Topical Collection on "Data Mining Algorithms and Intelligence to Empower Pollution Remediation Approaches"
Deadline for manuscript submissions: 10 November 2024
Collection Editors
Institut Teknologi Garut, Indonesia
Email: dede.kurniadi@itg.ac.id, dr.dede.kurniadi@gmail.com
Bicol University, Philippines
Email: bjbbalilo@bicaol-u.edu.ph
The Hong Kong Polytechnic University, China
Email: jiaqi.ruan@polyu.edu.hk
Topical Collection Information
In this day and age, pollution remediation has become expeditiously essential to preserve the environment and human well-being. Therefore, environmental preservation and management have become a global concern, specifically in the colonial sectors. In recent years, various traditional, engineering-based biophysical decontamination approaches have been exploited to remediation of polluted areas. Although, these remediation approaches have limited efficacy with several other challenges, including higher expenses. Thus, implementing innovative technological advances such as data mining and its’ computational algorithms and intelligence aid in empowering the conventional pollution remediation approaches and support a sustainable environment.
As we all know, pollution is one of the greatest environmental crises the world is facing today and is responsible for grave and irrecoverable damage to the human community and the natural world, with most challenges worldwide occurring due to the pollution of air, water, soil, radioactive, thermal, plastic, light, and a lot more. To assess the recovery of polluted soils, groundwater, and contaminated environment, implementing data mining techniques and methodologies help in the understanding of the behavior of various pollution, predict, and then guide and enhance actions to alleviate it. Exploiting data mining can provide significant advances in the remediation process of eliminating contaminants from areas that have been continuously polluted from industrial, mining, commercial, and manufacturing activities. For instance, data mining intelligence is a powerful tool to explore pertinent questions and offer actionable outcomes and convenient applications through professional management and investigation of huge and heterogeneous raw data. Therefore, it identifies effects related to climate and land-use change aids system of feedback which can develop the negative effects of soil pollution on human health, ecological conservation, and soil ecosphere services maintenance. Further, data mining software or data to knowledge in the remediation of heavy metal contaminated sites help to mitigate threats and make the land resource accessible for agricultural purposes. Additionally, applying data mining algorithms helps understand relevant factors as their relationship, such as the potential discovery of non-obvious features in the dataset, which can recommend better conceptions of the physical models. In this context, this special issue intends to explore data mining algorithms and intelligence to empower pollution remediation approaches. We invite researchers, practitioners, environmental experts, and scholars from technology and environment disciplines to present novel and innovative solutions for this special issue.
Suggested topics include, but are not limited to, the following,
- Intelligent data mining methods to control environmental pollution
- Machine learning and data mining for water pollution epidemiology
- Frontiers in predictive biodegradation for sustainable mitigation of ecosystem pollutants
- Role of innovative technologies to prevent marine plastic pollution
- AI and edge computing for industrial transformation
- Data mining algorithms for identifying the spatial distribution of heavy metals at polluted areas
- Deep learning algorithms for air quality prediction
- Predicting air pollution with advanced data mining techniques
- Soil contamination and remediation with data mining algorithms
- Data analytics and AI for pollution risk assessment and remediation
- Artificial neural networks for persistent organic pollutants
- Biological data mining and knowledge discovery for environmental pollution
- Data mining for green remediation approaches for the future era
- Data mining models for contaminated groundwater environment