Machine Learning in Action: Outcome Prediction for Sustainable Development

Dear Colleagues,

Machine learning techniques are being increasingly applied to solve challenging issues for companies, industries, public sectors, and the entire society. Predictive analytics and prescriptive analytics solutions provide the means to predict the best course of action to take in a proposed strategy. In addition, while such evolutions occur, we also identify a growing search for solutions focused on currently alarming issues (e.g., waste generation, social inequality, water pollution, greenhouse emissions), aiming at innovative actions that contribute to sustainable development. A high potential is identified regarding the use of machine learning for sustainable development purposes, with a focus on providing advancement and support for sustainable development from the ability to predict outcomes.

1) Introduction, including scientific background and highlighting the importance of this research area.

Therefore, diverse machine learning techniques, such as classification (e.g., support vector machines, decision trees, Bayesian networks, artificial neural networks); regression (e.g., linear regression, logistic regression); clustering (e.g., k-means, Gaussian mixture) and decision making (e.g., q-learning, td learning) can contribute to leverage the current challenges inherent to sustainability. In this context, the applicability of machine learning in sustainable development is also anchored by the adoption of different technologies identified in studies related to digital transformation and industry 4.0. Examples include the adoption of big data and data science to provide meaningful and valuable datasets and insights to machine learning models, the adoption of cloud computing-based capabilities providing adequate demand for processing and analysis of machine learning applications, as well as the adoption of sensors from the IoT context, contributing to data acquisition in solutions such as energy consumption analysis, urban traffic management, assessment of water pollution, and carbon footprint management.

2) Aim of the Special Issue and how the subject relates to the journal scope.

Therefore, the purpose of this Special Issue is to present a state-of-the-art reference regarding current solutions and discussions regarding the multidisciplinary approach aimed at sustainable development based on the ability to predict the outcomes from machine learning techniques. For this, we encourage the publication of high-quality research papers that incorporate in their studies the adoption of machine learning techniques aimed at sustainable development in the most different areas that can benefit from these studies.

3) Suggest themes.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• ML techniques applied to social, environmental, and economic aspects;
• Applicability of ML focused on a specific sustainable development goal (SDG);
• Proposal of ML pipelines addressed to sustainable development;
• Innovative ML models applied to circular economy strategies;
• Outcome prediction to reduce waste generation;
• ML strategies applied to predict and reduce energy consumption;
• Applicability of ML models for renewable energy strategies;
• Comparison of ML models applied to sustainable development;
• ML techniques applied to predict the effects of gender inequality;
• ML applications towards low carbon emissions;
• Deployment of ML models for sustainable indicators.

We look forward to receiving your contributions.

Prof. Dr. Tereza Cristina Melo De Brito Carvalho
Prof. Dr. Rosangela De Fátima Pereira Marquesone
Guest Editors

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• machine learning
• supervised learning
• unsupervised learning
• reinforcement learning
• data-driven sustainability
• sustainability
• circular economy
• sustainable development
• data science
• big data