Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Yuan, Hang; Yu, Jia Yuan; Dai, X.; Huang, Zheng; Li, Yuanyuan

doi:10.3390/su14094889

Cited by 31 publications

(11 citation statements)

References 45 publications

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“…Accordingly, in this case, one needs to reduce the δ parameter for (9). If the time series is close to random, then the errors e n p τ τ − ( ) will increase, and, therefore, it is necessary to take them into consideration in adjusting the forecast according to the combined selective model, increasing the δ parameter for (9). The level of persistence, randomness, and anti-persistence of the time series is determined on the basis of fractal R/S-analysis.…”

Section: Discussion Of the Choice Of Parameters For The Forecasting M...mentioning

confidence: 99%

“…The results of the study described in [8] showed that the EMD method can be used to improve the accuracy of the forecasting model when working with complex air quality data. Paper [9] considers the combination of models based on EMD with the model of neural networks. Neural network models require parameter adjustments to ensure the calculation of the high accuracy forecast.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The EMD-ESM hybrid model (6), the HWM additive model (7), and the adaptive TLM (8) model, as well as their selective type combination (9), were implemented. To calculate the forecast error, an experimental section of the time series { } , z t t u n = was selected, U < u < n. U is chosen taking into account the adaptation of models of exponential smoothing to the mechanism of time series generation, theoretically, it should be U > 40, in the verification process the value U = 100 was chosen.…”

Section: Development and Validation Of The Combined Mo Del For Foreca...mentioning

confidence: 99%

See 2 more Smart Citations

Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

Biloshchytskyi¹,

Kuchansky

Andrashko

et al. 2022

EEJET

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This paper formalizes the model of choosing a strategy for reducing air pollution in an urban environment. The model involves determining the optimal location of biotechnological systems – biotechnological filter systems or smart air purification devices based on solving the problem of discrete optimization, taking into consideration the forecast of the air quality index. Two subtasks have been formalized, which make it possible to form a strategy for reducing air pollution. To solve one of the subtasks, a combined selective model for predicting the time series of the Air Quality Index (CSM) was built. The combined model software suite consists of the EMD-ESM hybrid model (Empirical Mode Decomposition-Exponential Smoothing Model), the HWM additive model (Holt-Winters Model), and the adaptive TLM (Trigg-Lich Model). To verify the proposed combined selective model, the time series of air quality indices (AQI) for the city of Nur-Sultan (data from 2010‒2021, period 6 hours) were selected. As a result of verification, it was established that in the case of short-term forecasting of the air quality index time series, the EMD-ESM model has an advantage according to the criterion of a minimum root mean square error (RMSE), δ=0.11. For the case of medium-term forecasting of 3<τ≤5, the combined selective model (CSM) has the advantage. The results reported here are input data for the task of choosing strategies for reducing the volume of air pollution in the urban environment. The study’s results make it possible to increase the flexibility of the formation of strategies for reducing air pollution since they avoid restrictions on the location of cleaners in specific urban areas. The consequence is the improvement of the environmental situation in the city and the development of the region in general

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Section: Discussion Of the Choice Of Parameters For The Forecasting M...mentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Development and Validation Of The Combined Mo Del For Foreca...mentioning

confidence: 99%

See 1 more Smart Citation

Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

Biloshchytskyi¹,

Kuchansky

Andrashko

et al. 2022

EEJET

View full text Add to dashboard Cite

show abstract

“…For recent decades, due to rapid urbanization and economic growth, developing countries like India are facing severe haze pollution. The World Health Organization (WHO) has stated that around 4.2 million individuals died prematurely/annum to long‐term exposure to ambient atmosphere 1 . PM2.5 is the major risk factor among all pollutants, causing more health hazards such as lung cancer, cardiovascular disease, asthma, and so forth.…”

Section: Introductionmentioning

confidence: 99%

Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach

Kandan,

Jayasakthi Velmurugan,

Gururama Senthilvel

et al. 2024

Trans Emerging Tel Tech

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In recent times, air quality prediction is turned out to be one of the important research topics among research communities to prevent lives from negative health impacts. Random fluctuations of PM2.5 level brought about by frequent variations in meteorological factors create difficulties air pollution management. Forecasting the quality of air using time series data serves as a defense mechanism against threatening hazards by providing immense support to take preventive measures. Besides, handling dynamic real time workloads, forecasted by the prediction model requires appropriate computing resources to distribute workloads based on demands. To achieve this goal, this paper proposes a new Air Quality Prediction‐enabled Resource Allocation scheme for cloud‐based software services, which offers dynamic adjustment of resources based on workload demands with high energy efficiency. The proposed system is a two phase system that executes both air quality prediction and resource allocation processes consecutively. A new weighted average ensemble classifier is designed by combining support vector machine (SVM), artificial neural network (ANN), and gradient boosting machine (GBM) techniques to measure PM2.5 level on time series information of Beijing PM2.5 dataset. The system then dynamically allocates appropriate computing resources using crossover particle swarm optimization (CPSO) algorithm based on the forecasted results of PM2.5 level. This system has the potential to contribute significantly to reducing energy consumption and improving air quality in cities worldwide. The experimental results conducted to determine the efficiency of the proposed system in terms of different metrics proves that it achieves greater performance with less error functions for PM2.5 level prediction as well as minimizes energy consumption for resource allocation when compared with existing methods.

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“…Four different position updating methods are used according to the different categories of individuals. Although DBO has advantages of strong global search capability, it also has disadvantages of local optimization and slow convergence due to the random distribution of initial population [21]. Scholars have made improvements to address the shortcomings.…”

Section: Introductionmentioning

confidence: 99%

A Multi-strategy Improved Dung Beetle Optimisation Algorithm and its Application

Gu,

Wang

2024

Preprint

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In order to solve the shortcomings of Dung Beetle Optimizer such as low convergence accuracy and easy to fall into local optimum, a multi-strategy improved Dung Beetle Optimizer (IDBO) is proposed. Firstly, the Cubic chaotic mapping strategy is introduced to improve the diversity of the initial population. Secondly, the global exploration strategy in the Fishhawk algorithm is introduced to give the dung beetle algorithm the exploration ability of identifying the optimal region and escaping from the local optimum, which initially improves the convergence speed and optimality-seeking accuracy of the algorithm. Finally, the dung beetle foraging behaviour is perturbed using the adaptive t-distribution perturbation strategy, making the dung beetle algorithm improve the global exploitation ability and local exploration ability while further accelerate its speed of convergence. The effectiveness of the three improved strategies is verified by testing and analysing the CEC2021 and CEC2017 test functions. The optimization results of the improved algorithms and the comparison algorithms are subjected to convergence analysis and Wilcoxon rank sum test, which proves that the IDBO algorithm has good convergence speed and optimization accuracy. In addition, the IDBO algorithm is adopted to optimise parameters of the HKELM prediction model which is applied to the short-term PV power prediction simulation and comparison experiments. Experimental results show that the IDBO-HKELM prediction model can effectively improve the prediction accuracy of short-term PV power, which further verifies the feasibility and validity of the IDBO algorithm in solving the problems of practical applications.

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Air-Quality Prediction Based on the EMD–IPSO–LSTM Combination Model

Cited by 31 publications

References 45 publications

Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

Building a model for choosing a strategy for reducing air pollution based on data predictive analysis

Air quality forecasting‐driven cloud resource allocation for sustainable energy consumption: An ensemble classifier approach

A Multi-strategy Improved Dung Beetle Optimisation Algorithm and its Application

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