Evaluation of Travel Security Early Warnings by Applying TOPSIS with Information Entropy Weighting and Positive Research

A comprehensive evaluation index system for travel security early warnings consists of three components,namely the frequency of tourism destination disasters,safety levels of facilities and the regional safety and security situation.The index system is composed of ten indexes including the frequency of hydrological and meteorological disasters,frequency of seismic-geological hazard accidents,frequency of disease epidemics,and others.The information entropy method,one of the fixed objective-weighting methods,is applied to determine the indexes' weight(namely entropy weight) influence on the characters and distribution of sample data.Entropy weighting overcomes the subjectivity of the Delphi method.The entropy weight can also be used to judge the indexes' importance,and to classify and order them.It is helpful to be able to put forward effective measures to improve tourism security.The Technique for Order Preference by Similarity to Idea Solution(TOPSIS) method,choosing the compromise solution(namely the relative similarity to the positive ideal solution(PIS)) with the shortest distance from the(PIS) and the farthest distance from the negative ideal solution(NIS),is applied to tourism security evaluations and early-warnings in this study.The preferred orders can be obtained and classified according to relative similarity to the PIS.The entropy weight TOPSIS(EW-TOPSIS) is characterized by simple calculations and discernible mathematical principles,and can be applied successfully to small and moderate sample sizes.The calculated results are stable and reproducible.Two case studies show that the results from applying EW-TOPSIS are correct,reliable and valid.The EW-TOPSIS can not only verify the known samples completely,but also accurately judge and distinguish the unknown samples.With critical-value samples,the various ranges of similarity to PIS for different tourism security levels are also obtained,and can conveniently assist in distinguishing the unknown levels of the samples.A Neural network(NN) is a data-driven and nonlinear modeling tool usually requiring a large enough sample.There are many factors and guidelines that need to be determined,judged and followed,which can affect the performance of a NN.The design of a NN is more of an art than a science.In NN modeling,over-learning inevitably occurs in the training process because too large a topology is used with a small size sample.Even if a much tighter topology with only two or no neurons in the hidden layer is used,the training-sample error is readily very low,but the verification-sample error is quite large.Without a verification sample,it is very difficult to determine when it is best to stop training to avoid the over-learning problem and to assess the model's ability to generalize.The results of the unknown sample data are unstable and the tourism security level may be given as excellent,just good,or only pass,indicating that the generalizations of these established NN models are very poor and cannot be applied in practice.For the evaluation of a tourism security situation and early-warning with small and moderate sample size,the EW-TOPSIS approach is practically suited and valid.Consequently,it is unnecessary and impossible in practice to use NN technology,which is usually applied to large-sample sizes.To use a NN for evaluating a problem,it is important to understand the limitations of NNs,what they can do well and what they cannot do.