AIMC Topic: Seasons
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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...
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 ...
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),...
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...
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...
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...
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...
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...
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
May 21, 2024
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
May 20, 2024
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...
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