Hybrid artificial neural network algorithm for air pollution estimation Article Swipe

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Vijayalaxmi S. Kumbhar , Shaminder Singh Sohi , V. Jayaram , Pillai G Sreelekshmy , Surendra Kumar Shukla , K. Sravan Abhilash ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.53730/ijhs.v6ns5.9080

In recent years, airborne broadcasting has grown more prevalent in cities. Air quality degradation is a severe air pollution issue that exists daily. To forecast the amount of pollutants, Artificial Neural Network (ANN) and Linear Vector Quantization (LVQ) techniques were utilized. The data set dimensions are defined by the pre-processing procedure and the feature extraction mechanism. The ANN model predicts categorization concentration, allowing the LVQ model to classify direct situations with greater accuracy using explanatory factors. The ANN+LVQ model outperformed other technologies in terms of classification accuracy. The raw data was cleaned to improve the accuracy of the prediction algorithms. The pollutants discovered in the collection are NO2, NOx, O3, Benzene, Xylene, NH3, CO, SO2, PM10, NO, and Toluene. The performance of the recommendation and forecast models were tested in this study using two datasets in two distinct experiments. In urban, rural, and industrial settings, the proposed ANN model is successful in detecting air quality and predicting pollution levels. The ANN-LVQ model obtained 90% percent sensitivity, 97.59% accuracy, and 99.46% specificity with 2.43% error rate. The suggested model's accuracy is much greater than that of other current research models.

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Artificial Intelligence

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Artificial neural network Learning vector quantization Air quality index Computer science Data mining Machine learning Artificial intelligence Mean squared error Meteorology Statistics Mathematics Geography

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Type: article
Language: en
Landing Page: https://doi.org/10.53730/ijhs.v6ns5.9080
PDF: https://sciencescholar.us/journal/index.php/ijhs/article/download/9080/5201
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Cited By: 1
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DOI: https://doi.org/10.53730/ijhs.v6ns5.9080

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Title: Hybrid artificial neural network algorithm for air pollution estimation

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Language: en

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Publication year: 2022

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Publication date: 2022-06-16

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Authors: Vijayalaxmi S. Kumbhar, Shaminder Singh Sohi, V. Jayaram, Pillai G Sreelekshmy, Surendra Kumar Shukla, K. Sravan Abhilash

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Landing page: https://doi.org/10.53730/ijhs.v6ns5.9080

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Concepts: Artificial neural network, Learning vector quantization, Air quality index, Computer science, Data mining, Machine learning, Artificial intelligence, Mean squared error, Meteorology, Statistics, Mathematics, Geography

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abstract_inverted_index.proposed	146
abstract_inverted_index.research	187
abstract_inverted_index.accuracy,	167
abstract_inverted_index.accuracy.	86
abstract_inverted_index.detecting	152
abstract_inverted_index.pollution	18, 157
abstract_inverted_index.prevalent	8
abstract_inverted_index.procedure	50
abstract_inverted_index.settings,	144
abstract_inverted_index.suggested	176
abstract_inverted_index.utilized.	40
abstract_inverted_index.Artificial	29
abstract_inverted_index.collection	105
abstract_inverted_index.dimensions	44
abstract_inverted_index.discovered	102
abstract_inverted_index.extraction	54
abstract_inverted_index.industrial	143
abstract_inverted_index.mechanism.	55
abstract_inverted_index.pollutants	101
abstract_inverted_index.predicting	156
abstract_inverted_index.prediction	98
abstract_inverted_index.situations	69
abstract_inverted_index.successful	150
abstract_inverted_index.techniques	38
abstract_inverted_index.algorithms.	99
abstract_inverted_index.degradation	13
abstract_inverted_index.explanatory	74
abstract_inverted_index.performance	120
abstract_inverted_index.pollutants,	28
abstract_inverted_index.specificity	170
abstract_inverted_index.Quantization	36
abstract_inverted_index.broadcasting	4
abstract_inverted_index.experiments.	138
abstract_inverted_index.outperformed	79
abstract_inverted_index.sensitivity,	165
abstract_inverted_index.technologies	81
abstract_inverted_index.categorization	60
abstract_inverted_index.classification	85
abstract_inverted_index.concentration,	61
abstract_inverted_index.pre-processing	49
abstract_inverted_index.recommendation	123
cited_by_percentile_year.max	94
cited_by_percentile_year.min	90
countries_distinct_count	1
institutions_distinct_count	6
sustainable_development_goals[0].id	https://metadata.un.org/sdg/11
sustainable_development_goals[0].score	0.6800000071525574
sustainable_development_goals[0].display_name	Sustainable cities and communities
citation_normalized_percentile.value	0.36617396
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False