Air Pollution Prediction
7 papers with code • 0 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Air Pollution Prediction
Most implemented papers
Multi-task Learning for Aggregated Data using Gaussian Processes
Our model represents each task as the linear combination of the realizations of latent processes that are integrated at a different scale per task.
Real-time Air Pollution prediction model based on Spatiotemporal Big data
In this paper, based on this spatiotemporal Big data, we propose a real-time air pollution prediction model based on Convolutional Neural Network (CNN) algorithm for image-like Spatial distribution of air pollution.
MSSTN: Multi-Scale Spatial Temporal Network for Air Pollution Prediction
We further present a novel deep convolutional neural network model, named Multi-Scale Spatial Temporal Network (MSSTN), for the learning task on this data structure.
Deciphering Environmental Air Pollution with Large Scale City Data
Air pollution poses a serious threat to sustainable environmental conditions in the 21st century.
PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability
In this paper we introduce a new problem within the growing literature of interpretability for convolution neural networks (CNNs).
Air Pollution Prediction in Mass Rallies With a New Temporally-Weighted Sample-Based Multitask Learner
Then, we construct a temporal support vector regressor (TSVR), which puts more emphasis on the adjacent samples by considering the fact that the crowd usually flows promptly and disorderly in mass rallies.
Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction
The prediction results of deep learning algorithms are compared with default hyperparameters and random search algorithms to confirm the efficacy of the genetic algorithm approach.