Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques

Improving the accuracy of flood prediction and mapping is crucial for reducing damage resulting from flood events.In this study,we proposed and validated three ensemble models based on the Best First Decision Tree(BFT)and the Bagging(Bagging-BFT),Decorate(Bagging-BFT),and Random Subspace(RSS-BFT)ensemble learning techniques for an improved prediction of flood susceptibility in a spatially-explicit manner.A total number of 126 historical flood events from the Nghe An Province(Vietnam)were connected to a set of 10 flood influencing factors(slope,elevation,aspect,curvature,river density,distance from rivers,flow direction,geology,soil,and land use)for generating the training and validation datasets.The models were validated via several performance metrics that demonstrated the capability of all three ensemble models in elucidating the underlying pattern of flood occurrences within the research area and predicting the probability of future flood events.Based on the Area Under the receiver operating characteristic Curve(AUC),the ensemble Decorate-BFT model that achieved an AUC value of 0.989 was identified as the superior model over the RSS-BFT(AUC=0.982)and Bagging-BFT(AUC=0.967)models.A comparison between the performance of the models and the models previously reported in the literature confirmed that our ensemble models provided a reliable estimate of flood susceptibilities and their resulting susceptibility maps are trustful for flood early warning systems as well as development of mitigation plans.

An Adaptive-Feature Centric XGBoost Ensemble Classifier Model for Improved Malware Detection and Classification

Machine learning(ML)is often used to solve the problem of malware detection and classification,and various machine learning approaches are adapted to the problem of malware classification;still acquiring poor performance by the way of feature selection,and classification.To address the problem,an efficient novel algorithm for adaptive feature-centered XG Boost Ensemble Learner Classifier“AFC-XG Boost”is presented in this paper.The proposed model has been designed to handle varying data sets of malware detection obtained from Kaggle data set.The model turns the XG Boost classifier in several stages to optimize performance.At preprocessing stage,the data set given has been noise removed,normalized and tamper removed using Feature Base Optimizer“FBO”algorithm.The FBO would normalize the data points,as well as perform noise removal according to the feature values and their base information.Similarly,the performance of standard XG Boost has been optimized by adapting the selection using Class Based Principle Component Analysis“CBPCA”algorithm,which performs the selection according to the fitness of any feature for different classes.Based on the selected features,the method generates a regression tree for each feature considered.Based on the generated trees,the method performs classification by computing the tree-level ensemble similarity‘TLES’and the class-level ensemble similarity‘CLES’.Using both methods calculates the value of the class match similarity‘CMS’based on which the malware has been classified.The proposed approach achieves 97% accuracy in malware detection and classification with the less time complexity of 34 s for 75000 samples.

Tuna classification using super learner ensemble of region-based CNN-grouped 2D-LBP models

Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food.Separation of Tuna into their species is done in industries manually,and the process is tiresome.This work proposes an automated system for classifying Tuna spe-cies based on their images.An ensemble of region-based deep neural networks is used.A sub region contrast stretching operation is applied to enhance the images.Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks(CNN).After fine-tuning the models,the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor(G2DLBP).Statistical features from the descriptor are applied to different classifiers,and the best clas-sifier for each image region model is identified.Different ensemble methods are subse-quently used to combine the three CNN-G2DLBP models.Among the ensemble techniques,super learner ensemble method with random forest(RF)classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%.The perfor-mance of different ensemble methods is analyzed in terms of accuracy,precision,recall,and f-score.The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset.An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.

Correlation between travel experiences and post-COVID outbound tourism intention:a case study from China

The COVID-19 pandemic has devastated global tourism and recovery is proceeding very slowly.For many countries,tourism served as a major economic sector,so investigating how to recover is essential.As China was the largest source of outbound travelers before the outbreak,study of the factors influencing Chinese intentions to travel overseas in the post-COVID era is revealing.In Apr.2022,among seven provinces(or cities)with the most outbound tourists from 2019 to 2021,2450 individuals responded to a questionnaire on daily mobility,tourism experiences,and the shifts due to the pandemic.Light gradient boosting machine(LightGBM),a robust ensemble learning method,was adopted to quantify and visualize the impact of explanatory factors on outbound travel intention.In addition,the Optuna mechanism and Shapley additive explanation(SHAP)instruments were employed for tuning hyperparameters and interpreting results,respectively.Findings suggest neither one-day nor multi-day tours have resumed to pre-COVID levels.Higher frequency of multi-day tours with further destinations,less car utilization in daily shopping trips,and moderate pandemic restrictions can boost the intention to travel abroad.The concerns and desires of different age groups for overseas travel need different responses.This study reveals the factors affecting Chinese outbound travel intentions and provides suggestions for the recovery of tourism in the post-COVID period.