# Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data

arXiv: https://arxiv.org/abs/2405.15835 · HTML: https://mlsanity.com/paper/2405.15835/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 7 of 7)

- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2206.09112/) (2022)
- [ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modeling](https://mlsanity.com/paper/2307.01909/) (2023)
- [Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series](https://mlsanity.com/paper/2301.11308/) (2023)
- [Exascale Deep Learning for Climate Analytics](https://mlsanity.com/paper/1810.01993/) (2018)
- [A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patterns](https://mlsanity.com/paper/1811.04817/) (2018)
- [Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaboration](https://mlsanity.com/paper/2110.05196/) (2021)

## Abstract (arXiv)

Industrial operations have grown exponentially over the last century, driving advancements in energy utilization through vehicles and machinery.This growth has significant environmental implications, necessitating the use of sophisticated technology to monitor and analyze climate data.The surge in industrial activities presents a complex challenge in forecasting its diverse environmental impacts, which vary greatly across different regions.Aim to understand these dynamics more deeply to predict and mitigate the environmental impacts of industrial activities.