Risk Assessment and Prediction of Important Transmission Channel Based on Water Wave Optimization-Factor Analysis-Long and Short-Term Memory Network

Risk assessment and prediction of important transmission channels are of guiding significance for condition based maintenance and line operation and maintenance. However, it is difficult to adjust parameters artificially and the prediction accuracy is low when traditional long and short-term memory (abbr. LSTM) network is used to predict line risk. For this reason, an accurate risk assessment and fast prediction method for important transmission channel based on water wave optimization-factor analysis-long and short-term memory (abbr. WWO-FA-LSTM) was proposed. Firstly, by means of leading in Levy distribution, Gauss-Cauchy mutation operator and linearly decreasing wave height the WWO was improved. Secondly, the multi-dimensional disaster causing factors in the assessment area were obtained, and after the factor analysis and dimensionality reduction these factors were regarded as network input, and considering the hazard inducing environmental sensitivity and vulnerability of disaster bearing body the risk index RC was calculated and taken as the network output. By means of improved WWO the LSTM was continuously optimized to obtain the most optimal LSTM model. Finally, by use of the most optimized LSTM model the risk prediction for important transmission channel was conducted. Results of risk prediction show that it is more accurate to assess the risk by the proposed model, and comparing with traditional methods the error from the model-based prediction is lower, so it is suitable to the risk assessment and prediction of transmission channels.