Forecasting PM2.5 Concentration Using Gradient-Boosted Regression Tree with CNN Learning Model

IF 1 Q4 OPTICS Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI:10.3103/s1060992x24010107

A. Usha Ruby, J. George Chellin Chandran, Prasannavenkatesan Theerthagiri, Renuka Patil, B. N. Chaithanya, T. J. Swasthika Jain

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Abstract

Air pollution imposed by particle matter (PM) made it a public health concern and hazard to humans and the environment. Reduced vision, allergic responses, pneumonia, asthma, cardiovascular disorders, lung cancer, and even mortality can result from prolonged exposure to the concentration of air’s small particulate matter. Air quality prediction can offer reliable information for future air pollution status to operate air pollution control effectively and make preventative plans. Tracking, predicting, and regulating emissions is crucial. Controlling PM2.5 is the key for enhancing air quality, and it can be accomplished by forecasting PM2.5 concentrations. This work develops a methodology for forecasting PM2.5 concentrations using a gradient-boosted regression tree with Convolutional Neural Network (CNN) and fuzzy K-nearest neighbour (fuzzy-KNN). The results of the proposed methodology have been comparatively analysed with multiple linear regression, stacked long short-term memory, bidirectional gated recurrent unit, and gradient-boosted regression tree. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) are evaluated, and it shows that the gradient-boosted regression tree model produces a reduced error with improved accuracy in forecasting air quality.

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利用梯度提升回归树和 CNN 学习模型预测 PM2.5 浓度

摘要颗粒物质（PM）造成的空气污染已成为公共健康问题，并对人类和环境造成危害。长期暴露于空气中的小颗粒物浓度会导致视力下降、过敏反应、肺炎、哮喘、心血管疾病、肺癌，甚至死亡。空气质量预测可以为未来的空气污染状况提供可靠的信息，从而有效地进行空气污染控制和制定预防计划。跟踪、预测和控制排放至关重要。控制 PM2.5 是提高空气质量的关键，而这可以通过预测 PM2.5 的浓度来实现。本研究利用梯度提升回归树、卷积神经网络（CNN）和模糊 K 近邻（fuzzy-KNN），开发了一种预测 PM2.5 浓度的方法。建议方法的结果与多元线性回归、堆叠长短期记忆、双向门控递归单元和梯度增强回归树进行了比较分析。评估了均方根误差 (RMSE)、平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE)，结果表明梯度增强回归树模型可减少误差，提高空气质量预报的准确性。

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.