AIMC Topic: Seasons

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Simulation and prediction of sulfamethazine migration, transformation and risk diffusion during cross-media infiltration from surface water to groundwater driven by dynamic water level: Machine learning coupled HYDRUS-GMS model.

Journal of environmental management
Seasonal water level fluctuations in rivers significantly influenced the cross-media migration, transformation, and risk diffusion of antibiotics from the vadose zone into groundwater. This study developed a coupled model integrating machine learning...

Regional PM prediction with hybrid directed graph neural networks and Spatio-temporal fusion of meteorological factors.

Environmental pollution (Barking, Essex : 1987)
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...

Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador.

International journal of biometeorology
Accurately estimating gross primary productivity (GPP) is crucial for simulating the carbon cycle and addressing the challenges of climate change. However, estimating GPP is challenging due to the absence of direct measurements at scales larger than ...

Motivation and socialization during summer predict medical students' success: An artificial intelligence study.

Medical teacher
PURPOSE: The latest reform of French medical studies has moved the National Ranking Examination before residency to the beginning of the sixth-year for undergraduate medical students, thus placing unprecedented workload during the preceding summer. T...

Enhancing streamflow prediction in a mountainous watershed using a convolutional neural network with gridded data.

Environmental science and pollution research international
In this research, we demonstrate the effectiveness of a convolutional neural network (CNN) model, integrated with the ERA5-Land dataset, for accurately simulating daily streamflow in a mountainous watershed. Our methodology harnesses image-based inpu...

Downscaling MODIS evapotranspiration into finer resolution using machine learning approach on a small scale, Ribb watershed, Ethiopia.

Environmental monitoring and assessment
By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agr...

High spatiotemporal resolution estimation and analysis of global surface CO concentrations using a deep learning model.

Journal of environmental management
Ambient carbon monoxide (CO) is a primary air pollutant that poses significant health risks and contributes to the formation of secondary atmospheric pollutants, such as ozone (O). This study aims to elucidate global CO pollution in relation to healt...

Hypoxia extreme events in a changing climate: Machine learning methods and deterministic simulations for future scenarios development in the Venice Lagoon.

Marine pollution bulletin
Climate change pressures include the dissolved oxygen decline that in lagoon ecosystems can lead to hypoxia, i.e. low dissolved oxygen concentrations, which have consequences to ecosystem functioning including biogeochemical cycling from mild to seve...

Development, study, and comparison of models of cross-immunity to the influenza virus using statistical methods and machine learning.

Voprosy virusologii
INTRODUCTION: The World Health Organization considers the values of antibody titers in the hemagglutination inhibition assay as one of the most important criteria for assessing successful vaccination. Mathematical modeling of cross-immunity allows fo...

Parametric seasonal-trend autoregressive neural network for long-term crop price forecasting.

PloS one
Crop price forecasting is difficult in that supply is not as elastic as demand, therefore, supply and demand should be stabilized through long-term forecasting and pre-response to the price. In this study, we propose a Parametric Seasonal-Trend Autor...