eprintid: 16437
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/64/37
datestamp: 2023-12-19 03:22:57
lastmod: 2023-12-19 03:22:57
status_changed: 2023-12-19 03:06:15
type: article
metadata_visibility: show
creators_name: Almalawi, A.
creators_name: Khan, A.I.
creators_name: Alsolami, F.
creators_name: Alkhathlan, A.
creators_name: Fahad, A.
creators_name: Irshad, K.
creators_name: Alfakeeh, A.S.
creators_name: Qaiyum, S.
title: Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model
ispublished: pub
keywords: Air pollution; Forecasting; Heavy metals; Learning algorithms; Optimization, Airborne particle; Airborne particle bound metal; Airborne particulate matters; Arithmetic optimization algorithm; Bound metals; Deep learning; Hyper-parameter; Optimization algorithms; Prediction modelling; Size predictions, Long short-term memory, aluminum; copper; heavy metal; zinc; heavy metal, algorithm; atmospheric pollution; fractionation; heavy metal; optimization; particulate matter; size distribution, air pollutant; air pollution; airborne particle; Article; atmospheric particulate matter; correlation coefficient; deep learning; learning algorithm; long short term memory network; mean absolute error; root mean squared error; short term memory; air pollutant; environmental monitoring; human; particle size; particulate matter; procedures, Air Pollutants; Deep Learning; Environmental Monitoring; Humans; Metals, Heavy; Particle Size; Particulate Matter
note: cited By 15
abstract: Recently, heavy metal air pollution has received significant interest in computing the total concentration of every toxic metal. Chemical fractionation of possibly toxic substances in airborne particles becomes a vital element. Among the primary and secondary air pollutants, airborne particulate matter (APM) received considerable internet among research communities owing to the adversative impact on human health. Hence, size distribution details of airborne heavy metals are important in assessing the adverse health effects over the globe. Recently, deep learning models have gained significant interest over the mathematical and statistical prediction models. In this view, this paper presents a novel arithmetic optimization algorithm (AOA) with multi-head attention based bidirectional long short-term memory (MABLSTM) model for predicting the size fractionated airborne particle bound metals. The proposed AOA-MABLSTM technique focuses on the forecasting of the size-fractionated airborne particle bound matter. The presented model intends to examine the concentration of PM and distinct sized-fractionated APM. The proposed model establishes MABLSTM based accurate predictive approaches for atmospheric heavy 83 metals is used for determining temporal trend of heavy metal. The proposed model employs AOA based hyperparameter tuning process to optimally tune the hyperparameters included in the MABLSTM method. To demonstrate the improved outcomes of the AOA-MABLSTM approach, a comparison study is performed with recent models. The stimulation results emphasized the betterment of the presented model over the other methods. Aluminum metal had an RMSE of 73.200 for AOA-MABLSTM. On Cu metal, the AOA-MABLSTM approach had an RMSE of 6.747. On Zn metal, the AOA-MABLSTM system lowered the RMSE by 45.250. © 2022 Elsevier Ltd
date: 2022
publisher: Elsevier Ltd
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85131037482&doi=10.1016%2fj.chemosphere.2022.134960&partnerID=40&md5=c8e14c083e8d4baa6be19f5795b90dda
id_number: 10.1016/j.chemosphere.2022.134960
full_text_status: none
publication: Chemosphere
volume: 303
refereed: TRUE
issn: 00456535
citation:   Almalawi, A. and Khan, A.I. and Alsolami, F. and Alkhathlan, A. and Fahad, A. and Irshad, K. and Alfakeeh, A.S. and Qaiyum, S.  (2022) Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model.  Chemosphere, 303.   ISSN 00456535