AIMC Topic: Environmental Monitoring

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GeoAI-based soil erosion risk assessment in the Brahmaputra River Basin: a synergistic approach using RUSLE and advanced machine learning.

Environmental monitoring and assessment
Soil erosion is a critical environmental issue in the Brahmaputra River Basin, threatening agricultural productivity, water resources, and ecological balance. This study employs the revised universal soil loss equation (RUSLE) alongside remote sensin...

Monitoring the dynamics of irrigated parcels and impacts on phreatic water quality in the Mostaganem Plateau (northwestern Algeria): an integrated analysis using remote sensing and field data for 2010 and 2020.

Environmental monitoring and assessment
Since the early 2000s, Algeria has implemented several agricultural policies to expand its irrigated areas and enhance its national food security. While these efforts have significantly increased irrigated land, they have raised concerns about ground...

Application of machine learning models for zooplankton abundance prediction in ponds of Southeastern Coastal Regions in Bangladesh.

Environmental monitoring and assessment
Zooplankton abundance prediction in surface water bodies is crucial because they reflect ecosystem health and have role in aquatic food webs and nutrient cycling. This study examined the applicability of machine learning algorithms to estimate zoopla...

Modeling water quality in the brazilian semiarid region using remote sensing: support for water management.

Environmental monitoring and assessment
Water management in semi-arid regions faces challenges due to water scarcity and the need for continuous quality monitoring. This study evaluates the use of remote sensing to analyze a reservoir's water quality status in Brazil's semi-arid region to ...

Prediction of water quality parameters and pollution exceedance analysis in typical rivers of semi-arid regions based on interpretable deep learning models.

Environmental pollution (Barking, Essex : 1987)
Deep learning models that integrate environmental characteristics provide a powerful means for high-precision water quality prediction; however, their black-box nature can limit interpretability and reliability. We proposed an interpretable Attention...

Exploiting the gut microbiota of aquatic animals as indicators of microplastic pollution using interpretable machine learning models.

Journal of hazardous materials
The response of aquatic animal gut microbiota to microplastics has been extensively studied and shows sensitivity, however, the potential of using gut microbiota as indicators for microplastic pollution has not yet been fully explored. To address thi...

Harnessing deep learning for fusion-based heavy metal contamination index prediction in groundwater.

Journal of contaminant hydrology
Groundwater contamination by heavy metals presents a major environmental threat with serious implications for public health and resource sustainability. This study proposes a novel deep learning-based data fusion framework to predict heavy metal cont...

Groundwater health probability risk prediction through oral intake using advanced optimization methods.

Journal of contaminant hydrology
Examining the cancer risk associated with oral groundwater (GW) intake is crucial, particularly in regions heavily reliant on GW for human consumption and agriculture. The study was based on real field investigations and controlled laboratory experim...

Using open data to derive parsimonious data-driven models for uncovering the influence of local traffic and meteorology on air quality: The case of Madrid.

Environmental pollution (Barking, Essex : 1987)
Air pollution remains a critical public health and environmental challenge, particularly in urban areas where traffic emissions and meteorological conditions strongly influence air quality. While Machine Learning (ML) techniques have been increasingl...

Machine learning-based detection of changes in mapping the mangrove forest of the Yangon estuary, Southeast Asia.

Marine environmental research
Mangrove forests are globally acknowledged for stabilizing coastlines, reducing wave energy, and protecting coastal habitats and adjacent land uses from extreme events. However, most regions experience alarming mangrove loss against natural and human...