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The Usage of Information Tools and MCDA for the Application...
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The Usage of Information Tools and MCDA for the Application of Environmental Policy and Sustainable Development

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (20 September 2021) | Viewed by 16207

Special Issue Editors

Dr. Konstantinos Ioannou

E-Mail Website
Guest Editor
Forest Research Institute, Hellenic Agricultural Organization “DEMETER”, Thessaloniki, Greece
Interests: forest informatics; decision support systems; expert systems; spatial planning of RES multicriteria decision analysis; sensor networks; artificial neural networks and computing
Special Issues, Collections and Topics in MDPI journals
Dr. Dimitris Karampatzakis

E-Mail Website
Assistant Guest Editor
Department of Computer Science, International Hellenic University, 65403 Kavala, Greece
Interests: embedded systems; systems of systems modeling; lorawan; physical internet; industrial maintenance; VLSI computer systems

Special Issue Information

Dear colleagues,

In recent decades, it has become more evident than ever before that a great need exists for the application of methodologies for the protection and restoration of the environment. Many environmental problems have been identified to date, including deforestation, lowered biodiversity, over-exploitation of natural resources, etc.

Additionally, and due to the fact that environmental pressure, especially from countries that are not part of the Organization for Economic Co-operation and Development (OECD), is continuously increasing, there is a constant need for the application of sustainable management methods and the development of methods for the mitigation of environmental degradation.

Modern information tools and multicriteria decision analysis (MCDA) can easily be modified and used in order to determine the factors that cause environmental problems and propose solutions for restoration and sustainable development.

The objective of this Special Issue is to capture the latest advances regarding the use of information systems and MCDA analysis for the application of environmental policy and sustainable development.

Topics of interest for publication in this Special Issue include but are not limited to the following:

  • Forecasting environmental conditions using artificial neural networks, fuzzy logic, etc.;
  • Land use change prediction and spatial analysis;
  • Quantifying qualitative data;
  • MCDA usage for sustainable development;
  • Internet of Things applications for environmental monitoring;
  • Identification of environmental problems using big data analysis;
  • Image recognition for the identification of environmental problems;
  • Improvement on the usage of renewable energy sources (RES);
  • Application of MCDA methods for allocating RES investments.

Dr. Konstantinos Ioannou
Dr. Dimitris Karampatzakis
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. Information is an international peer-reviewed open access monthly 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 1600 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.

References

  1. Koutroumanidis, Theodoros; Ioannou, Konstantinos; Arabatzis, Garyfallos;"Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA–ANN model",Energy Policy,37,9,3627-3634,2009.
  2. Ioannou, K; Arabatzis, G; Lefakis, P; Predicting the prices of forest energy resources with the use of Artificial Neural networks (ANNs). The case of conifer fuel wood in Greece,Journal of Environmental Protection and Ecology,10,3,678-694,2009.
  3. Ioannou, Konstantinos; Lefakis, Panagiotis; Arabatzis, Garyfallos; Development of a decision support system for the study of an area after the occurrence of forest fire,International Journal of Sustainable Society,3,1,5-32,2011.
  4. Myronidis, Dimitrios; Stathis, Dimitrios; Ioannou, Konstantinos; Fotakis, Dimitrios; An integration of statistics temporal methods to track the effect of drought in a shallow Mediterranean Lake,Water Resources Management,26,15,4587-4605,2012.
  5. Emmanouloudis, D; Myronidis, D; Ioannou, K; Assessment of flood risk in Thasos Island with the combined use of multicriteria analysis AHP and geographical information system,Innov Appl Info Agric Environ, 2, 103-115,2008.
  6. Ioannou, Konstantinos; Tsantopoulos, Georgios; Arabatzis, Garyfallos; Andreopoulou, Zacharoula; Zafeiriou, Eleni; "A spatial decision support system framework for the evaluation of biomass energy production locations: Case study in the regional unit of drama, Greece" Sustainability,10,2,531,2018.
  7. Konstantinos, Ioannou; Georgios, Tsantopoulos; Garyfalos, Arabatzis; ,"A Decision Support System methodology for selecting wind farm installation locations using AHP and TOPSIS: Case study in Eastern Macedonia and Thrace region, Greece",Energy Policy,132,,232-246,2019.
  8. Pratibha Rani, Arunodaya Raj Mishra, Kamal Raj Pardasani, Abbas Mardani, Huchang Liao, Dalia Streimikiene, “A novel VIKOR approach based on entropy and divergence measures of Pythagorean fuzzy sets to evaluate renewable energy technologies in India”, Journal of Cleaner Production, Volume 238, 2019, 117936, ISSN 0959-6526.
  9. Theocharis Tsoutsos, Maria Drandaki, Niki Frantzeskaki, Eleftherios Iosifidis, Ioannis Kiosses,“Sustainable energy planning by using multi-criteria analysis application in the island of Crete”, Energy Policy, Volume 37, Issue 5, 2009. Pages 1587-1600,ISSN 0301-4215,
  10. Savvas Theodorou, Georgios Florides, Savvas Tassou, “The use of multiple criteria decision making methodologies for the promotion of RES through funding schemes in Cyprus, A review”, Energy Policy, Volume 38, Issue 12, 2010, Pages 7783-7792, ISSN 0301-4215.

Keywords

  • Multicriteria decision analysis
  • Analytical hierarchy process (AHP)
  • Technique for order of preference by similarity to ideal solution (TOPSIS)
  • VIKOR
  • PROMETHEE
  • Data envelopment analysis (DEA)
  • Artificial neural networks
  • Auto regressive integrated moving average (ARIMA) models
  • Fuzzy logic
  • Spatial databases
  • Distributed ledger databases

Published Papers (3 papers)

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Research

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15 pages, 2337 KiB  
Article
Estimating Livestock Grazing Activity in Remote Areas Using Passive Acoustic Monitoring
by Ilias Karmiris, Christos Astaras, Konstantinos Ioannou, Ioakim Vasiliadis, Dionisios Youlatos, Nikolaos Stefanakis, Aspassia D. Chatziefthimiou, Theodoros Kominos and Antonia Galanaki
Information 2021, 12(8), 290; https://doi.org/10.3390/info12080290 - 22 Jul 2021
Cited by 3 | Viewed by 2245
Abstract
Grazing has long been recognized as an effective means of modifying natural habitats and, by extension, as a wildlife and protected area management tool, in addition to the obvious economic value it has for pastoral communities. A holistic approach to grazing management requires [...] Read more.
Grazing has long been recognized as an effective means of modifying natural habitats and, by extension, as a wildlife and protected area management tool, in addition to the obvious economic value it has for pastoral communities. A holistic approach to grazing management requires the estimation of grazing timing, frequency, and season length, as well as the overall grazing intensity. However, traditional grazing monitoring methods require frequent field visits, which can be labor intensive and logistically demanding to implement, especially in remote areas. Questionnaire surveys of farmers are also widely used to collect information on grazing parameters, however there can be concerns regarding the reliability of the data collected. To improve the reliability of grazing data collected and decrease the required labor, we tested for the first time whether a novel combination of autonomous recording units and the semi-automated detection algorithms of livestock vocalizations could provide insight on grazing activity at the selected areas of the Greek Rhodope mountain range. Our results confirm the potential of passive acoustic monitoring (PAM) techniques as a cost-efficient method for acquiring high resolution spatiotemporal data on grazing patterns. Additionally, we evaluate the three algorithms that we developed for detecting cattle, sheep/goat, and livestock bell sounds, and make them available to the broader scientific community. We conclude with suggestions on ways that acoustic monitoring can further contribute to managing legal and illegal grazing, and offer a list of priorities for related future research. Full article
(This article belongs to the Special Issue The Usage of Information Tools and MCDA for the Application of Environmental Policy and Sustainable Development)
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12 pages, 3771 KiB  
Article
A Hybrid Model for Air Quality Prediction Based on Data Decomposition
by Shurui Fan, Dongxia Hao, Yu Feng, Kewen Xia and Wenbiao Yang
Information 2021, 12(5), 210; https://doi.org/10.3390/info12050210 - 15 May 2021
Cited by 13 | Viewed by 2802
Abstract
Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of [...] Read more.
Accurate and reliable air quality predictions are critical to the ecological environment and public health. For the traditional model fails to make full use of the high and low frequency information obtained after wavelet decomposition, which easily leads to poor prediction performance of the model. This paper proposes a hybrid prediction model based on data decomposition, choosing wavelet decomposition (WD) to generate high-frequency detail sequences WD(D) and low-frequency approximate sequences WD(A), using sliding window high-frequency detail sequences WD(D) for reconstruction processing, and long short-term memory (LSTM) neural network and autoregressive moving average (ARMA) model for WD(D) and WD(A) sequences for prediction. The final prediction results of air quality can be obtained by accumulating the predicted values of each sub-sequence, which reduces the root mean square error (RMSE) by 52%, mean absolute error (MAE) by 47%, and increases the goodness of fit (R2) by 18% compared with the single prediction model. Compared with the mixed model, reduced the RMSE by 3%, reduced the MAE by 3%, and increased the R2 by 0.5%. The experimental verification found that the proposed prediction model solves the problem of lagging prediction results of single prediction model, which is a feasible air quality prediction method. Full article
(This article belongs to the Special Issue The Usage of Information Tools and MCDA for the Application of Environmental Policy and Sustainable Development)
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Review

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21 pages, 10533 KiB  
Review
Low-Cost Automatic Weather Stations in the Internet of Things
by Konstantinos Ioannou, Dimitris Karampatzakis, Petros Amanatidis, Vasileios Aggelopoulos and Ilias Karmiris
Information 2021, 12(4), 146; https://doi.org/10.3390/info12040146 - 29 Mar 2021
Cited by 33 | Viewed by 10266
Abstract
Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is [...] Read more.
Automatic Weather Stations (AWS) are extensively used for gathering meteorological and climatic data. The World Meteorological Organization (WMO) provides publications with guidelines for the implementation, installation, and usages of these stations. Nowadays, in the new era of the Internet of Things, there is an ever-increasing necessity for the implementation of automatic observing systems that will provide scientists with the real-time data needed to design and apply proper environmental policy. In this paper, an extended review is performed regarding the technologies currently used for the implementation of Automatic Weather Stations. Furthermore, we also present the usage of new emerging technologies such as the Internet of Things, Edge Computing, Deep Learning, LPWAN, etc. in the implementation of future AWS-based observation systems. Finally, we present a case study and results from a testbed AWS (project AgroComp) developed by our research team. The results include test measurements from low-cost sensors installed on the unit and predictions provided by Deep Learning algorithms running locally. Full article
(This article belongs to the Special Issue The Usage of Information Tools and MCDA for the Application of Environmental Policy and Sustainable Development)
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