AIMC Topic: Environmental Monitoring

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A comparative study of deep learning-based network model and conventional method to assess beach debris standing-stock.

Marine pollution bulletin
The conventional survey of marine debris standing-stock has various drawbacks such as high cost and inaccuracy because the total amount of debris in the whole beach is inferred using the results of the manual investigation in selected narrow areas. T...

A novel multi-model data-driven ensemble approach for the prediction of particulate matter concentration.

Environmental science and pollution research international
Accuracy in the prediction of the particulate matter (PM and PM) concentration in the atmosphere is essential for both its monitoring and control. In this study, a novel neuro fuzzy ensemble (NF-E) model was proposed for prediction of hourly PM and P...

A spatially based quantile regression forest model for mapping rural land values.

Journal of environmental management
Rural land valuation plays an important role in the development of land use policies for agricultural purposes. The advance of computational software and machine learning methods has enhanced mass appraisal methodologies for modeling and predicting e...

Prediction modeling of potentially toxic elements' hydrogeopollution using an integrated Q-mode HCs and ANNs machine learning approach in SE Nigeria.

Environmental science and pollution research international
Machine learning techniques have proven to be very useful in environmental and engineering assessments, including water quality studies. This is because they have flexible linear and nonlinear forecasting functions that can efficiently and reliably e...

A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Exploring the characteristics and types of heavy metal pollution in crops has important implications for food security and human health. In this study, a method for distinguishing heavy metal-polluted elements in corn leaves was proposed. Based on th...

Quantification of chlorophyll-a in typical lakes across China using Sentinel-2 MSI imagery with machine learning algorithm.

The Science of the total environment
Lake eutrophication has attracted the attention of the government and general public. Chlorophyll-a (Chl-a) is a key indicator of algal biomass and eutrophication. Many efforts have been devoted to establishing accurate algorithms for estimating Chl-...

Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms.

Environmental monitoring and assessment
Evapotranspiration (ET) is one of the most important components of global hydrologic cycle and has significant impacts on energy exchange and climate change. Numerous models have been developed to estimate ET so far; however, great uncertainties in m...

From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?

Environmental science & technology
Dissolved oxygen (DO) reflects river metabolic pulses and is an essential water quality measure. Our capabilities of forecasting DO however remain elusive. Water quality data, specifically DO data here, often have large gaps and sparse areal and temp...

Understanding global changes in fine-mode aerosols during 2008-2017 using statistical methods and deep learning approach.

Environment international
Despite their extremely small size, fine-mode aerosols have significant impacts on the environment, climate, and human health. However, current understandings of global changes in fine-mode aerosols are limited. In this study, we employed newly devel...

Asthma-prone areas modeling using a machine learning model.

Scientific reports
Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased. Therefore, the purpose of our study was to determine the asthma-prone areas in Tehran, Iran conside...