Over the last decades,the development of Geographic Information System(GIS) technology has provided a method for the evaluation of landslide hazard.Through the use of direct-reverse DEM technology,the Changshougou valley is divided into 216 slope units,which includes 123 landslide units.According to mechanism analyses of landslides in the study area,six environmental factors are selected to evaluate the landslide occurrence,such as elevation,slope,aspect,curvature,distance to rivers,and human activities.Each factor is extracted in terms of slope unit within the scope of ArcGIS.The spatial analysis shows that most of landslides in the Changshougou valley are located at the elevation ranging from 100 to 150 m,with an aspect of 135°~225° and 40°~60° in slope,and on convex slopes,which are also influenced by hydrological processing and human activities.After the spatial analysis of environmental factors,this paper presents a case study for landslide hazard prediction,using back-propagation artificial neural network modeling optimized by genetic algorithms.Parameters of genetic algorithms and neural networks are set.The population size is 100,crossover probability 0.65,mutation probability 0.01,momentum factor 0.60,learning rate 0.7,max learning number 10000,and target error 0.000001.From a database of 216 landslides,120 landslides are used for training neural network models,and 96 landslides are used for the validation of landslide susceptibility.Comparing landslide presence with a susceptibility map,it is noted that the prediction accuracy of landslide occurrence is 93.02%,while the units without landslide occurrence is predicted with accuracy of 81.13%.The verification shows satisfactory agreement with accuracy of 86.46% between the susceptibility map and the landslide locations.In this case study,it is also found that some disadvantages can be overcome in the application of BP neural networks,for example,the low convergence rate and local minimum,after the optimization is carried out using genetic algorithm.In conclusion,we note that genetic algorithm-BP neural networks are an effective method to predict landslide hazard with high accuracy.

Application of back propagation neural networks with optimization of genetic algorithms to landslide hazard prediction

In response to the relatively huge error, low efficiency, and low accuracy in the prediction and analysis data on the quality of multimedia professional talents at present, an analytical method for the prediction of the quality of multimedia professional talents based on the multiobjective data fuzzy evolutionary algorithm is proposed in this paper. The weights of the talent quality data are analyzed in detail mainly based on the multiobjective data fuzzy evolution algorithm to establish a prediction and analysis model for the quality of multimedia professional talents. This model can be used to implement the prediction and analysis of the comprehensive abilities of multimedia professional talents effectively. Finally, through a practical case study, it is verified that the method put forward in this paper has high efficiency and high accuracy in predicting and analyzing the quality of multimedia professional talents.

/pdf/prediction-and-analysis-of-the-quality-of-multimedia-3m6ugebpdq.pdf

Prediction and Analysis of the Quality of Multimedia Professional Talents Combining Multiobjective Data Fuzzy Evolution

A BP artificial neural network model was developed to predict monthly mean daily global solar radiation of Lanzhou by using the meteorological observation dataThe model used the interpretation diagram method and connection weights method to compute the relative contribution of each input variable for estimating the importance to the output variableThen,the redundant input variables can be eliminatedThe measured meteorology values from 1971 to 1995 for Lanzhou station were used for the training of the networkThen we can predict the solar radiation from 1996 to 2000 with the simplified networkThe measured values of monthly mean daily global solar radiation for Lanzhou station were analyzed to test the network modelThe experimental results indicate that the method can increase the transparency of model and improve the reliability and robustness of the modelThe model has the smaller error and the higher precisionThe coefficient of determination is 0987Results have shown better agreement between the estimated and measured values of global solar radiation than other empirical modelsTherefore,using the BP neural network model with the meteorological observation data is a very effective method to predict the solar radiation of some region which has no radiation observation sites

Simulation of solar radiation in Lanzhou based on BP neural network

Analysis of Mission-Oriented Aeronautic Spare Parts Demand Prediction

In the current talent model analysis, the phenomenon occurs such as serious errors, low efficiency, and poor accuracy. Based on this, a talent training model evaluation method is proposed based on multi-constraint model knowledge recognition algorithm. The knowledge recognition algorithm of the multiple constraint model is used to establish a talent training mode evaluation based on the knowledge recognition algorithm of the multiple constraint model, and the mode weight value is used to adjust the connection weights of the knowledge recognition algorithm of the multiple constraint model. The proposed knowledge recognition algorithm of the multiple constraint model is used to analyze the data index weights of the medical professional talent model in detail, to establish the appraisal model of the talent training. The model can effectively complete the evaluation and analysis of the comprehensive ability of medical professional talents. When using the classifier for training, 76% of the data feature dimensions can be selected for dimension reduction, so as to improve the imbalance problem under the training sample. The characteristics of the various constraint models designed in this paper can be tested by using the classifier. Finally, the experimental analysis results show that the application of the knowledge recognition algorithm of the multiple constraint model to the training mode of medical professionals can significantly enhance the accuracy of evaluation. It has certain reference significance in personnel training.

https://downloads.hindawi.com/journals/mpe/2022/4507246.pdf

Evaluation of Talent Training Model Taking into Account the Knowledge Recognition Algorithm of Multiple Constraint Models

Air material demand forecast is an important content of air material management, How to scientifically determine the general aircraft material demand has always been a key research subject of general aviation enterprise. Consider the general aircraft material demand forecast problem, forecast method is proposed by using Modified Particle Swarm Optimization (MPSO) algorithm to optimize the BP neural network. Firstly analyzed the main influence factors of general aircraft material demand, then introduces the basic principle of BP neural network and PSO algorithm and its improvement and MPSO-BP neural network forecast model is constructed, finally case analysis is carried out by using historical data of general aviation enterprise. The results show that the model prediction accuracy is improved effectively and good results have been achieved.

http://www.sersc.org/journals/IJMUE/vol11_no6_2016/16.pdf

General Aircraft Material Demand Forecast Based on Modified PSO Optimized BP Neural Network

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General Aircraft Material Demand Forecast Based on Modified PSO Optimized BP Neural Network