AI Medical Compendium Journal:
Environmental science and pollution research international

Showing 21 to 30 of 423 articles

Improving groundwater quality predictions in semi-arid regions using ensemble learning models.

Environmental science and pollution research international
Groundwater resources constitute one of the primary sources of freshwater in semi-arid and arid climates. Monitoring the groundwater quality is an essential component of environmental management. In this study, a comprehensive comparison was conducte...

Wastewater treatment plant site selection using advanced decision tree machine learning and remote sensing techniques.

Environmental science and pollution research international
Wastewater treatment plants in Coimbatore South are under pressure from rapid urbanization, inadequate infrastructure, and industrial pollution, leading to environmental and public health concerns. This study aimed to identify suitable locations for ...

Utilizing InVEST ecosystem services model combined with deep learning and fallback bargaining for effective sediment retention in Northern Iran.

Environmental science and pollution research international
This study aimed to integrate game theory and deep learning algorithms with the InVEST Ecosystem Services Model (IESM) for Sediment Retention (SR) modeling in the Kasilian watershed, Iran. The Kasilian watershed is characterized by multiple sub-water...

Reproductive performance of Channa striata in wetland ecosystems: a fuzzy logic approach to water quality and eco-climatic factors for long-term sustainable management and aquaculture advancement.

Environmental science and pollution research international
The striped snakehead, Channa striata, is commercially and nutritionally important due to its medicinal properties, such as wound healing and antimicrobial abilities. This study investigated the reproductive biology of C. striata in relation to hydro...

Recent advances in dark fermentative hydrogen production from vegetable waste: role of inoculum, consolidated bioprocessing, and machine learning.

Environmental science and pollution research international
Waste-centred-bioenergy generation have been garnering interest over the years due to environmental impact presented by fossil fuels. Waste generation is an unavoidable consequence of urbanization and population growth. Sustainable waste management t...

Particulate matter estimation using satellite datasets: a machine learning approach.

Environmental science and pollution research international
In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10 µm (PM) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. I...

Machine learning and regression in the management of runoff in bauxite mines under rehabilitation.

Environmental science and pollution research international
Accurate and reliable forecasting of monthly runoff considering several years of rehabilitation helps in planning and managing the water resources system of bauxite mining areas. A combination of linear regression models and artificial intelligence w...

Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran.

Environmental science and pollution research international
Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering...

A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning.

Environmental science and pollution research international
Monthly runoff forecasting plays a critically supportive role in water resources planning and management. Various signal decomposition techniques have been widely applied to enhance the accuracy of monthly runoff forecasting. However, the forecasting...

Stacked Ensemble with Machine Learning Regressors on Optimal Features (SMOF) of hyperspectral sensor PRISMA for inland water turbidity prediction.

Environmental science and pollution research international
Leveraging hyperspectral data across various domains yields substantial benefits, yet managing many spectral bands and identifying the essential ones poses a formidable challenge. This study identifies the most relevant bands within a hyperspectral d...