scholarly journals A machine-learning approach to human footprint index estimation with applications to sustainable development

2021 ◽  
Author(s):  
Patrick W Keys ◽  
Elizabeth A Barnes ◽  
Neil H Carter
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Learning Approach ◽  
Human Footprint ◽  
Machine Learning Approach ◽  
Index Estimation

scholarly journals A machine-learning approach to human footprint index estimation with applications to sustainable development

2020 ◽  
Author(s):  
Patrick W. Keys ◽  
Elizabeth A. Barnes ◽  
Neil H. Carter
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Monitoring And Evaluation ◽  
Global Scale ◽  
Sustainable Development Goal ◽  
Conservation Policies ◽  
Human Footprint ◽  
Machine Learning Approach ◽  
Index Estimation ◽  
Ongoing Monitoring

AbstractThe human footprint index is an extensively used tool for interpreting the accelerating pressure of humanity on Earth. Up to now, the process of creating the human footprint index has required significant data and modeling, and updated versions of the index often lag the present day by many years. Here we introduce a near-present, global-scale machine learning-based human footprint index (ml-HFI) which is capable of routine update using satellite imagery alone. We present the most up-to-date map of the human footprint index, and document changes in human pressure during the past 20 years (2000 to 2019). Moreover, we demonstrate its utility as a monitoring tool for the United Nations Sustainable Development Goal 15 (SDG15), “Life on Land”, which aims to foster sustainable development while conserving biodiversity. We identify countries that are making progress toward SDG15 while also experiencing increases in their ml-HFI. We briefly examine a subset of these in the context of conservation policies that may or may not enable continued progress toward SDG15. This has immediate policy relevance, since the majority of countries globally are not on track to achieve Goal 15 by the declared deadline of 2030. Moving forward, the ml-HFI may be used for ongoing monitoring and evaluation support toward the twin goals of fostering a thriving society and global Earth system.

scholarly journals Machine Learning Approach to Quantity Management for Long-Term Sustainable Development of Dockless Public Bike: Case of Shenzhen in China

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qingfeng Zhou ◽  
Chun Janice Wong ◽  
Xian Su
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Clustering Algorithms ◽  
Utilization Efficiency ◽  
Impact Factors ◽  
Classification Algorithms ◽  
Learning Approach ◽  
High Efficient ◽  
Machine Learning Approach

Since the number of bicycles is critical to the sustainable development of dockless PBS, this research practiced the introduction of a machine learning approach to quantity management using OFO bike operation data in Shenzhen. First, two clustering algorithms were used to identify the bicycle gathering area, and the available bike number and coefficient of available bike number variation were analyzed in each bicycle gathering area’s type. Second, five classification algorithms were compared in the accuracy of distinguishing the type of bicycle gathering areas using 25 impact factors. Finally, the application of the knowledge gained from the existing dockless bicycle operation data to guide the number planning and management of public bicycles was explored. We found the following. (1) There were 492 OFO bicycle gathering areas that can be divided into four types: high inefficient, normal inefficient, high efficient, and normal efficient. The high inefficient and normal inefficient areas gathered about 110,000 bicycles with low usage. (2) More types of bicycle gathering area will affect the accuracy of the classification algorithm. The random forest classification had the best performance in identifying bicycle gathering area types in five classification algorithms with an accuracy of more than 75%. (3) There were obvious differences in the characteristics of 25 impact factors in four types of bicycle gathering areas. It is feasible to use these factors to predict area type to optimize the number of available bicycles, reduce operating costs, and improve utilization efficiency. This work helps operators and government understand the characteristics of dockless PBS and contributes to promoting long-term sustainable development of the system through a machine learning approach.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach
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