AIMC Topic: Geologic Sediments

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Inferring landscape-scale land-use impacts on rivers using data from mesocosm experiments and artificial neural networks.

PloS one
Identifying land-use drivers of changes in river condition is complicated by spatial scale, geomorphological context, land management, and correlations among responding variables such as nutrients and sediments. Furthermore, variations in standard me...

Persistence after prohibition: Revealing the drivers of traditional and novel organochlorine pesticide residues in river sediments.

Environmental research
Legacy organochlorine pesticides (OCPs) persist as global environmental threats despite international bans, while novel OCPs have been widely adopted as alternatives; however, the spatiotemporal dynamics and regulatory drivers of both legacy and nove...

Microplastics assessment in the lower stretch of the Ganga River sediment from East Indian region: Influence of land use and rainfall patterns.

Chemosphere
Microplastic (MP) pollution is increasingly viewed as a serious threat to waterways. However, little is known about the effects of land use and rainfall patterns on the occurrence and distribution of MPs in the river sediments. Herein, the MP polluti...

Microbial degradation potential of microplastics in urban river sediments: Assessing and predicting the enrichment of PE/PP-degrading bacteria using SourceTracker and machine learning.

Journal of environmental management
Microplastic mitigation strategies that adapt to various actual aquatic environments require the ability to predict their microbial degradation potential. However, the sources and enrichment characteristics of the degrading bacteria in the plastisphe...

Analyzing the impact of clay minerals on the reservoir quality of the Lower Goru Formation using Unsupervised Machine Learning.

PloS one
The reservoir quality of the Lower Goru Formation is highly variable due to its heterogeneous nature influenced by sea level fluctuations during the Early Cretaceous period. This study applies an unsupervised machine learning workflow, integrating Pr...

Geobiology: Machine learning puts bioturbation on the map.

Current biology : CB
Bioturbation, the mixing of sediment through the actions of organisms, is a crucial ecosystem engineering process that controls biogeochemical cycles and helps structure marine ecosystems. Machine learning is helping to develop global maps of the int...

Decision tree (DT), generalized regression neural network (GR) and multivariate adaptive regression splines (MARS) models for sediment transport in sewer pipes.

Water science and technology : a journal of the International Association on Water Pollution Research
Sediment deposition in sewers and urban drainage systems has great effect on the hydraulic capacity of the channel. In this respect, the self-cleansing concept has been widely used for sewers and urban drainage systems design. This study investigates...