Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have dev...Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.展开更多
Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of meth...Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.展开更多
通过电能质量监测系统(power quality monitoring system,PQMS)中蕴含的电网历史故障变化、趋势等重要信息,对未来电压暂降进行预测,可为用户和电网公司合理规划生产,避免经济损失提供有力帮助。该文提出一种基于隐马尔可夫模型的电压...通过电能质量监测系统(power quality monitoring system,PQMS)中蕴含的电网历史故障变化、趋势等重要信息,对未来电压暂降进行预测,可为用户和电网公司合理规划生产,避免经济损失提供有力帮助。该文提出一种基于隐马尔可夫模型的电压暂降发生时间(occurrence time of voltage sag,OTVS)预测方法。首先对电压暂降发生时间的变量可预测性、数据冗余性、事件混沌性进行分析,揭示电压暂降监测数据特性;然后针对这三种特性,提出基于模糊C-均值聚类算法(fuzzy C-means algorithm,FCMA)和赤池信息准则(Akaike information criterion,AIC)的电压暂降历史状态识别与划分方法,以区间型变量刻画监测数据中的历史变化信息;建立考虑暂降历史变化信息和电网扰动变化信息的隐马尔可夫模型,实现对未来电压暂降的预测。最后,利用中部某省10个监测点的历史数据进行验证,所提方法的预测准确率最高可达92.85%,所提方法的预测性能较其他典型预测方法约高5%~30%。展开更多
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
文摘Traffic flow prediction,as the basis of signal coordination and travel time prediction,has become a research point in the field of transportation.For traffic flow prediction,researchers have proposed a variety of methods,but most of these methods only use the time domain information of traffic flow data to predict the traffic flow,ignoring the impact of spatial correlation on the prediction of target road segment flow,which leads to poor prediction accuracy.In this paper,a traffic flow prediction model called as long short time memory and random forest(LSTM-RF)was proposed based on the combination model.In the process of traffic flow prediction,the long short time memory(LSTM)model was used to extract the time sequence features of the predicted target road segment.Then,the predicted value of LSTM and the collected information of adjacent upstream and downstream sections were simultaneously used as the input features of the random forest model to analyze the spatial-temporal correlation of traffic flow,so as to obtain the final prediction results.The traffic flow data of 132 urban road sections collected by the license plate recognition system in Guiyang City were tested and verified.The results show that the method is better than the single model in prediction accuracy,and the prediction error is obviously reduced compared with the single model.
文摘通过电能质量监测系统(power quality monitoring system,PQMS)中蕴含的电网历史故障变化、趋势等重要信息,对未来电压暂降进行预测,可为用户和电网公司合理规划生产,避免经济损失提供有力帮助。该文提出一种基于隐马尔可夫模型的电压暂降发生时间(occurrence time of voltage sag,OTVS)预测方法。首先对电压暂降发生时间的变量可预测性、数据冗余性、事件混沌性进行分析,揭示电压暂降监测数据特性;然后针对这三种特性,提出基于模糊C-均值聚类算法(fuzzy C-means algorithm,FCMA)和赤池信息准则(Akaike information criterion,AIC)的电压暂降历史状态识别与划分方法,以区间型变量刻画监测数据中的历史变化信息;建立考虑暂降历史变化信息和电网扰动变化信息的隐马尔可夫模型,实现对未来电压暂降的预测。最后,利用中部某省10个监测点的历史数据进行验证,所提方法的预测准确率最高可达92.85%,所提方法的预测性能较其他典型预测方法约高5%~30%。
