scholarly journals Analysis of Influencing Factors of PM2.5 Concentration and Design of a Pollutant Diffusion Model Based on an Artificial Neural Network in the Environment of the Internet of Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Sumin Li ◽  
Xiuqin Pan ◽  
Qian Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Diffusion Model ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Model Based ◽  
Artificial Neural ◽  
Vehicle Network ◽  
Pollutant Diffusion

With the development of the automobile industry, artificial intelligence, big data, 5G, and other technologies, the Internet of Vehicles (IoV) industry has entered a stage of rapid development. In this paper, a pollutant diffusion model based on an artificial neural network is designed in the context of a vehicle network. The application of artificial neural networks in haze prediction is studied. This paper first analyzes the causes and influencing factors of haze and selects the most representative and relatively large meteorological factors from temperature, wind, relative humidity, and several pollutant factors. Through training and simulation, a haze prediction model in the Beijing, Tianjin, and Hebei regions of China is established. Finally, according to the collected meteorological data, the pollutant diffusion model is established. The model is deduced by a standard mathematical formula, which makes the prediction results more accurate and rigorous, and the main conclusions and feasible scientific suggestions are obtained. The simulation results show that the method is effective. By strengthening the service system of the IoV, meteorological services can be more intelligent, and the information acquisition and service ability of the vehicle network can be effectively improved.

scholarly journals Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network

2021 ◽  
Vol 163 ◽  
pp. 113782
Author(s):  
Eduardo Ramos-Pérez ◽  
Pablo J. Alonso-González ◽  
José Javier Núñez-Velázquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Model Based ◽  
Artificial Neural

scholarly journals Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Tuan Vu Dinh ◽  
Hieu Nguyen ◽  
Xuan-Linh Tran ◽  
Nhat-Duc Hoang
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Soil Erosion ◽  
Differential Evolution ◽  
Influencing Factors ◽  
Support Vector ◽  
Adaptive Differential Evolution ◽  
Artificial Neural

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

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