Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Browser

► Journal Browser

Neural Networks and Data Analytics for Sustainable Development

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 10179

Share This Special Issue

Special Issue Editors

Prof. Dr. David Dominguez

E-Mail Website
Guest Editor

Higher Polytechnic School, University Autonoma of Madrid, 28049 Madrid, Spain
Interests: neural networks; sustainability; information theory; metric topology; stochastic dynamics; statistical mechanics; machine learning; big data
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Mario Gonzalez

E-Mail Website
Guest Editor

Intelligent & Interactive Systems Lab (SI2 Lab), FICA, Universidad de Las Américas, Quito, Ecuador
Interests: machine learning; sustainability; graph for neural networks

Prof. Dr. Sara Cuenda

E-Mail Website
Guest Editor

Dpto. Análisis Económico Economía Cuantitativa, Facultad de Ciencias Economicas y Empresariales, University Autonoma of Madrid, Madrid 28049, Spain
Interests: ecology; complex systems; sustainability; network theory

Special Issue Information

Dear Colleagues,

Neural networks (NNs) are biologically inspired computational tools which are able to learn, classify or forecast data. Since an NN is robust against a noisy environment and efficient enough to solve complex nonlinear problems, it has been applied to a broad range of sciences and technologies.

Thus, we expect it to also be suitable for application to achieve an equilibrium between targets of different disciplines, such as sociology, economy or ecology, and to enhance our understanding of this complex sustainability problem from a quantitative viewpoint.

Several NNs have been proposed, among them are feed-forward and recurrent NN, with supervised and unsupervised learning rules, with short or long range connections, some of them able to store and retrieve patterns, others to classify examples, or to predict time series. LSTM or deep learning has largely been used recently. Together with other machine learning models, such as the Bayesian Networks or the Genetic Algorithms, these techniques are useful to analyze massive data with complex behavior, the so called Big Data.

The present special issue aims to contribute to the theoretical and empirical improvement of our knowledge of climate change, environmental conservation, waste management, and other tasks of a sustainable world, by using NN.

a) Focus:

The present issue aims to produce an interplay between theoretical and empirical knowledge on neural networks and its applications for sustainable development. Interdisciplinary cooperation between researchers from the environmental sciences, economy, sociology, engineers or physicists is expected, as well as their exchanging of experiences.

b) Scope

We expect papers on any of the following subjects:

- NN applied to forecast deforestation.

- NN applied to the waste recycling.

- NN applied to the touristic impact problem.

- NN applications to the migration flows.

- Big Data for water and air pollution.

- Big Data for local vs global development.

- Big Data for renewable energy.

- Big Data for rural vs urban areas.

- Sustainability inspired NN models.

- Related NN theory and applications.

Supplement:

This issue should be related to publications found in journals from the Web of Science

categories, among others:

Computer Sciences – Artificial Inteligence, & Interdisciplinary Applications.

Ecology, Economics,

Engineering – Environmental, & Multidisciplinary

Green & Sustainable Science & Technology

Mathematics – Interdisciplinary Applications

Multidisciplinary Sciences

Physics – Multidisciplinary

Social Sciences – Interdisciplinary, & Mathematical Methods

Statistics & Probability

Prof. Dr. David Dominguez
Prof. Dr. Mario Gonzalez
Prof. Dr. Sara Cuenda
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Neural Networks
Sustainability
Big Data
Local vs Global development
Renewable resources
Climate change

Published Papers (2 papers)

Download All Papers

Research

24 pages, 1681 KiB

Open AccessArticle

Solid Waste Analysis Using Open-Access Socio-Economic Data

by Jürgen Dunkel, David Dominguez, Óscar G. Borzdynski and Ángel Sánchez

Sustainability 2022, 14(3), 1233; https://0-doi-org.brum.beds.ac.uk/10.3390/su14031233 - 21 Jan 2022

Cited by 4 | Viewed by 3019

Abstract

Nowadays, problems related with solid waste management become a challenge for most countries due to the rising generation of waste, related environmental issues, and associated costs of produced wastes. Effective waste management systems at different geographic levels require accurate forecasting of future waste generation. In this work, we investigate how open-access data, such as provided from the Organisation for Economic Co-operation and Development (OECD), can be used for the analysis of waste data. The main idea of this study is finding the links between socio-economic and demographic variables that determine the amounts of types of solid wastes produced by countries. This would make it possible to accurately predict at the country level the waste production and determine the requirements for the development of effective waste management strategies. In particular, we use several machine learning data regression (Support Vector, Gradient Boosting, and Random Forest) and clustering models (k-means) to respectively predict waste production for OECD countries along years and also to perform clustering among these countries according to similar characteristics. The main contributions of our work are: (1) waste analysis at the OECD country-level to compare and cluster countries according to similar waste features predicted; (2) the detection of most relevant features for prediction models; and (3) the comparison between several regression models with respect to accuracy in predictions. Coefficient of determination (R

^{2}

), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), respectively, are used as indices of the efficiency of the developed models. Our experiments have shown that some data pre-processings on the OECD data are an essential stage required in the analysis; that Random Forest Regressor (RFR) produced the best prediction results over the dataset; and that these results are highly influenced by the quality of available socio-economic data. In particular, the RFR model exhibited the highest accuracy in predictions for most waste types. For example, for “municipal” waste, it produced, respectively, R

^{2}

= 1 and

M A P E = 4.31

global error values for the test set; and for “household” waste, it, respectively, produced R

^{2}

= 1 and

M A P E = 3.03

. Our results indicate that the considered models (and specially RFR) all are effective in predicting the amount of produced wastes derived from input data for the considered countries. Full article

(This article belongs to the Special Issue Neural Networks and Data Analytics for Sustainable Development)

► Show Figures

Figure 1

18 pages, 2828 KiB

Open AccessArticle

Forecasting Amazon Rain-Forest Deforestation Using a Hybrid Machine Learning Model

by David Dominguez, Luis de Juan del Villar, Odette Pantoja and Mario González-Rodríguez

Sustainability 2022, 14(2), 691; https://0-doi-org.brum.beds.ac.uk/10.3390/su14020691 - 09 Jan 2022

Cited by 8 | Viewed by 5809

Abstract

The present work aims to carry out an analysis of the Amazon rain-forest deforestation, which can be analyzed from actual data and predicted by means of artificial intelligence algorithms. A hybrid machine learning model was implemented, using a dataset consisting of 760 Brazilian Amazon municipalities, with static data, namely geographical, forest, and watershed, among others, together with a time series data of annual deforestation area for the last 20 years (1999–2019). The designed learning model combines dense neural networks for the static variables and a recurrent Long Short Term Memory neural network for the temporal data. Many iterations were performed on augmented data, testing different configurations of the regression model, for adjusting the model hyper-parameters, and generating a battery of tests to obtain the optimal model, achieving a R-squared score of 87.82%. The final regression model predicts the increase in annual deforestation area (square kilometers), for a decade, from 2020 to 2030, predicting that deforestation will reach 1 million square kilometers by 2030, accounting for around 15% compared with the present 1%, of the between 5.5 and 6.7 millions of square kilometers of the rain-forest. The obtained results will help to understand the impact of man’s footprint on the Amazon rain-forest. Full article

(This article belongs to the Special Issue Neural Networks and Data Analytics for Sustainable Development)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Neural Networks and Data Analytics for Sustainable Development

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (2 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI