Scholarlink

The Air Quality Index (AQI) forecasting is a very significant area of research as it has an impact on worldwide ecosystems and human health. The close monitoring of AQI is necessary to develop different mitigation strategies. The present study investigated the efficacy of different techniques such as Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks to forecast AQI patterns. After modeling the historical AQI climate data it is found that LSTM models can accurately predict AQI as these models can capture temporal dependencies. As per this study, LSTM models can be used to make predictions and to support policies for tackling related issues of the dynamism of Climate Science.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Temporal Dependency Modeling in Air Quality Index Prediction Using LSTM Networks

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Yajnaseni Dash,
  • Ajith Abraham,
  • Naween Kumar,
  • Gayatri Purohit,
  • Arnav Mittal,
  • Aarav Bagla

摘要

The Air Quality Index (AQI) forecasting is a very significant area of research as it has an impact on worldwide ecosystems and human health. The close monitoring of AQI is necessary to develop different mitigation strategies. The present study investigated the efficacy of different techniques such as Linear Regression, Support Vector Regression, Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks to forecast AQI patterns. After modeling the historical AQI climate data it is found that LSTM models can accurately predict AQI as these models can capture temporal dependencies. As per this study, LSTM models can be used to make predictions and to support policies for tackling related issues of the dynamism of Climate Science.