Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data
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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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 32 references that are in the map. First posted on arXiv 24 May 2024.
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7 of 32 references.
References · 7
- 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 330
- 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
- 2023ClimateLearn: Benchmarking Machine Learning for Weather and Climate Modelingcited by 46
- 2018Exascale Deep Learning for Climate Analyticscited by 28
- 2023Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Seriescited by 28
- 2018A test case for application of convolutional neural networks to spatio-temporal climate data: Re-identifying clustered weather patternscited by 15
- 2021Learning and Dynamical Models for Sub-seasonal Climate Forecasting: Comparison and Collaborationcited by 6
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