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Application of AI in Environmental Engineering
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Application of AI in Environmental Engineering

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (30 December 2023) | Viewed by 4046

Special Issue Editors

Dr. Hossein Bonakdari

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: water resources management; environmental study; risk assessment; hydrological modelling; artificial intelligence; sustainable development
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Majid Sartaj

E-Mail Website
Guest Editor
Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: sustainable solid waste management (SWM) technologies; bioreactor landfilling/biogas/renewable natural gas (NRG) production; bioremediation of contaminated soils; adsorption and ion-exchange processes; modelling fate and transport of contaminants in the environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over the past two decades, there have been significant advances in artificial intelligence techniques to improve our understanding and knowledge in the field of environmental science and engineering including meteorology, climatology, aeronomy, ecosystem, biosphere, aquatic, soil chemistry and oceanography. As a result of their powerful modeling abilities for complex and nonlinear problems, AI techniques have shown promise in solving complex data patterns or formats. Providing health and well-being today and in the future requires conserving environmental resources and safeguarding ecosystems. Accurate modeling, analyzing, interpreting and predicting of environmental variables have major economic and social implications for sustainable development. In situations where conventional analytical environmental methods are limited or difficult to apply, AI tools may hold the key to exposing hidden patterns or establishing correlations that are otherwise not possible to uncover using traditional methods.

The current Special Issue will focus on all major fields of environmental sciences including water resources, atmospheric sciences, ecology, water quality, waste management, and geosciences. These objectives of this Special Issue will also enhance the understanding of environmental challenges associated with sustainable development in today’s rapidly globalizing and urbanizing world. Research and review studies focusing on complex and dynamic meteorological/hydrological environmental variables and implementing novel modeling approaches, developing new tools, or improving the existing predictive models are especially welcome.

Dr. Hossein Bonakdari
Dr. Majid Sartaj
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

  • artificial intelligence
  • machine learning
  • data mining
  • atmospheric sciences
  • meteorology and climatology
  • aeronomy
  • natural ecosystems
  • water resources
  • geology
  • water quality modeling
  • environmental hydraulics
  • soil chemistry
  • waste management

Published Papers (2 papers)

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Research

21 pages, 8954 KiB  
Article
Multi-Tempo Forecasting of Soil Temperature Data; Application over Quebec, Canada
by Mohammad Zeynoddin, Hossein Bonakdari, Silvio José Gumiere and Alain N. Rousseau
Sustainability 2023, 15(12), 9567; https://doi.org/10.3390/su15129567 - 14 Jun 2023
Cited by 1 | Viewed by 965
Abstract
The profound impact of soil temperature (TS) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they [...] Read more.
The profound impact of soil temperature (TS) on crucial environmental processes, including water infiltration, subsurface movement, plant growth, and its influence on land–atmosphere dynamics, cannot be undermined. While satellite and land surface model-based data are valuable in data-sparse areas, they necessitate innovative solutions to bridge gaps and overcome temporal delays arising from their dependence on atmospheric and hydro–meteorological factors. This research introduces a viable technique to address the lag in the Famine Early Warning Network Land Data Assimilation System (FLDAS). Notably, this approach exhibits versatility, proving highly effective in analyzing datasets characterized by significant seasonal trends, and its application holds immense value in watershed-scaled hydrological research. Leveraging the enhanced state-space (SS) method for forecasting in the FLDAS, this technique harnesses TS datasets collected over time at various depths (0–10 cm, 10–40 cm, and 40–100 cm), employing a multiplicative SS model for modeling purposes. By employing the 1-step, 6-step, and 12-step-ahead models at different depths and 2 locations in Quebec, Canada, the outcomes showcased a performance with an average coefficient of determination (R2) of 0.88 and root mean squared error (RMSE) of 2.073 °C for the dynamic model, R2 of 0.834 and RMSE of 2.979 °C for the 6-step-ahead model, and R2 of 0.921 and RMSE of 1.865 °C for the 12-step-ahead model. The results revealed that as the prediction horizon expands and the length of the input data increases, the accuracy of predictions progressively improves, indicating that this model becomes increasingly accurate over time. Full article
(This article belongs to the Special Issue Application of AI in Environmental Engineering)
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18 pages, 1358 KiB  
Article
Plant Disease Classification and Adversarial Attack Using SimAM-EfficientNet and GP-MI-FGSM
by Haotian You, Yufang Lu and Haihua Tang
Sustainability 2023, 15(2), 1233; https://doi.org/10.3390/su15021233 - 09 Jan 2023
Cited by 10 | Viewed by 1778
Abstract
Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is [...] Read more.
Plant diseases have received common attention, and deep learning has also been applied to plant diseases. Deep neural networks (DNNs) have achieved outstanding results in plant diseases. Furthermore, DNNs are very fragile, and adversarial attacks in image classification deserve much attention. It is important to detect the robustness of DNNs through adversarial attacks. The paper firstly improves the EfficientNet by adding the SimAM attention module. The SimAM-EfficientNet is proposed in this paper. The experimental results show that the accuracy of the improved model on PlantVillage reaches 99.31%. The accuracy of ResNet50 is 98.33%. The accuracy of ResNet18 is 98.31%. The accuracy of DenseNet is 98.90%. In addition, the GP-MI-FGSM adversarial attack algorithm improved by gamma correction and image pyramid in this paper can increase the success rate of attack. The model proposed in this paper has an error rate of 87.6% whenattacked by the GP-MI-FGSM adversarial attack algorithm. The success rate of GP-MI-FGSM proposed in this paper is higher than other adversarial attack algorithms, including FGSM, I-FGSM, and MI-FGSM. Full article
(This article belongs to the Special Issue Application of AI in Environmental Engineering)
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