A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New methods avoid spectral pollution in transfer operators for accurate analysis.
Bayesian method estimates Kronecker graphical models from autoregressive processes.
Kernel method approximates Koopman operator eigenfunctions.
Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollut…
This paper analyzes air pollution trends in Rwanda using low-cost sensors and machine learning.
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
High levels of air pollution may seriously affect people's living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, m…
Online algorithms for identifying river pollution sources.
Modeling pollution from competing firms using mean-field games.
Unified framework for growth models with environmental risk and pollution-dependent disasters.
Study shows reducing anthropogenic emissions significantly lowers levels but has little effect on in Delhi.
Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.
The study corrects measurement error in evaluating health effects of multiple pollutants.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…
DGPs improve air quality inference from sparse data.
GraphSVR forecasts urban air pollution robustly across stations and seasons.
The study examines how climate risk influences sovereign debt default decisions.
Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.
Polluting fine dusts in South Korea which are mainly consisted of biomass burning and fugitive dust blown from dust belt is significant problem these days. Predicting concentrations of fine dust particles in Seoul is challenging because they are product of complicate chemical reactions among gaseous pollutants and also…
Weather2vec learns representations to adjust for non-local confounding in air pollution studies.
Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental sci…
Fine particulate matter (PM) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of P…
Water pollution is a major global environmental problem, and it poses a great environmental risk to public health and biological diversity. This work is motivated by assessing the potential environmental threat of coal mining through increased sulfate concentrations in river networks, which do not belong to any simple …
The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurem…
Study develops ensemble machine learning framework for predicting groundwater heavy metal pollution.
Modeling air pollutants using data-driven techniques and sparse identification of nonlinear dynamics.
Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of seve…
Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.
Paper uses Gaussian Processes to monitor air quality in Kampala.
Reduced-order model improves LES for atmospheric pollutant dispersion.
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 learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…
Time-aware deep learning methods improve spatial downscaling of atmospheric pollutants.
Study improves PM concentration forecasting using MCCR loss.
Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.
Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially an issue if methods from resource-rich regions are applied wi…
Model predicts carbon price for green tech adoption.
Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.
Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-le…
The study forecasts water quality from satellite data using machine learning.
Video classification is a challenging task in computer vision. Although Deep Neural Networks (DNNs) have achieved excellent performance in video classification, recent research shows adding imperceptible perturbations to clean videos can make the well-trained models output wrong labels with high confidence. In this pap…
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., m…
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 available data. Thus, they are typically hard to reproduce and challenging to a…
This paper analyzes the dynamic incentives for technology adoption under a transferable permits system, which allows for strategic trading on the permit market. Initially, firms can invest both in low-emitting production technologies and trade permits. In the model, technology adoption and allowance price are generated…
Defines an implied CO2-price to cover climate change costs, finding it significantly higher than the SCC.
Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…
This work robustifies Wasserstein distance estimation with MoM estimators for outlier-polluted data.
The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence e…
Double machine learning improves causal effect estimation by relaxing assumptions.