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Artificial Intelligence Pathway for Environmental Sustainability: Monitoring, Modeling, and Decision Making

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 September 2022) | Viewed by 14737

Special Issue Editors

Dr. Omid Rahmati

E-Mail Website
Guest Editor
Soil Conservation and Watershed Management Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, AREEO, Sanandaj, Iran
Interests: environmental sustainability; geospatial modeling; watershed management; natural disasters
Special Issues, Collections and Topics in MDPI journals
Dr. Zahra Kalantari

E-Mail Website
Guest Editor
Department of Physical Geography and Bolin Centre for Climate Research, Stockholm University, SE-10691 Stockholm, Sweden
Interests: regional and global hydrology; water resources; water-related disasters; sustainable urban and rural development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Decisions involving sustainability require scientific information on natural processes and the role of geo-environmental and anthropogenic factors. The concept of environmental sustainability has different facets, for example, involving ecological carrying capacity, natural resources, human activities, natural disasters, and critique of technology. However, environmental processes are complicated and assessing their systems and dynamics is often difficult or even impossible. Geo-environmental modeling, an approach for simultaneously managing and learning about natural processes on the landscape scale, has been widely applied for efficient environmental management for several decades. Artificial intelligence (AI) technology, such as algorithms, data-mining, and statistical models, can provide vital information about the relationships between natural resources, natural disasters (flood, ground subsidence, landslide, debris flow, wildfire, etc.), geo-environmental factors, and human activities, and thus can support adaptive decision making. AI technology allows for the inclusion of knowledge processing (decision support systems) for sustainable natural resource management. This Special Issue will benefit natural, environmental, social, and sustainability scientists, engineers, managers, and other stakeholders with an interest in the GIS-based machine learning modeling of natural disasters and environmental planning. This open access Special Issue welcomes high-quality and innovative scientific papers describing cutting-edge research related to the application of artificial intelligence approaches, GIS, and remote sensing techniques in the study of sustainability-related issues.

Dr. Omid Rahmati
Dr. Zahra Kalantari
Guest Editors

Manuscript Submission Information

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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
  • Sustainable development
  • Nature-based solutions
  • Geo-environmental modeling
  • Natural disasters
  • Natural resources
  • Ecosystem services

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Published Papers (4 papers)

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Research

16 pages, 3998 KiB  
Article
Application of Machine Learning (ML) and Artificial Intelligence (AI)-Based Tools for Modelling and Enhancing Sustainable Optimization of the Classical/Photo-Fenton Processes for the Landfill Leachate Treatment
by Hüseyin Cüce and Duygu Özçelik
Sustainability 2022, 14(18), 11261; https://doi.org/10.3390/su141811261 - 8 Sep 2022
Cited by 3 | Viewed by 2023
Abstract
This study presents a machine learning (ML)/artificial intelligence (AI)-based perspective to reliably predict and enhance the treatment efficiency of landfill leachate by classical-Fenton (c-Fenton) and photo-Fenton (p-Fenton) processes. This experiment also sought to lower treatment costs by evaluating the impact of using different [...] Read more.
This study presents a machine learning (ML)/artificial intelligence (AI)-based perspective to reliably predict and enhance the treatment efficiency of landfill leachate by classical-Fenton (c-Fenton) and photo-Fenton (p-Fenton) processes. This experiment also sought to lower treatment costs by evaluating the impact of using different numbers of UV-c (254 nm) lamps during p-Fenton processes, as well as to develop a sustainable process design for landfill leachate. In the modeling stage, the radial basis function neural network (RBFN), the feed forward neural network (FFNN), and the support vector regression (SVR) were used and the results were evaluated in a broad scanning. Our experimental results, optimized with the help of genetic algorithm (GA), showed an increasing trend in treatment efficiency and a decreasing trend in chemical usage amounts for p-Fenton oxidation. The results indicate that both treatment techniques performed (classical and p-Fenton) within 1 h contact time showed a very high pollutant removal with a reduction in COD of approximately 60% and 80%, respectively, during the first 30 min of processing. Additionally, it was noted that the COD elimination for the c-Fenton and the p-Fenton was significantly finished in first 15 min, 52% and 73%, respectively. According to the results of the optimization model, there is an increase from 62 to 82 percent under eight UV lamps compared to seven UV lamps when considering the impact of the number of UV lamps on the treatment efficiency in p-Fenton. It has been noted that when the results are taken as a whole, the better modeling abilities of ML-based models, particularly the RBFN and the FFNN, come to the fore. From a different angle, the FFNN and the RBFNN have both shown percentile errors that are extremely close to zero when MAPE values, a percentile error measure independent of the unit of the data set, are evaluated alone. Except for two tests whose desirability levels are still around 99.99%, all experiments attained outstanding desirability levels of 100.00%. This serves as more evidence for the higher modeling performance of these ML-based approaches. Full article
(This article belongs to the Special Issue Artificial Intelligence Pathway for Environmental Sustainability: Monitoring, Modeling, and Decision Making)
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22 pages, 4827 KiB  
Article
Urban Flood-Risk Assessment: Integration of Decision-Making and Machine Learning
by Fereshteh Taromideh, Ramin Fazloula, Bahram Choubin, Alireza Emadi and Ronny Berndtsson
Sustainability 2022, 14(8), 4483; https://doi.org/10.3390/su14084483 - 9 Apr 2022
Cited by 45 | Viewed by 5789
Abstract
Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can [...] Read more.
Urban flood-risk mapping is an important tool for the mitigation of flooding in view of continuing urbanization and climate change. However, many developing countries lack sufficiently detailed data to produce reliable risk maps with existing methods. Thus, improved methods are needed that can help managers and decision makers to combine existing data with more soft semi-subjective data, such as citizen observations of flood-prone and vulnerable areas in view of existing settlements. Thus, we present an innovative approach using the semi-subjective Analytic Hierarchy Process (AHP), which integrates both subjective and objective assessments, to help organize the problem framework. This approach involves measuring the consistency of decision makers’ judgments, generating pairwise comparisons for choosing a solution, and considering criteria and sub-criteria to evaluate possible options. An urban flood-risk map was created according to the vulnerabilities and hazards of different urban areas using classification and regression-tree models, and the map can serve both as a first stage in advancing flood-risk mitigation approaches and in allocating warning and forecasting systems. The findings show that machine-learning methods are efficient in urban flood zoning. Using the city Rasht in Iran, it is shown that distance to rivers, urban drainage density, and distance to vulnerable areas are the most significant parameters that influence flood hazards. Similarly, for urban flood vulnerability, population density, land use, dwelling quality, household income, distance to cultural heritage, and distance to medical centers and hospitals are the most important factors. The integrated technique for both objective and semi-subjective data as outlined in the present study shows credible results that can be obtained without complicated modeling and costly field surveys. The proposed method is especially helpful in areas with little data to describe and display flood hazards to managers and decision makers. Full article
(This article belongs to the Special Issue Artificial Intelligence Pathway for Environmental Sustainability: Monitoring, Modeling, and Decision Making)
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21 pages, 2835 KiB  
Article
Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model
by Yongmao Xiao, Wei Yan, Ruping Wang, Zhigang Jiang and Ying Liu
Sustainability 2021, 13(4), 1929; https://doi.org/10.3390/su13041929 - 11 Feb 2021
Cited by 5 | Viewed by 2203
Abstract
The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on [...] Read more.
The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production. Full article
(This article belongs to the Special Issue Artificial Intelligence Pathway for Environmental Sustainability: Monitoring, Modeling, and Decision Making)
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30 pages, 8184 KiB  
Article
A Methodological Comparison of Three Models for Gully Erosion Susceptibility Mapping in the Rural Municipality of El Faid (Morocco)
by Ali Azedou, Said Lahssini, Abdellatif Khattabi, Modeste Meliho and Nabil Rifai
Sustainability 2021, 13(2), 682; https://doi.org/10.3390/su13020682 - 12 Jan 2021
Cited by 24 | Viewed by 3018
Abstract
Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of [...] Read more.
Erosion is the main threat to sustainable water and soil management in Morocco. Located in the Souss-Massa watershed, the rural municipality of El Faid remains an area where gully erosion is a major factor involved in soil degradation and flooding. The aim of this study is to predict the spatial distribution of gully erosion at the scale of this municipality and to evaluate the predictive capacity of three prediction methods (frequency ratio (FR), logistic regression (LR), and random forest (RF)) for the characterization of gullying vulnerability. Twelve predisposing factors underlying gully formation were considered and mapped (elevation, slope, aspect, plane curvature, slope length (SL), stream power index (SPI), composite topographic index (CTI), land use, topographic wetness index (TWI), normalized difference vegetation index (NDVI), lithology, and vegetation cover (C factor). Furthermore, 894 gullies were digitized using high-resolution imagery. Seventy-five percent of the gullies were randomly selected and used as a training dataset, whereas the remaining 25% were used for validation purposes. The prediction accuracy was evaluated using area under the curve (AUC). Results showed that the factor that most contributed to the prevalence of gullying was topographic (slope, CTI, LS). Furthermore, the fitted models revealed that the RF model had a better prediction quality, with the best AUC (91.49%). The produced maps represent a valuable tool for sustainable management, land conservation, and protecting human lives against natural hazards (floods). Full article
(This article belongs to the Special Issue Artificial Intelligence Pathway for Environmental Sustainability: Monitoring, Modeling, and Decision Making)
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