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Artificial Intelligence and Data Science Solutions in Environmental Sensing
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Artificial Intelligence and Data Science Solutions in Environmental Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Environmental Sensing".

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4169

Special Issue Editors

Dr. Giovanni Delnevo

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, University of Bologna, 40126 Bologna, Italy
Interests: machine learning; human–computer interaction; tinyML; digital sustainability
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pietro Manzoni

E-Mail Website
Guest Editor
Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain
Interests: mobile networks and systems; internet of things
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Many of the Sustainable Development Goals (SDGs), presented in “Transforming our world: the 2030 Agenda for Sustainable Development” and adopted by Heads of State, governments, and High Representatives, are related to the preservation of the environment. In this context, artificial intelligence and data science provide unique opportunities in the analysis of the collected data to fight against environmental problems such as air pollution, climate change, and soil erosion, simultaneously promoting sustainable behaviors.

A wide range of environmental sensing systems have become widespread, in both urban and rural scenarios, thanks to the Internet of Things coupled with new networking solutions. Starting from the environmental data collected, artificial intelligence approaches and data science techniques can be exploited to predict events, to assess the impact of environmental policies, and to discover trends and relations.

This Special Issue, entitled "Artificial Intelligence and Data Science in Environmental Sensing", aims to delve into the scientific–technological frontiers of artificial intelligence approaches and data science techniques applied to environmental sensing. We seek original papers that propose new approaches, address existing challenges, and present new applications in the extraction of knowledge from environmental data.

Dr. Giovanni Delnevo
Prof. Dr. Pietro Manzoni
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. Sensors 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 2600 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

  • environmental intelligence
  • data science
  • artificial intelligence
  • machine learning
  • deep learning
  • sustainability IoT

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Published Papers (2 papers)

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Research

18 pages, 966 KiB  
Article
Novel Method for Determining Internal Combustion Engine Dysfunctions on Platform as a Service
by Tomas Harach, Petr Simonik, Adela Vrtkova, Tomas Mrovec, Tomas Klein, Joy Jason Ligori and Martin Koreny
Sensors 2023, 23(1), 477; https://doi.org/10.3390/s23010477 - 2 Jan 2023
Cited by 1 | Viewed by 1553
Abstract
This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses [...] Read more.
This article deals with a unique, new powertrain diagnostics platform at the level of a large number of EU25 inspection stations. Implemented method uses emission measurement data and additional data from significant sample of vehicles. An original technique using machine learning that uses 9 static testing points (defined by constant engine load and constant engine speed), volume of engine combustion chamber, EURO emission standard category, engine condition state coefficient and actual mileage is applied. An example for dysfunction detection using exhaust emission analyses is described in detail. The test setup is also described, along with the procedure for data collection using a Mindsphere cloud data processing platform. Mindsphere is a core of the new Platform as a Service (Paas) for data processing from multiple testing facilities. An evaluation on a fleet level which used quantile regression method is implemented. In this phase of the research, real data was used, as well as data defined on the basis of knowledge of the manifestation of internal combustion engine defects. As a result of the application of the platform and the evaluation method, it is possible to classify combustion engine dysfunctions. These are defects that cannot be detected by self-diagnostic procedures for cars up to the EURO 6 level. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science Solutions in Environmental Sensing)
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13 pages, 862 KiB  
Article
Machine-Learning-Based Near-Surface Ozone Forecasting Model with Planetary Boundary Layer Information
by Kabseok Ko, Seokheon Cho and Ramesh R. Rao
Sensors 2022, 22(20), 7864; https://doi.org/10.3390/s22207864 - 16 Oct 2022
Cited by 5 | Viewed by 1738
Abstract
Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. [...] Read more.
Surface ozone is one of six air pollutants designated as harmful by National Ambient Air Quality Standards because it can adversely impact human health and the environment. Thus, ozone forecasting is a critical task that can help people avoid dangerously high ozone concentrations. Conventional numerical approaches, as well as data-driven forecasting approaches, have been studied for ozone forecasting. Data-driven forecasting models, in particular, have gained momentum with the introduction of machine learning advancements. We consider planetary boundary layer (PBL) height as a new input feature for data-driven ozone forecasting models. PBL has been shown to impact ozone concentrations, making it an important factor in ozone forecasts. In this paper, we investigate the effectiveness of utilization of PBL height on the performance of surface ozone forecasts. We present both surface ozone forecasting models, based on multilayer perceptron (MLP) and bidirectional long short-term memory (LSTM) models. These two models forecast hourly ozone concentrations for an upcoming 24-h period using two types of input data, such as measurement data and PBL height. We consider the predicted values of PBL height obtained from the weather research and forecasting (WRF) model, since it is difficult to gather actual PBL measurements. We evaluate two ozone forecasting models in terms of index of agreement (IOA), mean absolute error (MAE), and root mean square error (RMSE). Results showed that the MLP-based and bidirectional LSTM-based models yielded lower MAE and RMSE when considering forecasted PBL height, but there was no significant changes in IOA when compared with models in which no forecasted PBL data were used. This result suggests that utilizing forecasted PBL height can improve the forecasting performance of data-driven prediction models for surface ozone concentrations. Full article
(This article belongs to the Special Issue Artificial Intelligence and Data Science Solutions in Environmental Sensing)
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