Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine

Li, Yongan; Jiang, Peng; She, Qingshan; Lin, Guang

doi:10.1016/j.envpol.2018.05.072

Cited by 50 publications

(18 citation statements)

References 29 publications

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“…After pretreatment, the spectral information was effectively extracted, and the model exhibited robust extrapolation ability. However, the calibration set R 2 of the ELM model was higher than that of the validation set, which might result in some defects caused by the randomness of the ELM model (Li et al, 2018); therefore, the fitting effect of the ELM model was not as good as that of the RF model. The validation results of the eight models (Fig.…”

Section: Discussionmentioning

confidence: 99%

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Wang

Ding

et al. 2019

PeerJ

143

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Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions.

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Section: Discussionmentioning

confidence: 99%

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Wang

Ding

et al. 2019

PeerJ

143

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“…In this paper, case studies were carried out to measure the performance of forecasting model. Single model and hybrid model including ARIMA [37], GRNN [38], ELM [26], GA-ELM, WOA-ELM and EEMD-WOA-ELM were used as benchmarks to assess the proposed hybrid model. The experiment was first conducted in Beijing to verify the predictive performance of the model in details, and then experiments in Tianjin and Shijiazhuang were used to prove universality.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…The prediction part took advantage of advanced improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and combined whale optimization algorithm (WOA) with extreme learning machine (ELM). The three methods have been proved to be effective in air pollutant forecasting [24,25,26]. Fuzzy comprehensive evaluation (FCE) based on fuzzy mathematics was conducted subsequently.…”

Section: Introductionmentioning

confidence: 99%

A Novel Air Quality Early-Warning System Based on Artificial Intelligence

Zhi

2019

IJERPH

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The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

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“…An important motivation for hydraulic engineers is whether a hybrid soft computing model with an evolutionary algorithm can improve the accuracy of the performance in a field where they have yet to be examined. Different optimization algorithms such as genetic algorithm (GA), firefly algorithm (FFA), shark algorithm (SA), and bat algorithm (BA) have been utilized to train the soft computing models [19][20][21][22][23]. In recent years, multilayer perceptron models have been applied to forecast the scour depth around hydraulic structures.…”

Section: Introductionmentioning

confidence: 99%

Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm

Ehteram

Ahmed

Ling

et al. 2020

Water

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In this research, the advanced multilayer perceptron (MLP) models are utilized to predict the free rate of expansion that usually occurs around the pipeline (PL) because of waves. The MLP model was structured by integrating it with three optimization algorithms: particle swarm optimization (PSO), whale algorithm (WA), and colliding bodies’ optimization (CBO). The sediment size, wave characteristics, and PL geometry were used as the inputs for the applied models. Moreover, the scour rate, vertical scour rate along the pipeline, and scour rate at both right and left sides of the pipeline were predicted as the model outputs. Results of the three suggested models, MLP-CBO, MLP-WA, and MLP-PSO, for both testing and training sessions were assessed based on different statistical indices. The results indicated that the MLP-CBO model performed better in comparison to the MLP-PSO, MLP-WA, regression, and empirical models. The MLP-CBO can be used as a powerful soft-computing model for predictions.

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Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine

Cited by 50 publications

References 29 publications

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

A Novel Air Quality Early-Warning System Based on Artificial Intelligence

Pipeline Scour Rates Prediction-Based Model Utilizing a Multilayer Perceptron-Colliding Body Algorithm

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