AIMC Topic: Environmental Monitoring

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Least square support vector machine-based variational mode decomposition: a new hybrid model for daily river water temperature modeling.

Environmental science and pollution research international
Machines learning models have recently been proposed for predicting rivers water temperature (T) using only air temperature (T). The proposed models relied on a nonlinear relationship between the T and T and they have proven to be robust modelling to...

Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model.

Chemosphere
Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and ...

A novel deep learning method for marine oil spill detection from satellite synthetic aperture radar imagery.

Marine pollution bulletin
Oil spill discharges from operational maritime activities like ships, oil rigs and other structures, leaking pipelines, as well as natural hydrocarbon seepage pose serious threats to marine ecosystems and fisheries. Satellite synthetic aperture radar...

Application of a semivariogram based on a deep neural network to Ordinary Kriging interpolation of elevation data.

PloS one
The Ordinary Kriging method is a common spatial interpolation algorithm in geostatistics. Because the semivariogram required for kriging interpolation greatly influences this process, optimal fitting of the semivariogram is of major significance for ...

Formulating Convolutional Neural Network for mapping total aquifer vulnerability to pollution.

Environmental pollution (Barking, Essex : 1987)
Aquifer vulnerability mapping to pollution is topical research activity, and common frameworks such as the basic DRASTIC framework (BDF) suffer from the inherent subjectivity. This paper formulates an artificial intelligence modeling strategy based o...

MODWT-ANN hybrid models for daily precipitation estimates with time-delayed entries in Amazon region.

Environmental monitoring and assessment
Hydrological analyses based on precipitation records in the Amazon are essential due to their importance in climate regulation and regional and global atmospheric circulation. However, there are limitations related to data series with short periods a...

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks.

Journal of hazardous materials
The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stabi...

Determination of impervious area of Saroor Nagar Watershed of Telangana using spectral indices, MLC, and machine learning (SVM) techniques.

Environmental monitoring and assessment
Urbanization affects the local wind and water cycle by changing the natural surface and atmospheric conditions, which further changes the local climate and climate system. Assessment of built-up-area changes in a rapidly growing urban area within a s...

Water clarity mapping of global lakes using a novel hybrid deep-learning-based recurrent model with Landsat OLI images.

Water research
Information regarding water clarity at large spatiotemporal scales is critical for understanding comprehensive changes in the water quality and status of ecosystems. Previous studies have suggested that satellite observation is an effective means of ...

Using multiple linear regression and BP neural network to predict critical meteorological conditions of expressway bridge pavement icing.

PloS one
Icy bridge deck in winter has tremendous consequences for expressway traffic safety, which is closely related to the bridge pavement temperature. In this paper, the critical meteorological conditions of icy bridge deck were predicted by multiple line...