scholarly journals A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

2019 ◽  
Vol 9 (18) ◽  
pp. 3765 ◽  
Author(s):  
Yin Xing ◽  
Jianping Yue ◽  
Chuang Chen ◽  
Yunfei Xiang ◽  
Yang Chen ◽  
...  
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Fitness Function ◽  
Deep Belief Network ◽  
Structure Parameters ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Belief Network ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

scholarly journals Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity

2020 ◽  
Vol 17 (16) ◽  
pp. 5906
Author(s):  
Chien-Cheng Jung ◽  
Wan-Yi Lin ◽  
Nai-Yun Hsu ◽  
Chih-Da Wu ◽  
Hao-Ting Chang ◽  
...  
Keyword(s):  
Prediction Model ◽  
Human Activities ◽  
Cross Validation ◽  
Prediction Models ◽  
Multiple Linear Regression Model ◽  
Outdoor Air ◽  
The Impact ◽  
Indoor Pm2.5 ◽  
Concentration Prediction ◽  
Pm2.5 Concentration

Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations.

PM2.5 concentration modeling and prediction by using temperature-based deep belief network

2021 ◽  
Vol 133 ◽  
pp. 157-165
Author(s):  
Haixia Xing ◽  
Gongming Wang ◽  
Caixia Liu ◽  
Minghe Suo
Keyword(s):  
Deep Belief Network ◽  
Belief Network ◽  
Modeling And Prediction ◽  
Pm2.5 Concentration

scholarly journals Motion Key frame Extraction Based on Grey Wolf Optimization Algorithm

2018 ◽  
Vol 232 ◽  
pp. 03032
Author(s):  
Yi Zhang ◽  
Juan Li ◽  
Min Zhang
Keyword(s):  
Fitness Function ◽  
Reconstruction Error ◽  
Human Motion ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Key Frame Extraction ◽  
Key Frame ◽  
Motion Data ◽  
Key Frames ◽  
Original Motion

In order to extract the key frames more effectively, we propose a key frame extraction method for human motion sequences based on Grey Wolf Optimization (GWO) algorithm. The fitness function is defined with the minimum reconstruction error and the optimal compression rate. The social hierarchy of grey wolves and hunting strategy are simulated to search key frames. Experimental results show that the proposed method can not only maintain the consistency of key frames between similar human motion sequences, but also effectively compress and summarize the original motion data. Under the same compression ratio, the reconstruction error is the minimum.

An Efficient Hybrid Grey Wolf Optimization Based KELM Approach for Prediction of the Discharge Coefficient of Submerged Radial Gates

2021 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Saman Shahnazi ◽  
Arman Alirezazadeh Sadaghiani
Keyword(s):  
Discharge Coefficient ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Gaussian Process Regression ◽  
Support Vector ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Experimental Database ◽  
Learning Machine ◽  
Downstream Flow

Abstract Radial gates are widely used hydraulic structures for flow control in irrigation canals. Accurately prediction of discharge coefficient through radial gates is considered as a challenging hydraulic subject, particularly under highly submerged flow conditions. Incurring the advantages of Kernel-depend Extreme Learning Machine (KELM), this study offers a Grey Wolf Optimization-based KELM (GWO-KELM) for effective prediction of discharge coefficient through submerged radial gates. Additionally, Support Vector Machine (SVM), and Gaussian Process Regression (GPR) methods are also presented for comparative purposes. To build prediction models using GWO-KELM, GPR, and SVM an extensive experimental database was established, consisting of 2125 data samples gathered by the US Bureau of Reclamation. From simulation results, it is observed that the proposed GWO-KELM approach with input parameters of the ratio of the downstream flow depth to the gate opening (y3/w) and submergence ratio (y1-y3/w) provides the best performance with the correlation coefficient (R) of 0.983, the Determination Coefficient (DC) of 0.966 and the Root Mean Squared Error (RMSE) of 0.027. Furthermore, the obtained results showed that the employed kernel-depend methods are capable of a statistically predicting the discharge coefficient under varied submergence conditions with satisfactory level of accuracy.

scholarly journals Durability Prediction Method of Concrete Soil Based on Deep Belief Network

2022 ◽  
Vol 2022 ◽  
pp. 1-7
Author(s):  
Xiao Tian ◽  
Niankun Zhu
Keyword(s):  
Environmental Conditions ◽  
Concrete Structure ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Chloride Ions ◽  
Deep Belief Network ◽  
Concrete Durability ◽  
Belief Network ◽  
Structural Durability ◽  
Durability Prediction

To truly reflect the durability characteristics of concrete subjected to multiple factors under complex environmental conditions, it is necessary to discuss the prediction of its durability. In response to the problem of durability prediction of traditional concrete structures, there is a low prediction accuracy, and the predicted time is long, and a concrete structural durability prediction method based on the deep belief network is proposed. The influencing factors of the concrete structural durability parameters are analyzed by two major categories of concrete material and external environmental conditions, and the transmission of chloride ions in the concrete structure is described. According to the disconnection of the steel bars, the durability of the concrete structure is started, and the determination is determined. The concrete structural antiflexural strength, using a deep belief network training concrete structural antiflexural strength judgment data, constructs a concrete structural durability predictive model and completes the durability prediction of the concrete structure based on the deep belief network. The proposed prediction method based on the deep belief network has a high prediction accuracy of 98% for the durability of concrete column structures. The simulation results show that the concrete structural durability’s prediction accuracy is high and the prediction time is short. The prediction of concrete durability discussed here has important guiding significance for the improvement of concrete durability test methods and the improvement of concrete durability evaluation standards in China.

scholarly journals An Ensemble Deep Belief Network Model Based on Random Subspace for NOx Concentration Prediction

ACS Omega ◽  
2021 ◽  
Vol 6 (11) ◽  
pp. 7655-7668
Author(s):  
Yingnan Wang ◽  
Guotian Yang ◽  
Ruibiao Xie ◽  
Han Liu ◽  
Kai Liu ◽  
...  
Keyword(s):  
Network Model ◽  
Deep Belief Network ◽  
Random Subspace ◽  
Belief Network ◽  
Model Based ◽  
Concentration Prediction

Evaluation of Grey Wolf Optimization Algorithm on Rigid and Flexible Receptor Docking

2020 ◽  
Author(s):  
Kin Meng Wong ◽  
Shirley Siu
Keyword(s):  
Scoring Function ◽  
Ligand Docking ◽  
Receptor Protein ◽  
Pose Prediction ◽  
Grey Wolf ◽  
Success Rates ◽  
Autodock Vina ◽  
Grey Wolf Optimization ◽  
Structure Based Drug Design ◽  
Docking Program

Protein-ligand docking programs are indispensable tools for predicting the binding pose of a ligand to the receptor protein in current structure-based drug design. In this paper, we evaluate the performance of grey wolf optimization (GWO) in protein-ligand docking. Two versions of the GWO docking program – the original GWO and the modified one with random walk – were implemented based on AutoDock Vina. Our rigid docking experiments show that the GWO programs have enhanced exploration capability leading to significant speedup in the search while maintaining comparable binding pose prediction accuracy to AutoDock Vina. For flexible receptor docking, the GWO methods are competitive in pose ranking but lower in success rates than AutoDockFR. Successful redocking of all the flexible cases to their holo structures reveals that inaccurate scoring function and lack of proper treatment of backbone are the major causes of docking failures.

Sign in / Sign up

Export Citation Format

Share Document