Gaussian Processes for Monitoring Air-Quality in Kampala
원문 초록
Monitoring air pollution is of vital importance to the overall health of the population. Unfortunately, devices that can measure air quality can be expensive, and many cities in low and middle-income countries have to rely on a sparse allocation of them. In this paper, we investigate the use of Gaussian Processes for both nowcasting the current air-pollution in places where there are no sensors and forecasting the air-pollution in the future at the sensor locations. In particular, we focus on the city of Kampala in Uganda, using data from AirQo's network of sensors. We demonstrate the advantage of removing outliers, compare different kernel functions and additional inputs. We also compare two sparse approximations to allow for the large amounts of temporal data in the dataset.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Statistical Machine Learning for Astronomy -- A Textbook
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
MathOptAI.jl: Embed trained machine learning predictors into JuMP models
Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
The Currents of Conflict: Decomposing Conflict Trends with Gaussian Processes
Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
토론
이 논문과 연결된 포럼 글이 아직 없습니다.