AIMC Topic:
Environmental Monitoring

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Enhancing Asthma Self-Management with Environmental Passive-Monitoring Data and Machine Learning-Based Predictions.

Studies in health technology and informatics Aug 22, 2024
Monitoring enables timely action which is critical in avoiding asthma attacks. With the abundance of local weather and pollution data, when augmented with machine learning, it is becoming possible to replace traditional tedious active monitoring in c...
Humans Machine Learning Environmental Monitoring Self-Management Asthma
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[Soil Cadmium Prediction and Health Risk Assessment of an Oasis on the Eastern Edge of the Tarim Basin Based on Feature Optimization and Machine Learning].

Huan jing ke xue= Huanjing kexue Aug 8, 2024
Soil heavy metal pollution poses a serious threat to food security, human health, and soil ecosystems. Based on 644 soil samples collected from a typical oasis located at the eastern margin of the Tarim Basin, a series of models, namely, multiple lin...
Soil Pollutants Cadmium Machine Learning Linear Models Environmental Monitoring Soil Humans Support Vector Machine Neural Networks, Computer Ecosystem Risk Assessment China
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A hybrid approach to improvement of watershed water quality modeling by coupling process-based and deep learning models.

Water environment research : a research publication of the Water Environment Federation Aug 1, 2024
Watershed water quality modeling to predict changing water quality is an essential tool for devising effective management strategies within watersheds. Process-based models (PBMs) are typically used to simulate water quality modeling. In watershed mo...
Environmental Monitoring Water Quality Rivers Deep Learning Models, Theoretical
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[Multi-factor Impact Analysis of Grassland Phenology Changes on the Qinghai-Xizang Plateau Based on Interpretable Machine Learning].

Huan jing ke xue= Huanjing kexue Jun 8, 2024
The vegetation phenology of the Qinghai-Xizang Plateau is changing significantly in the context of climate change. However, there are many hydrothermal factors affecting the phenology, and few studies have focused on the effects of multiple factors o...
Seasons Poaceae Climate Change Environmental Monitoring Remote Sensing Technology Rain Altitude Grassland Machine Learning Soil China Temperature
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Long-term monitoring chlorophyll-a concentration using HJ-1 A/B imagery and machine learning algorithms in typical lakes, a cold semi-arid region.

Optics express Apr 22, 2024
Chlorophyll a (Chl-a) in lakes serves as an effective marker for assessing algal biomass and the nutritional level of lakes, and its observation is feasible through remote sensing methods. HJ-1 (Huanjing-1) satellite, deployed in 2008, incorporates a...
Machine Learning Algorithms Satellite Imagery Remote Sensing Technology Lakes Chlorophyll Environmental Monitoring Chlorophyll A
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Rapid fiber-detection technique by artificial intelligence in phase-contrast microscope images of simulated atmospheric samples.

Annals of work exposures and health Apr 22, 2024
Since the manufacture, import, and use of asbestos products have been completely abolished in Japan, the main cause of asbestos emissions into the atmosphere is the demolition and removal of buildings built with asbestos-containing materials. To dete...
Asbestos, Serpentine Microscopy, Phase-Contrast Asbestos Japan Humans Artificial Intelligence Atmosphere Environmental Monitoring Neural Networks, Computer
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Enhancing rainfall-runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics.

Water science and technology : a journal of the International Association on Water Pollution Research Jan 1, 2024
The advancement of data-driven models contributes to the improvement of estimating rainfall-runoff models due to their advantages in terms of data requirements and high performance. However, data-driven models that rely solely on rainfall data have l...
Environmental Monitoring Rain Water Movements Water Models, Theoretical Neural Networks, Computer Machine Learning Soil
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Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

Water science and technology : a journal of the International Association on Water Pollution Research Mar 1, 2023
There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to en...
Artificial Intelligence Machine Learning Environmental Monitoring Water Quality Rivers Fuzzy Logic
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Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction.

Mathematical biosciences and engineering : MBE Sep 1, 2022
As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD) concentration, which is a stream pollutant with an extreme circumstance of...
Rivers Neural Networks, Computer Deep Learning Water Quality Machine Learning Quality Indicators, Health Care Environmental Monitoring
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Rain garden infiltration rate modeling using gradient boosting machine and deep learning techniques.

Water science and technology : a journal of the International Association on Water Pollution Research Nov 1, 2021
Rain garden is effective in reducing storm water runoff, whose efficiency depends upon several parameters such as soil type, vegetation and meteorological factors. Evaluation of rain gardens has been done by various researchers. However, knowledge fo...
Rain Environmental Monitoring Soil Poaceae Deep Learning
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