AIMC Topic: Environmental Monitoring

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Hyperspectral Data and Machine Learning for Estimating CDOM, Chlorophyll , Diatoms, Green Algae and Turbidity.

International journal of environmental research and public health
Inland waters are of great importance for scientists as well as authorities since they are essential ecosystems and well known for their biodiversity. When monitoring their respective water quality, in situ measurements of water quality parameters ar...

FESAEI: a fuzzy rule-based expert system for the assessment of environmental impacts : A fuzzy logic approach to impact assessment.

Environmental monitoring and assessment
Currently, the method mostly used by practitioners of environmental impact assessment (EIA) is the "crisp numbers" method. Nevertheless, this arithmetic method is far away of giving correct values due to its rigidity and the lack of consideration of ...

Evolving connectionist systems (ECoSs): a new approach for modeling daily reference evapotranspiration (ET).

Environmental monitoring and assessment
Over the last few years, the uses of artificial intelligence techniques (AI) for modeling daily reference evapotranspiration (ET) have become more popular and a considerable amount of models were successfully applied to the problem. Therefore, in the...

Mapping specific vulnerability of multiple confined and unconfined aquifers by using artificial intelligence to learn from multiple DRASTIC frameworks.

Journal of environmental management
An investigation is presented to improve on the performances of the Basic DRASTIC Framework (BDF) and its variation by the Fuzzy-Catastrophe Framework (FCF), both of which provide an estimate of intrinsic aquifer vulnerabilities to anthropogenic cont...

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM.

Environmental pollution (Barking, Essex : 1987)
Fine particulate matter (PM) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to m...

Supervised machine learning outperforms taxonomy-based environmental DNA metabarcoding applied to biomonitoring.

Molecular ecology resources
Biodiversity monitoring is the standard for environmental impact assessment of anthropogenic activities. Several recent studies showed that high-throughput amplicon sequencing of environmental DNA (eDNA metabarcoding) could overcome many limitations ...

Application of fuzzy logic tools for the biogeochemical characterisation of (un)contaminated waters from Aljustrel mining area (South Portugal).

Chemosphere
Aljustrel mining area (South Portugal) belongs to the Iberian Pyrite Belt (IPB). It is classified of high environmental risk due to its large tailings and to the Acid Mine Drainage (AMD) affected waters, generated by sulphides' oxidation. Integrating...

Modeling of an activated sludge process for effluent prediction-a comparative study using ANFIS and GLM regression.

Environmental monitoring and assessment
In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive mod...

Effect of Cd and Pb Pollutions on Physiological Growth: Wavelet Neural Network (WNN) as a New Approach on Age Determination of Coenobita scaevola.

Bulletin of environmental contamination and toxicology
Environmental pollution of aquatic ecosystems leads to an interference in several fundamental biochemical and physiological functions. In this study the interference of Cd and Pb pollutions on the physiological growth and subsequently on the age dete...

Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil.

The Science of the total environment
Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition in...