Research on prediction of environmental aerosol and PM2.5 based on artificial neural network

Wang, Xianghong; Wang, Baozhen

doi:10.1007/s00521-018-3861-y

Cited by 41 publications

(20 citation statements)

References 13 publications

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“…Huang used a large number of predictor variables to forecast macroeconomic time series variables, first used the principal component analysis to construct a small number of indices to summarize the predictor variables, used an approximate dynamic factor model as a statistical framework for estimating indicators and forecasting structure, selected data from 1970 to 1998 to simulate real-time 215 predictor variables, where the predictions outperformed univariate autoregressive models [5]. Wang reviewed empirical evidence on the success of different econometric model-based economic forecasting methods in practice and found that models that allow for a reduction in VARs by starting with a relatively loose lag specification, by least-squares estimation, tested on constant data, had the best results on average [6]. Guo et al suggested that the extensive use of cointegration-based equilibrium correction models (ECM) in macroeconometric forecasting may increase their sensitivity to deterministic shifts, with forecasting accuracy decreasing instead when the data show jumps [7].…”

Section: Status Of Researchmentioning

confidence: 99%

“…e weights and thresholds of shallow neural networks are commonly calculated by BP algorithm, and the process of calculating weights and thresholds is described as the training process. e essence of the process of calculating weights and thresholds of neural networks can be described as the optimization process represented by equation (6). As can be seen from Figure 2, the traditional BP algorithm starts from one point in the search space and follows the gradient descent to find the optimal solution of the target, which is very easy to fall into the local minima point, while the neuroevolutionary method uses the evolutionary algorithm to start from multiple points in the search space, which is relatively easy to find the global minima point; therefore, the global optimization algorithm, which is the evolutionary algorithm, can be used to achieve the fast network weights and thresholds computational capability and improve the search capability of the network.…”

Section: Neural Network Fusion Bionic Algorithm Designmentioning

confidence: 99%

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Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Dai

Lin

2021

Complexity

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In this article, an in-depth study and analysis of economic structure are carried out using a neural network fusion release algorithm. The method system defines the weight space and structure space of neural networks from the perspective of optimization theory, proposes a bionic optimization algorithm under the weight space and structure space, and establishes a neuroevolutionary method with shallow neural network and deep neural network as the research objects. In the shallow neuroevolutionary, the improved genetic algorithm (IGA) based on elite heuristic operation and migration strategy and the improved coyote optimization algorithm (ICOA) based on adaptive influence weights are proposed, and the shallow neuroevolutionary method based on IGA and the shallow neuroevolutionary method based on ICOA are applied to the weight space of backpropagation (BP) neural networks. In deep neuroevolutionary method, the structure space of convolutional neural network is proposed to solve the search space design of neural structure search (NAS), and the GA-based deep neuroevolutionary method under the structure space of convolutional neural network is proposed to solve the problem that numerous hyperparameters and network structure parameters can produce explosive combinations when designing deep learning models. The neural network fusion bionic algorithm used has the application value of exploring the spatial structure and dynamics of the socioeconomic system, improving the perception of the socioeconomic situation, and understanding the development law of society, etc. The idea is also verifiable through the present computer technology.

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Section: Status Of Researchmentioning

confidence: 99%

Section: Neural Network Fusion Bionic Algorithm Designmentioning

confidence: 99%

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Dai

Lin

2021

Complexity

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“…The indirect measurements [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][33][34][35][36][37][38][39] used prediction approach based on the past data of the predictand, pollutants and/or meteorological parameters. These pollutants and meteorological parameters are correlated with the predictand [32,[40][41][42].…”

Section: Introductionmentioning

confidence: 99%

“…Prediction of PM2.5 based on the back propagation (BP) neural network was explored [26] using satellite-based aerosol optical depth (AOD), meteorological data and past PM2.5 data. The optimized version of the BP network using a genetic algorithm is proposed in [27].…”

Section: Introductionmentioning

confidence: 99%

Analytical equations based prediction approach for PM2.5 using artificial neural network

Shah

Mishra

2020

SN Appl. Sci.

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The worldwide, particulate matter pollution is considered one of the deadliest types of air pollution due to its significant impact on the global environment and human health. The particulate matter (PM2.5) plays a key role in evaluating the air quality index. However, the conventional PM2.5 monitoring instruments used by the air quality monitoring stations are costly, bulkier, time-consuming, and power-hungry. Furthermore, due to limited data availability and non-scalability, it is challenging to provide high spatial and temporal resolution in real-time. To overcome these challenges, we present analytical equations based prediction approach for PM2.5 using an artificial neural network. Moreover, we contribute the correlation study between PM2.5 and other pollutants using a large authenticate data set of Central Pollution Control Board online station, India. The correlation study reveals the strong correlation of eight pollutants with PM2.5, which found useful for the proposed prediction model and future research work. The computation of the proposed analytical equation using a low-cost processing tool (excel sheet) demonstrates a good match between predicted and actual results. Additionally, the derived analytical equation for the prediction can be computed using a wireless sensor node which ultimately eliminates the need for costly propriety tools. The performance of proposed analytical equation for prediction show root mean square error and coefficient of determination ( R 2 ) 1.80 μg/m 3 and 0.99 respectively using eight correlated predictors. The recalibrated prediction model with three correlated predictors show RMSE of 7.54 μg/m 3 and R 2 of 0.97 and proves the effectiveness of the proposed approach in implementation using minimum power-hungry gas sensors on the WSN. Therefore, obtained results demonstrate that the proposed approach is one of the promising approaches for monitoring PM2.5 without power-hungry gas sensors and bulkier analyzers.

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“…A prediction model of pKa values of neutral and alkaline drugs based on particle swarm optimization algorithm and back propagation artificial neural network, called PSO-BP ANN, was established by Chen et al [11]. Wang et al [12] select the air quality data released in real time, obtain the historical monitoring data of air environmental pollutants and normalize the data, and then divide the sample data, and divide it into training data set and test data set in appropriate proportion. Xu and Li [13] discuss the definition of the scientific connotation of the coordinated development of regional economy and put forward three evaluation indexes for coordinated development of regional economy (the degree of regional economic integration, the degree of regional economic development gap and the speed of regional economic development).…”

mentioning

confidence: 99%

Special issue on machine learning-based applications and techniques in cyber intelligence

Mei

Sugumaran

2019

Neural Comput & Applic

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The development of machine learning needs to rely on cyber intelligence technology, because it uses many theories and methods of cyber intelligence. Furthermore, it has achieved certain results. The value of machine learning as an infrastructure of cyber intelligence is about to usher in a long-term development opportunity. On the other hand, solving the problem of scalability and growth of cyber intelligence cannot be done without machine learning technology. The development of cyber intelligence also needs to rely on the environment and technology support of machine learning. It is necessary for cyber intelligence, just like human beings, to have a lot of knowledge and rich experience; behind these knowledge and experience is the need for a large number of data support. The development of cyber intelligence and machine learning will push each other to form an effective mutual promotion effect. Therefore, the development of cyber intelligence relies on machine learning [1-3], at the meanwhile; machine learning also opens a new chapter for cyber intelligence. Humans will develop intelligent technology under a new perspective of mechanism, thinking and environment of machine learning [4][5][6]. This special issue focuses on the theory, methods and creative solutions of cyber intelligence.The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. This special issue will be published by Neural Computing and Applications as special issues.

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Research on prediction of environmental aerosol and PM2.5 based on artificial neural network

Cited by 41 publications

References 13 publications

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Economic Structure Analysis Based on Neural Network and Bionic Algorithm

Analytical equations based prediction approach for PM2.5 using artificial neural network

Special issue on machine learning-based applications and techniques in cyber intelligence

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