scholarly journals Improving Grapevine Sustainability through Multifactorial Machine Learning Application

2020 ◽  
Vol 8 ◽  
Author(s):  
Francisco José Lacueva Pérez ◽  
Sergio Ilarri Artigas ◽  
Rafael Del Hoyo ◽  
Juan José Barriuso
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Learning Models ◽  
Development Goals ◽  
Machine Learning Models ◽  
Pest Risk

Wine farms have to adapt their activities to achieve sustainable development goals. Our goal is to contribute to this adaptation by developing Machine Learning models to predict phenology and pest risk with the aim of reducing applied phytosanitary treatments.

scholarly journals A Comparative Analysis of Countries' Performance According to SDG Indicators based on Machine Learning

2021 ◽  
Author(s):  
Guilherme Souza ◽  
Julian Santos ◽  
Gabriel SantClair ◽  
Janaina Gomide ◽  
Luan Santos
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Economic Growth ◽  
Principal Component Analysis ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Principal Component ◽  
The Sustainable Development ◽  
Development Goals ◽  
Impacts Of Climate Change

The Sustainable Development Goals (SDGs) are part of a global effort to reduce the impacts of climate change, promoting social justice and economic growth. The United Nations provides a database with hundreds of indicators to track the SDGs since 2016 for a total of 302 regions. This work aims to assess which countries are in a similar situation regarding sustainable development. Principal Component Analysis was used to reduce the dimension of the dataset and k-means algorithm was used to cluster countries according to their SDGs indicators. For the years of 2016, 2017 and 2018 were obtained 11, 13 and 11 groups, respectively. This paper also analyses clusters changes throughout the years.

scholarly journals Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018 ◽  
Vol 10 (9) ◽  
pp. 1365 ◽  
Author(s):  
Jacinta Holloway ◽  
Kerrie Mengersen
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Remote Sensing Data ◽  
Statistical Machine Learning ◽  
Learning Methods ◽  
Sensing Data ◽  
Machine Learning Methods ◽  
Development Goals

Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

Accelerating Financial Inclusion in Cambodia Using Artificial Intelligence & Machine Learning

2020 ◽  
Vol 11 (12) ◽  
pp. 79-88
Author(s):  
Anil K. Makhija
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Economic Growth ◽  
Sustainable Development ◽  
United Nations ◽  
Sustainable Development Goals ◽  
Social Needs ◽  
Financial Inclusion ◽  
Development Goals ◽  
Financial Transactions

Promoting prosperity and protecting the planet at the same time requires us to end poverty and simultaneously promote economic growth and address social needs. This is also reflected in the form of 17 sustainable development goals agenda set by United Nations. Financial inclusion has been identified as an enabler for 7 out of those 17 sustainable development goals. Financial inclusion requires I\individuals and businesses to have access and ability to do financial transactions, payments, get credit and insurance.

scholarly journals Monitoring sustainable development by means of earth observation data and machine learning: a review

2020 ◽  
Vol 32 (1) ◽  
Author(s):  
Bruno Ferreira ◽  
Muriel Iten ◽  
Rui G. Silva
Keyword(s):  
Machine Learning ◽  
Sustainable Development ◽  
Sustainable Development Goals ◽  
Earth Observation ◽  
Machine Learning Techniques ◽  
Observation Data ◽  
The Sustainable Development ◽  
Learning Techniques ◽  
Development Goals ◽  
Earth Observation Data

Abstract This paper presents and explores the different Earth Observation approaches and their contribution to the achievement of United Nations Sustainable Development Goals. A review on the Sustainable Development concept and its goals is presented followed by Earth Observation approaches relevant to this field, giving special attention to the contribution of Machine Learning methods and algorithms as well as their potential and capabilities to support the achievement of Sustainable Development Goals. Overall, it is observed that Earth Observation plays a key role in monitoring the Sustainable Development Goals given its cost-effectiveness pertaining to data acquisition on all scales and information richness. Despite the success of Machine Learning upon Earth Observation data analysis, it is observed that performance is heavily dependent on the ability to extract and synthesise characteristics from data. Hence, a deeper and effective analysis of the available data is required to identify the strongest features and, hence, the key factors pertaining to Sustainable Development. Overall, this research provides a deeper understanding on the relation between Sustainable Development, Earth Observation and Machine Learning, and how these can support the Sustainable Development of countries and the means to find their correlations. In pursuing the Sustainable Development Goals, given the relevance and growing amount of data generated through Earth Observation, it is concluded that there is an increased need for new methods and techniques strongly suggesting the use of new Machine Learning techniques.

Restructuring Educational Systems and Promoting Social Justice for Young People Involved in Drug Trafficking in Brazil

2019 ◽  
Vol 227 (2) ◽  
pp. 139-143 ◽  
Author(s):  
Alex Sandro Gomes Pessoa ◽  
Linda Liebenberg ◽  
Dorothy Bottrell ◽  
Silvia Helena Koller
Keyword(s):  
Sustainable Development ◽  
Social Justice ◽  
Young People ◽  
Latin American ◽  
Drug Trafficking ◽  
Sustainable Development Goals ◽  
Educational Systems ◽  
Economic Changes ◽  
2030 Agenda ◽  
Development Goals

Abstract. Economic changes in the context of globalization have left adolescents from Latin American contexts with few opportunities to make satisfactory transitions into adulthood. Recent studies indicate that there is a protracted period between the end of schooling and entering into formal working activities. While in this “limbo,” illicit activities, such as drug trafficking may emerge as an alternative for young people to ensure their social participation. This article aims to deepen the understanding of Brazilian youth’s involvement in drug trafficking and its intersection with their schooling, work, and aspirations, connecting with Sustainable Development Goals (SDGs) 4 and 16 as proposed in the 2030 Agenda for Sustainable Development adopted by the United Nations in 2015 .

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