AIMC Topic: Seasons

Clear Filters Showing 81 to 90 of 228 articles

Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery.

Environmental monitoring and assessment
Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for l...

European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression.

Scientific reports
Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observat...

Multispecies deep learning using citizen science data produces more informative plant community models.

Nature communications
In the age of big data, scientific progress is fundamentally limited by our capacity to extract critical information. Here, we map fine-grained spatiotemporal distributions for thousands of species, using deep neural networks (DNNs) and ubiquitous ci...

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Journal of medical systems
Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscle, leading to the deterioration or death of the affected tissue due to a lack of oxygen. MI, poses a...

Identifying influence factors and thresholds of the next day's pollen concentration in different seasons using interpretable machine learning.

The Science of the total environment
The prevalence of pollen allergies is a pressing global issue, with projections suggesting that half of the world's population will be affected by 2050 according to the estimation of the World Health Organization (WHO). Accurately forecasting pollen ...

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model.

Journal of environmental management
Nitrogen dioxide (NO) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO levels thro...

Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages.

PloS one
Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the la...

Seasonal antigenic prediction of influenza A H3N2 using machine learning.

Nature communications
Antigenic characterization of circulating influenza A virus (IAV) isolates is routinely assessed by using the hemagglutination inhibition (HI) assays for surveillance purposes. It is also used to determine the need for annual influenza vaccine update...

Spatiotemporal models of dengue epidemiology in the Philippines: Integrating remote sensing and interpretable machine learning.

Acta tropica
Previous dengue epidemiological analyses have been limited in spatiotemporal extent or covariate dimensions, the latter neglecting the multifactorial nature of dengue. These constraints, caused by rigid and traditional statistical tools which collaps...

Daily scale air quality index forecasting using bidirectional recurrent neural networks: Case study of Delhi, India.

Environmental pollution (Barking, Essex : 1987)
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such a...