AIMC Topic: Cities

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A knowledge-data fusion framework accelerates deep reinforcement learning for real-time control of urban drainage systems.

Water research
Deep reinforcement learning (DRL) has been applied to real-time control (RTC) of urban drainage systems (UDSs), with impressive performance and efficiency in reducing urban flooding and combined sewer overflows (CSO). However, for complex UDSs, learn...

A HAZOP-based hazard identification model for urban gas accidents: Development and empirical validation.

PloS one
Urban gas accidents pose significant threats to public safety and urban infrastructure, with traditional hazard identification methods often relying on manual inspections and experience-based judgments, leading to incomplete or inconsistent results. ...

Analysis of spatial heterogeneity in Xi'an's urban heat island effect using multi-source data fusion.

PloS one
In the context of global climate change, this study aims to investigate the spatial heterogeneity and driving mechanisms of the urban heat island (UHI) effect within Xi'an's second ring road area. We constructed a novel multi-source data fusion frame...

A geography of indoors for analyzing global ways of living using computer vision.

Scientific reports
Globalization is claimed to have a homogenizing effect, reducing pronounced local cultural differences. Indoor living spaces are among the most vivid expressions of local culture, yet they remain underexplored in this context. Our visual AI framework...

Assessing future hydrological and sediment transport response of an urban watershed using a machine learning-based land cover change model.

Environmental monitoring and assessment
Assessing the impacts of land cover change (LCC) on hydrology and sediment load is essential for the sustainable management of urban watersheds. Modeling LCC using machine learning techniques enhances the ability to generate realistic future scenario...

Regional PM2.5 pollution forecasting using a hybrid model based on multi-scales feature fusion and deep learning algorithms.

PloS one
The issue of regional haze pollution has become increasingly prominent. However, early warning models for regional haze pollution are significantly lacking. To accurately predict regional PM2.5 pollution, hourly average concentration data of pollutan...

Short-term passenger flow prediction for urban rail systems: A deep learning approach utilizing multi-source big data.

PloS one
Predicting short-term passenger flow in urban rail transit is crucial for intelligent and real-time management of urban rail systems. This study utilizes deep learning techniques and multi-source big data to develop an enhanced spatial-temporal long ...

Prediction of subjective well-being level in residents of Dali City: Where modern tourism meets traditional ethnic culture.

PloS one
OBJECTIVE: Dali is a city rich in tourism resources and cultural heritage, where residents' subjective well-being (SWB) varies in response to the dynamics of local tourism culture. Few studies have examined the distribution of SWB levels and their in...

Distribution patterns and source contributions of emerging contaminants in urban wastewater systems: from pumping stations to wastewater treatment plants.

Environmental pollution (Barking, Essex : 1987)
Sewage pumping stations and wastewater treatment plants (WWTPs) serve as critical nodes through which emerging pollutants (ECs) migrate from urban and industrial sources into aquatic environments. However, research on the co-occurrence, source dynami...

Machine learning-based assessment of land use change effects on land surface temperature fluctuations in Ho Chi Minh city, Vietnam.

Environmental monitoring and assessment
Sustainable urban development requires actionable insights into the thermal consequences of land transformation. This study examines the impact of land use and land cover (LULC) changes on land surface temperature (LST) in Ho Chi Minh city, Vietnam, ...