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Environmental Monitoring

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The intuitionistic fuzzy linguistic assessment of forest soil quality with multi-granularity qualitative information.

Environmental monitoring and assessment
The soil quality of forest land is directly related to the growth of forest trees and the local ecological environment. This paper proposes an intuitionistic fuzzy linguistic aggregation method for heterogeneous linguistic assessment information, to ...

U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision.

Neural networks : the official journal of the International Neural Network Society
Forest fires pose a serious threat to the global ecological environment, and the critical steps in reducing the impact of fires are fire warning and real-time monitoring. Traditional monitoring methods, like ground observation and satellite sensing, ...

A three-dimensional marine plastic litter real-time detection embedded system based on deep learning.

Marine pollution bulletin
Marine plastic pollution has emerged as a significant ecological and biological issue impacting global marine ecosystems. To develop real-time cleaning systems for marine plastic litter, we implemented a three-dimensional marine plastic litter real-t...

Trustworthy and Human Centric neural network approaches for prediction of landfill methane emission and sustainable waste management practices.

Waste management (New York, N.Y.)
Landfills rank third among the anthropogenic sources of methane gas in the atmosphere, hence there is a need for greater emphasis on the quantification of landfill methane emission for mitigating environmental degradation. However, the estimation and...

Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

Environmental science and pollution research international
Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentration in rivers and its interactions with other water-quality parameters is crucial for effective freshw...

Predicting On-Road Air Pollution Coupling Street View Images and Machine Learning: A Quantitative Analysis of the Optimal Strategy.

Environmental science & technology
Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify ...

High-resolution groundwater storage anomalies in the Middle and Lower Yangtze River Basin of China using machine learning fusion of in-situ wells, satellite gravity and hydrological model.

Journal of environmental management
Groundwater plays a key role in the water cycle and is used to meet industrial, agricultural, and domestic water demands. High-resolution modeling of groundwater storage is often challenging due to the limitations of observation techniques and mathem...

XIS-PM: A daily spatiotemporal machine-learning model for PM in the contiguous United States.

Environmental research
Air-pollution monitoring is sparse across most of the United States, so geostatistical models are important for reconstructing concentrations of fine particulate air pollution (PM) for use in health studies. We present XGBoost-IDW Synthesis (XIS), a ...

A spatial interpolation method based on 3D-CNN for soil petroleum hydrocarbon pollution.

PloS one
Petroleum hydrocarbon pollution causes significant damage to soil, so accurate prediction and early intervention are crucial for sustainable soil management. However, traditional soil analysis methods often rely on statistical methods, which means th...

Advancing micro-nano supramolecular assembly mechanisms of natural organic matter by machine learning for unveiling environmental geochemical processes.

Environmental science. Processes & impacts
The nano-self-assembly of natural organic matter (NOM) profoundly influences the occurrence and fate of NOM and pollutants in large-scale complex environments. Machine learning (ML) offers a promising and robust tool for interpreting and predicting t...