Research on Community Risk Prediction Model and Management Based on Deep Learning

• Published in: Wireless communications and mobile computing Vol. 2022; pp. 1 - 9
• Main Author: Yin, Weida
• Format: Journal Article
• Language: English
• Published: Oxford Hindawi 11.03.2022; Hindawi Limited
• Subjects: Accuracy; Artificial neural networks; Correlation coefficients; Datasets; Deep learning; Mean square errors; Model accuracy; Neural networks; Prediction models; Public safety; Regression models; Risk; Root-mean-square errors
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2022/2373284

The influencing factors of community risk are complex. For the low accuracy of traditional prediction model, a multichannel convolutional neural network community risk prediction model is proposed by improving convolutional neural network of deep learning. First of all, in the community risk prediction model, the structure of multichannel input convolutional neural network is selected. Then, add it into the full connection layer. Subsequently, the DenseNet layer is added to establish connections between different network layers. Finally, the receptive field is improved, and the gradient disappearance is solved. Thus, the prediction accuracy of model is improved. Compared with the traditional model, the proposed multichannel convolutional neural network model has better prediction accuracy. In addition, it performs better on the three indicators, namely, correlation coefficient R, coefficient of determination R2, and mean square root error RMSE. Compared with the commonly used LSTM model and logic regression model, the proposed model also has certain advantages, which is more suitable for community risk prediction.