An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10)

Particulate matter with a diameter less than 10 micrometers (PM10) is today an important subject of study, mainly because of its increasing concentration and its impact on environment and public health. is article summarizes the usage of convolutional neural networks (CNNs) to forecast PM10 concentrations based on atmospheric variables. In this particular case-study, the use of deep convolutional neural networks (both 1D and 2D) was explored to probe the feasibility of these techniques in prediction tasks. Furthermore, in this contribution, an ensemble method called Bagging (BEM) is used to improve the accuracy of the prediction model. Lastly, a well-known technique for PM10 forecasting, called multilayer perceptron (MLP) is used as a comparison to show the feasibility, accuracy, and robustness of the proposed model. In this contribution, it was found that the CNNs outperforms MLP, especially when they are executed using ensemble models.Hindawi

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“…Next, the sigmoid activation function has an output that ranges between 0 and 1. In equation (4) and Figure 13 is shown its behavior.…”

Section: Hyperparametersmentioning

confidence: 98%

“…Previous approaches to the problem of PM10 modeling include the usage of artificial neural networks, fuzzy logic, and evolutionary computation. In [4] is implemented a PSO to a neurofuzzy method to enhance the modeling performance.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Aceves-Fernández

Domínguez-Guevara

Pedraza-Ortega

et al. 2020

Discrete Dynamics in Nature and Society

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“…Conventional ANFIS model proceeds with an integration of the gradient descent backpropagation algorithm and the least square method for the optimization of the adaptive layers [42]. However, recent studies in this domain have shown that optimization of the adaptive layers in ANFIS model with evolutionary algorithm increases ANFIS prediction accuracy [43,44]. This study optimizes the ANFIS model with PSO.…”

Section: Optimisation Of Anfis Model With Psomentioning

confidence: 99%

“…The 1 and 2 components of the equation are randomly generated numbers within the range of 0 and 1. The readers may refer to [17,34,35,43,46,47] for more information and further reading on PSO and ANFIS.…”

Section: Optimisation Of Anfis Model With Psomentioning

confidence: 99%

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Olatunji¹,

Akinlabi²,

Madushele³

et al. 2019

EAI Endorsed Transactions on Energy Web

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The global challenges of climate change have been compounded by an unprecedented level of environmental pollution consequent upon the municipal solid waste, MSW generation. Recent advances by researchers and policymakers are focused on sustainable and renewable energy sources which are technologically feasible, environmentally friendly, and economically viable. Waste-to-fuel initiative is therefore highly beneficial to our environment while also improves the socioeconomic well-being the nations. This current study introduces an adaptive neuro-fuzzy inference systems (ANFIS) model optimised with Particle Swarm Optimisation (PSO) algorithm aimed at predicting the enthalpy of combustion of MSW fuel based on the moisture content (H 2 O), Carbon, Hydrogen, Oxygen, Nitrogen, Sulphur, and Ash contents. This model was trained with 86 MSW biomass data and further tested with a new 37 data points. The developed model was observed to performed better in term of the accuracy when compared with other existing models in the literature. The model was evaluated based on some known error estimation. The values of Root Mean Squared Error (RMSE), Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Log Accuracy ratio (LAR), Coefficient of Correlation (CC) were 3.6277, 22.6202, 0.0337, 0.8673 respectively at computation time (CT) of 36.96 secs. Regression analysis was also carried out to determine the level of correlation between the experimental and predicted high heating values (HHV).

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“…The present work proposes a method to model the particulate matter concentrations using the BFOA; this method is considered as a novel method since it has not been found in the literature an application of the BFO algorithm in the problem of modeling the concentration of particulate material. Likewise, another contribution of the present work is to demonstrate how the adjustment of the parameters of the algorithm affects the result and the way in which each of these Particle Swarm Optimization, PSO [13] have also been applied to obtain a precise model for the concentration of polluting particles, where the technique showed good performance and is one of the recent works for the particulate matter modeling. PSO belongs to the swarm intelligence algorithms, the same as BFOA, that is why is expected to demonstrate that a swarm intelligence algorithm is capable to generate an accurate model for the problem of PM10 behavior.…”

Section: Introductionmentioning

confidence: 99%

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

Cabrera-Hernandez¹,

Aceves-Fernández²,

Ramos-Arreguín³

et al. 2019

IJIS

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The airborne pollutants monitoring is an overriding task for humanity given that poor quality of air is a matter of public health, causing issues mainly in the respiratory and cardiovascular systems, specifically the PM10 particle. In this contribution is generated a base model with an Adaptive Neuro Fuzzy Inference System (ANFIS) which is later optimized, using a swarm intelligence technique, named Bacteria Foraging Optimization Algorithm (BFOA). Several experiments were carried with BFOA parameters, tuning them to achieve the best configuration of said parameters that produce an optimized model, demonstrating that way, how the optimization process is influenced by choice of the parameters. parameters individually influences said results.The methodology is basically to use BFOA as optimizer of a base model generated with another technique, ANFIS. The model generated with ANFIS presents some inaccuracies since it is unstable with highly non-linear problems such as the one that is to be modeled in this work. This is why this method was devised where the accuracy of the base model is improved. Once the model optimized with BFOA is generated, it will be compared against that generated with ANFIS.The use of an algorithm that has several agents or swarm intelligence, such as BFOA, gives us the opportunity to find an optimal solution since it involves several, relatively simple agents exploring the study area, thus having a greater probability of finding the optimal global values avoiding the problem of getting stuck in a local solution as it happens with other methods such as neural networks, as well as being robust, flexible systems without central control that issues orders to system agents [1].To better understand what the problem is, it is important to define some concepts, which are presented below.

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An improved particle swarm optimization (PSO): method to enhance modeling of airborne particulate matter (PM10)

Cited by 13 publications

References 30 publications

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Evaluation of Key Parameters Using Deep Convolutional Neural Networks for Airborne Pollution (PM10) Prediction

Estimation of Municipal Solid Waste (MSW) combustion enthalpy for energy recovery

Parameters Influencing the Optimization Process in Airborne Particles PM10 Using a Neuro-Fuzzy Algorithm Optimized with Bacteria Foraging (BFOA)

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