (BDT), and extremely randomized trees (ERT).
Date:
June 26, 2021
Creator:
Pu, Junwei; Zhao, Xiaoqing; Dong, Pinliang; Wang, Qian & Yue, Qifa
Object Type:
Article
System:
The UNT Digital Library
Assessment of personal exposure to particulate air pollution: the first result of City Health Outlook (CHO) project
First paper of a series on the City Health Outlook (CHO) project, which aims to establish multi-scale, long-lasting, real-time urban environment and health monitoring networks. This paper is targeted at illustrating the characteristics of the participants and examining the effects of different covariates on personal exposure at various air pollution exposure levels.
Date:
June 7, 2019
Creator:
Liang, Lu; Gong, Peng; Cong, Na; Li, Zhichao; Zhao, Yu & Chen, Ying
Object Type:
Article
System:
The UNT Digital Library
An assessment of MODIS Collection 5 global land cover product for biological conservation studies
This paper identifies the uncertain areas and figures out the problematic land cover types in the MODIS Collection 5 global land cover dataset without ground validation samples.
Date:
June 18, 2010
Creator:
Liang, Lu & Gong, Peng
Object Type:
Paper
System:
The UNT Digital Library