相关论文: TopoFlow: Topography-aware Pollutant Flow Learning…

We propose TerraFlow, a novel approach to multimodal, multitemporal learning for Earth observation. TerraFlow builds on temporal training objectives that enable sequence-aware learning across space, time, and modality, while remaining…

Air pollution poses a serious threat to sustainable environmental conditions in the 21st century. Its importance in determining the health and living standards in urban settings is only expected to increase with time. Various factors…

Currently, the technique of numerical model-based atmospheric environment forecasting has becoming mature, yet traditional numerical prediction methods struggle to balance computational costs and forecast accuracy, facing developmental…

In recent years, the world has become increasingly concerned with air pollution. Particularly in the global north, countries are implementing systems to monitor air pollution on a large scale to aid decision-making. Such efforts are…

With the increase of global economic activities and high energy demand, many countries have raised concerns about air pollution. However, air quality prediction is a challenging issue due to the complex interaction of many factors. In this…

Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecasting is important because it helps evaluate traffic risk…

Data-driven machine learning approaches have recently been proposed to facilitate wireless network optimization by learning latent knowledge from historical optimization instances. However, existing methods do not well handle the topology…

The first part of this paper introduces a portfolio approach for quantifying the risk measures of pollution risk in the presence of dependence of PM$_{2.5}$ concentration of cities. The model is based on a copula dependence structure. For…

Modeling and synthesizing image noise is an important aspect in many computer vision applications. The long-standing additive white Gaussian and heteroscedastic (signal-dependent) noise models widely used in the literature provide only a…

This paper overviews two interdependent issues important for mining remote sensing data (e.g. images) obtained from atmospheric monitoring missions. The first issue relates the building new public datasets and benchmarks, which are hot…

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine…

The control of complex systems is of critical importance in many branches of science, engineering, and industry. Controlling an unsteady fluid flow is particularly important, as flow control is a key enabler for technologies in energy…

Air pollution is the leading environmental health hazard globally due to various sources which include factory emissions, car exhaust and cooking stoves. As a precautionary measure, air pollution forecast serves as the basis for taking…

While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim…

A computational fluid dynamics (CFD) model that solves the steady-state Reynolds-Averaged Navier-Stokes (RANS) equations for buoyant compressible pollution dispersion under different meteorological conditions is developed. A 6.4 km by 6.4…

Poor air quality can have a significant impact on human health. The National Oceanic and Atmospheric Administration (NOAA) air quality forecasting guidance is challenged by the increasing presence of extreme air quality events due to…

Urban pollution poses serious health risks, particularly in relation to traffic-related air pollution, which remains a major concern in many cities. Vehicle emissions contribute to respiratory and cardiovascular issues, especially for…

Prediction and discovery of new materials with desired properties are at the forefront of quantum science and technology research. A major bottleneck in this field is the computational resources and time complexity related to finding new…

Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally…

Urban wind flow modeling and simulation play an important role in air quality assessment and sustainable city planning. A key challenge for modeling and simulation is handling the complex geometries of the urban landscape. Low order models…