AIMC Topic: Seasons

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Predicting rice phenology across China by integrating crop phenology model and machine learning.

The Science of the total environment Aug 21, 2024
This study explores the integration of crop phenology models and machine learning approaches for predicting rice phenology across China, to gain a deeper understanding of rice phenology prediction. Multiple approaches were used to predict heading and...
Climate China Agriculture Machine Learning Seasons Crops, Agricultural Oryza
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Artificial neural networks as a tool for seasonal forecast of attack intensity of Spodoptera spp. in Bt soybean.

International journal of biometeorology Aug 13, 2024
Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that ...
Neural Networks, Computer Forecasting Animals Seasons Glycine max Plants, Genetically Modified Larva Weather Spodoptera
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Elevating hourly PM forecasting in Istanbul, Türkiye: Leveraging ERA5 reanalysis and genetic algorithms in a comparative machine learning model analysis.

Chemosphere Aug 13, 2024
Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA...
Turkey Linear Models Environmental Monitoring Air Pollutants Particulate Matter Seasons Air Pollution Algorithms Forecasting Cities Machine Learning
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Long-term trend forecast of chlorophyll-a concentration over eutrophic lakes based on time series decomposition and deep learning algorithm.

The Science of the total environment Aug 10, 2024
Long-term trend forecast of chlorophyll-a concentration (Chla) holds significant implications for eutrophication management and pollution control planning on lakes, especially under the background of climate change. However, it is a challenging task ...
China Chlorophyll A Eutrophication Lakes Forecasting Seasons Environmental Monitoring Deep Learning Climate Change Chlorophyll
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Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences.

BMC research notes Aug 9, 2024
OBJECTIVE: This study delves into the impact of urban meteorological elements-specifically, air temperature, relative humidity, and atmospheric pressure-on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius),...
Machine Learning Temperature Water Humans Humidity Water Supply Meteorological Concepts Seasons Atmospheric Pressure Cities
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Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series.

PloS one Aug 8, 2024
Fluctuations in the financial market are influenced by various driving forces and numerous factors. Traditional financial research aims to identify the factors influencing stock prices, and existing works construct a common neural network learning fr...
Seasons Time Factors Models, Economic Algorithms Investments Humans Financial Management Neural Networks, Computer
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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 Jul 16, 2024
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...
Seasons Satellite Imagery Global Warming Machine Learning Temperature Environmental Monitoring
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European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression.

Scientific reports Jul 9, 2024
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...
Machine Learning Plant Leaves Climate Change Forests Unmanned Aerial Devices Seasons Fagus
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Multispecies deep learning using citizen science data produces more informative plant community models.

Nature communications May 24, 2024
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...
Plants Ecosystem Switzerland Biodiversity Models, Biological Deep Learning Seasons Citizen Science
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A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Journal of medical systems May 22, 2024
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...
Myocardial Infarction Seasons Forecasting Humans Deep Learning Incidence
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