Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data
- URL: http://arxiv.org/abs/2405.15835v1
- Date: Fri, 24 May 2024 09:18:17 GMT
- Title: Analyzing the Impact of Climate Change With Major Emphasis on Pollution: A Comparative Study of ML and Statistical Models in Time Series Data
- Authors: Anurag Mishra, Ronen Gold, Sanjeev Vijayakumar,
- Abstract summary: The surge in industrial activities presents a complex challenge in forecasting its diverse environmental impacts.
Aim to understand these dynamics more deeply to predict and mitigate the environmental impacts of industrial activities.
- Score: 1.8092671403632705
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Variable-Agnostic Causal Exploration for Reinforcement Learning [56.52768265734155]
We introduce a novel framework, Variable-Agnostic Causal Exploration for Reinforcement Learning (VACERL)
Our approach automatically identifies crucial observation-action steps associated with key variables using attention mechanisms.
It constructs the causal graph connecting these steps, which guides the agent towards observation-action pairs with greater causal influence on task completion.
arXiv Detail & Related papers (2024-07-17T09:45:27Z) - Modeling of New Energy Vehicles' Impact on Urban Ecology Focusing on Behavior [0.0]
surging demand for new energy vehicles is driven by the imperative to conserve energy, reduce emissions, and enhance the ecological ambiance.
behavioral analysis and mining usage patterns of new energy vehicles can be identified.
Environmental computational modeling method has been proposed to simulate the interaction between new energy vehicles and the environment.
arXiv Detail & Related papers (2024-06-06T14:03:52Z) - Towards A Comprehensive Assessment of AI's Environmental Impact [0.5982922468400899]
Recent surge of interest in machine learning has sparked a trend towards large-scale adoption of AI/ML.
There is a need for a framework that monitors the environmental impact and degradation from AI/ML throughout its lifecycle.
This study proposes a methodology to track environmental variables relating to the multifaceted impact of AI around datacenters using openly available energy data and globally acquired satellite observations.
arXiv Detail & Related papers (2024-05-22T21:19:35Z) - Comparing Data-Driven and Mechanistic Models for Predicting Phenology in
Deciduous Broadleaf Forests [47.285748922842444]
We train a deep neural network to predict a phenological index from meteorological time series.
We find that this approach outperforms traditional process-based models.
arXiv Detail & Related papers (2024-01-08T15:29:23Z) - A Comparative Study of Machine Learning Algorithms for Anomaly Detection
in Industrial Environments: Performance and Environmental Impact [62.997667081978825]
This study seeks to address the demands of high-performance machine learning models with environmental sustainability.
Traditional machine learning algorithms, such as Decision Trees and Random Forests, demonstrate robust efficiency and performance.
However, superior outcomes were obtained with optimised configurations, albeit with a commensurate increase in resource consumption.
arXiv Detail & Related papers (2023-07-01T15:18:00Z) - Long-term Effects of Temperature Variations on Economic Growth: A
Machine Learning Approach [11.668836291461107]
We analyze global land surface temperature data from Berkeley Earth and economic indicators, including GDP and population data, from the World Bank.
Our analysis reveals a significant relationship between average temperature and GDP growth, suggesting that climate variations can substantially impact economic performance.
arXiv Detail & Related papers (2023-06-17T16:50:08Z) - Counting Carbon: A Survey of Factors Influencing the Emissions of
Machine Learning [77.62876532784759]
Machine learning (ML) requires using energy to carry out computations during the model training process.
The generation of this energy comes with an environmental cost in terms of greenhouse gas emissions, depending on quantity used and the energy source.
We present a survey of the carbon emissions of 95 ML models across time and different tasks in natural language processing and computer vision.
arXiv Detail & Related papers (2023-02-16T18:35:00Z) - Data-Centric Epidemic Forecasting: A Survey [56.99209141838794]
This survey delves into various data-driven methodological and practical advancements.
We enumerate the large number of epidemiological datasets and novel data streams that are relevant to epidemic forecasting.
We also discuss experiences and challenges that arise in real-world deployment of these forecasting systems.
arXiv Detail & Related papers (2022-07-19T16:15:11Z) - Unraveling the hidden environmental impacts of AI solutions for
environment [0.04588028371034406]
In the past ten years artificial intelligence has encountered such dramatic progress that it is seen now as a tool of choice to solve environmental issues.
The deep learning community began to realize that training models with more and more parameters required a lot of energy and as a consequence GHG emissions.
This article proposes to study the possible negative impact of "AI for green"
arXiv Detail & Related papers (2021-10-22T14:56:47Z) - Deciphering Environmental Air Pollution with Large Scale City Data [0.0]
Various factors ranging from emissions from traffic and power plants, household emissions, natural causes are known to be primary causal agents or influencers behind rising air pollution levels.
We introduce a large scale city-wise dataset for exploring the relationships among these agents over a long period of time.
Also, we provide a set of benchmarks for the problem of estimating or forecasting pollutant levels with a set of diverse models and methodologies.
arXiv Detail & Related papers (2021-09-09T22:00:51Z) - Analyzing Sustainability Reports Using Natural Language Processing [68.8204255655161]
In recent years, companies have increasingly been aiming to both mitigate their environmental impact and adapt to the changing climate context.
This is reported via increasingly exhaustive reports, which cover many types of climate risks and exposures under the umbrella of Environmental, Social, and Governance (ESG)
We present this tool and the methodology that we used to develop it in the present article.
arXiv Detail & Related papers (2020-11-03T21:22:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.