AIMC Topic: Seasons
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Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages.
PloS one
May 9, 2024
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
May 7, 2024
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
May 1, 2024
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)
Apr 27, 2024
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...
How Lazy Are Pet Cats Really? Using Machine Learning and Accelerometry to Get a Glimpse into the Behaviour of Privately Owned Cats in Different Households.
Sensors (Basel, Switzerland)
Apr 19, 2024
Surprisingly little is known about how the home environment influences the behaviour of pet cats. This study aimed to determine how factors in the home environment (e.g., with or without outdoor access, urban vs. rural, presence of a child) and the s...
Spatiotemporal variation reconstruction of total phosphorus in the Great Lakes since 2002 using remote sensing and deep neural network.
Water research
Mar 19, 2024
Total phosphorus (TP) is non-optically active, thus TP concentration (CTP) estimation using remote sensing still exists grand challenge. This study developed a deep neural network model (DNN) for CTP estimation with synchronous in-situ measurements a...
Convolutional Neural Networks Facilitate Process Understanding of Megacity Ozone Temporal Variability.
Environmental science & technology
Mar 15, 2024
Ozone pollution is profoundly modulated by meteorological features such as temperature, air pressure, wind, and humidity. While many studies have developed empirical models to elucidate the effects of meteorology on ozone variability, they predominan...
ENVINet5 deep learning change detection framework for the estimation of agriculture variations during 2012-2023 with Landsat series data.
Environmental monitoring and assessment
Feb 5, 2024
Remote sensing is one of the most important methods for analysing the multitemporal changes over a certain period. As a cost-effective way, remote sensing allows the long-term analysis of agricultural land by collecting satellite imagery from differe...
Fuzzy logic as a novel approach to predict biological condition gradient of various streams in Ceyhan River Basin (Turkey).
The Science of the total environment
Jan 12, 2024
Creating a method to categorize the ecological status of streams according to their biological conditions and establishing scientifically defensible nutrient criteria to protect their biotic integrity poses significant challenges. Biomonitoring of le...
Separating Daily 1 km PM Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.
Environmental science & technology
Apr 28, 2023
Fine particulate matter (PM) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model si...
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