A Novel Approach for Air Quality Index Prognostication using Hybrid Optimization Techniques

Krishnaraj Rajagopal, Kumar Narayanan
{"title":"A Novel Approach for Air Quality Index Prognostication using Hybrid Optimization Techniques","authors":"Krishnaraj Rajagopal, Kumar Narayanan","doi":"10.54392/irjmt2427","DOIUrl":null,"url":null,"abstract":"This research presents an innovative deep learning approach for forecasting the Air Quality Index (AQI), a crucial public health concern in both developed and developing countries. The proposed methodology encompasses four stages: (a) Pre-processing, involving data cleaning and transformation; (b) Feature Extraction, capturing central tendency, dispersion, higher order statistics, and Spearman's rank correlation; (c) Feature Selection, using a novel hybrid optimization model, Particle Updated Grey Wolf Optimizer (PUGWO); and (d) an ensembled deep learning model for AQI prediction, integrating a Convolutional Neural Network (CNN), an optimized Bi-directional Long Short-Term Memory (Bi-LSTM), and an Auto-encoder. The CNN and Auto-encoder are trained on the extracted features, and their outputs are fed into the optimized Bi-LSTM for final AQI prediction. Implemented on the PYTHON platform, this model is evaluated through R^2, MAE, and RMSE error metrics. The proposed HRFKNN model demonstrates superior performance with an R-Square of 0.961, RMSE of 11.92, and MAE of 10.29, outperforming traditional models like Logistic Regression, HRFLM, and HRFDT. This underscores its effectiveness in delivering precise and reliable AQI predictions.","PeriodicalId":14412,"journal":{"name":"International Research Journal of Multidisciplinary Technovation","volume":"185 ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Research Journal of Multidisciplinary Technovation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54392/irjmt2427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This research presents an innovative deep learning approach for forecasting the Air Quality Index (AQI), a crucial public health concern in both developed and developing countries. The proposed methodology encompasses four stages: (a) Pre-processing, involving data cleaning and transformation; (b) Feature Extraction, capturing central tendency, dispersion, higher order statistics, and Spearman's rank correlation; (c) Feature Selection, using a novel hybrid optimization model, Particle Updated Grey Wolf Optimizer (PUGWO); and (d) an ensembled deep learning model for AQI prediction, integrating a Convolutional Neural Network (CNN), an optimized Bi-directional Long Short-Term Memory (Bi-LSTM), and an Auto-encoder. The CNN and Auto-encoder are trained on the extracted features, and their outputs are fed into the optimized Bi-LSTM for final AQI prediction. Implemented on the PYTHON platform, this model is evaluated through R^2, MAE, and RMSE error metrics. The proposed HRFKNN model demonstrates superior performance with an R-Square of 0.961, RMSE of 11.92, and MAE of 10.29, outperforming traditional models like Logistic Regression, HRFLM, and HRFDT. This underscores its effectiveness in delivering precise and reliable AQI predictions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用混合优化技术预测空气质量指数的新方法
本研究提出了一种创新的深度学习方法,用于预测空气质量指数(AQI),这在发达国家和发展中国家都是一个重要的公共健康问题。所提出的方法包括四个阶段:(a) 预处理,包括数据清理和转换;(b) 特征提取,包括中心倾向、离散度、高阶统计和斯皮尔曼等级相关性;(c) 特征选择,使用新型混合优化模型--粒子更新灰狼优化器(PUGWO);(d) 用于空气质量指数预测的集合深度学习模型,包括卷积神经网络(CNN)、优化的双向长短期记忆(Bi-LSTM)和自动编码器。CNN 和自动编码器根据提取的特征进行训练,其输出输入优化的 Bi-LSTM 以进行最终的 AQI 预测。该模型在PYTHON平台上实现,通过R^2、MAE和RMSE误差指标进行评估。拟议的 HRFKNN 模型表现出卓越的性能,R 平方为 0.961,RMSE 为 11.92,MAE 为 10.29,优于逻辑回归、HRFLM 和 HRFDT 等传统模型。这凸显了它在提供精确可靠的空气质量指数预测方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
0.50
自引率
0.00%
发文量
0
期刊最新文献
Advancing Fault Detection Efficiency in Wireless Power Transmission with Light GBM for Real-Time Detection Enhancement Quantum Chemical Computational Studies on the Structural Aspects, Spectroscopic Properties, Hirshfeld Surfaces, Donor-Acceptor Interactions and Molecular Docking of Clascosterone: A Promising Antitumor Agent Evaluation of Structural Stability of Four-Storied building using Non-Destructive Testing Techniques Diagnosis of COVID-19 in X-ray Images using Deep Neural Networks An Ensemble Classification Model to Predict Alzheimer’s Incidence as Multiple Classes
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1