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Daily PM2.5 concentration prediction based on variational modal decomposition and deep learning for multi-site temporal and spatial fusion of meteorological factors.

Environmental monitoring and assessment
Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid m...

Accurate PM urban air pollution forecasting using multivariate ensemble learning Accounting for evolving target distributions.

Chemosphere
Over the past decades, air pollution has caused severe environmental and public health problems. According to the World Health Organization (WHO), fine particulate matter (PM), a key component reflecting air quality, is the fourth leading cause of de...

Spatial source, simulating improvement, and short-term health effect of high PM exposure during mutation event in the key urban agglomeration regions in China.

Environmental pollution (Barking, Essex : 1987)
Air quality in China has significantly improved owing to the effective implementation of pollution control measures. However, mutation events caused by short-term spikes in PM in urban agglomeration regions continue to occur frequently. Identifying t...

Elevating hourly PM forecasting in Istanbul, Türkiye: Leveraging ERA5 reanalysis and genetic algorithms in a comparative machine learning model analysis.

Chemosphere
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...

Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences.

BMC research notes
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),...

Supporting data-enhanced hybrid ordinary differential equation model for phosphate dynamics in municipal wastewater treatment.

Bioresource technology
A parallel hybrid ordinary differential equation (ODE) integrating the Activated Sludge Model No. 2d (ASM2d) and an artificial neural network (ANN) was developed to simulate biological phosphorus removal (BPR) with high accuracy and interpretability....

Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks.

Water research
Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks, and a surrogate model is needed to accelerate the online predictive modelling. Fully-connected neural networks (NNs) are potent...

Predicting Intimate Partner Violence Perpetration Among Young Adults Experiencing Homelessness in Seven U.S. Cities Using Interpretable Machine Learning.

Journal of interpersonal violence
Young adults experiencing homelessness (YAEH) are at higher risk for intimate partner violence (IPV) victimization than their housed peers. This is often due to their increased vulnerability to abuse and victimization before and during homelessness, ...

Machine learning-based analysis and prediction of meteorological factors and urban heatstroke diseases.

Frontiers in public health
INTRODUCTION: Heatstroke is a serious clinical condition caused by exposure to high temperature and high humidity environment, which leads to a rapid increase of the core temperature of the body to more than 40°C, accompanied by skin burning, conscio...