Air Pollution Prediction

7 papers with code • 0 benchmarks • 1 datasets

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Most implemented papers

Multi-task Learning for Aggregated Data using Gaussian Processes

frb-yousefi/aggregated-multitask-gp NeurIPS 2019

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

vanduc103/air_analysis_v1 5 Apr 2018

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

Zhiyuan-Wu/MSSTN 2019 IEEE International Conference on Big Data (Big Data) 2019

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

mayukh18/deap 9 Sep 2021

Air pollution poses a serious threat to sustainable environmental conditions in the 21st century.

PCACE: A Statistical Approach to Ranking Neurons for CNN Interpretability

silviacasac/ranking-cnn-neurons 31 Dec 2021

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

2023-MindSpore-1/ms-code-77 journal 2022

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

canererden/hpo_pm_2_5 International Journal of Environmental Science and Technology 2023

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.