AIMC Topic: Water Pollution, Chemical
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Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model.
Environment international
Apr 30, 2025
Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals", are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remai...
Modeling and predicting caffeine contamination in surface waters using artificial intelligence and standard statistical methods.
Environmental monitoring and assessment
Dec 5, 2024
Caffeine, considered an emerging contaminant, serves as an indicator of anthropic influence on water resources. This research employs various modeling techniques, including Artificial Neural Networks (ANN), Random Forest (RF), and more, along with hy...
Enhancing groundwater quality prediction through ensemble machine learning techniques.
Environmental monitoring and assessment
Dec 4, 2024
Groundwater quality is assessed by conducting water sampling and laboratory analysis. Field-based measurements are costly and time-consuming. This study introduces a machine learning (ML)-based framework and innovative application of stacking ensembl...
A case study of using artificial neural networks to predict heavy metal pollution in Lake Iznik.
Environmental monitoring and assessment
May 29, 2024
Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosys...
Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants' Activities and Properties.
Environmental science & technology
Jul 5, 2023
In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical structures of contaminants and develop machine learning (ML)-based predictive models for their activities and properties. Compared with the binary Morgan fing...
Prediction of microplastic abundance in surface water of the ocean and influencing factors based on ensemble learning.
Environmental pollution (Barking, Essex : 1987)
May 18, 2023
Microplastics are regarded as emergent contaminants posing a serious threat to the marine ecosystem. It is time-consuming and labor-intensive to determine the number of microplastics in different seas using traditional sampling and detection methods....
A novel random forest approach to revealing interactions and controls on chlorophyll concentration and bacterial communities during coastal phytoplankton blooms.
Scientific reports
Oct 7, 2021
Increasing occurrence of harmful algal blooms across the land-water interface poses significant risks to coastal ecosystem structure and human health. Defining significant drivers and their interactive impacts on blooms allows for more effective anal...
Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills.
Environmental pollution (Barking, Essex : 1987)
Dec 2, 2019
The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challeng...
Predicting the acute ecotoxicity of chemical substances by machine learning using graph theory.
Chemosphere
Aug 16, 2019
Accurate in silico predictions of chemical substance ecotoxicity has become an important issue in recent years. Most conventional methods, such as the Ecological Structure-Activity Relationship (ECOSAR) model, cluster chemical substances empirically ...
Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill.
Environmental science and pollution research international
Dec 3, 2018
Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environ...
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