Effectiveness of hybrid ensemble machine learning models for landslide susceptibility analysis:Evidence from Shimla district of North-west Indian Himalayan region

The Indian Himalayan region is frequently experiencing climate change-induced landslides.Thus,landslide susceptibility assessment assumes greater significance for lessening the impact of a landslide hazard.This paper makes an attempt to assess landslide susceptibility in Shimla district of the northwest Indian Himalayan region.It examined the effectiveness of random forest(RF),multilayer perceptron(MLP),sequential minimal optimization regression(SMOreg)and bagging ensemble(B-RF,BSMOreg,B-MLP)models.A landslide inventory map comprising 1052 locations of past landslide occurrences was classified into training(70%)and testing(30%)datasets.The site-specific influencing factors were selected by employing a multicollinearity test.The relationship between past landslide occurrences and influencing factors was established using the frequency ratio method.The effectiveness of machine learning models was verified through performance assessors.The landslide susceptibility maps were validated by the area under the receiver operating characteristic curves(ROC-AUC),accuracy,precision,recall and F1-score.The key performance metrics and map validation demonstrated that the BRF model(correlation coefficient:0.988,mean absolute error:0.010,root mean square error:0.058,relative absolute error:2.964,ROC-AUC:0.947,accuracy:0.778,precision:0.819,recall:0.917 and F-1 score:0.865)outperformed the single classifiers and other bagging ensemble models for landslide susceptibility.The results show that the largest area was found under the very high susceptibility zone(33.87%),followed by the low(27.30%),high(20.68%)and moderate(18.16%)susceptibility zones.The factors,namely average annual rainfall,slope,lithology,soil texture and earthquake magnitude have been identified as the influencing factors for very high landslide susceptibility.Soil texture,lineament density and elevation have been attributed to high and moderate susceptibility.Thus,the study calls for devising suitable landslide mitigation measures in the study area.Structural measures,an immediate response system,community participation and coordination among stakeholders may help lessen the detrimental impact of landslides.The findings from this study could aid decision-makers in mitigating future catastrophes and devising suitable strategies in other geographical regions with similar geological characteristics.

Hybrid stacking ensemble algorithm and simulated annealing optimization for stability evaluation of underground entry-type excavations

The stability of underground entry-type excavations(UETEs)is of paramount importance for ensuring the safety of mining operations.As more engineering cases are accumulated,machine learning(ML)has demonstrated great potential for the stability evaluation of UETEs.In this study,a hybrid stacking ensemble method aggregating support vector machine(SVM),k-nearest neighbor(KNN),decision tree(DT),random forest(RF),multilayer perceptron neural network(MLPNN)and extreme gradient boosting(XGBoost)algorithms was proposed to assess the stability of UETEs.Firstly,a total of 399 historical cases with two indicators were collected from seven mines.Subsequently,to pursue better evaluation performance,the hyperparameters of base learners(SVM,KNN,DT,RF,MLPNN and XGBoost)and meta learner(MLPNN)were tuned by combining a five-fold cross validation(CV)and simulated annealing(SA)approach.Based on the optimal hyperparameters configuration,the stacking ensemble models were constructed using the training set(75%of the data).Finally,the performance of the proposed approach was evaluated by two global metrics(accuracy and Cohen’s Kappa)and three within-class metrics(macro average of the precision,recall and F1-score)on the test set(25%of the data).In addition,the evaluation results were compared with six base learners optimized by SA.The hybrid stacking ensemble algorithm achieved better comprehensive performance with the accuracy,Kappa coefficient,macro average of the precision,recall and F1-score were 0.92,0.851,0.885,0.88 and 0.883,respectively.The rock mass rating(RMR)had the most important influence on evaluation results.Moreover,the critical span graph(CSG)was updated based on the proposed model,representing a significant improvement compared with the previous studies.This study can provide valuable guidance for stability analysis and risk management of UETEs.However,it is necessary to consider more indicators and collect more extensive and balanced dataset to validate the model in future.

Ensemble hybrid machine learning methods for gully erosion susceptibility mapping: K-fold cross validation approach

Gully erosion is one of the important problems creating barrier to agricultural development.The present research used the radial basis function neural network(RBFnn)and its ensemble with random sub-space(RSS)and rotation forest(RTF)ensemble Meta classifiers for the spatial mapping of gully erosion susceptibility(GES)in Hinglo river basin.120 gullies were marked and grouped into four-fold.A total of 23 factors including topographical,hydrological,lithological,and soil physio-chemical properties were effectively used.GES maps were built by RBFnn,RSS-RBFnn,and RTF-RBFnn models.The very high susceptibility zone of RBFnn,RTF-RBFnn and RSS-RBFnn models covered 6.75%,6.72%and 6.57%in Fold-1,6.21%,6.10%and 6.09%in Fold-2,6.26%,6.13%and 6.05%in Fold-3 and 7%,6.975%and 6.42%in Fold-4 of the basin.Receiver operating characteristics(ROC)curve and statistical techniques such as mean-absolute-error(MAE),root-mean-absolute-error(RMSE)and relative gully density area(R-index)methods were used for evaluating the GES maps.The results of the ROC,MAE,RMSE and R-index methods showed that the models of susceptibility to gully erosion have excellent predictive efficiency.The simulation results based on machine learning are satisfactory and outstanding and could be used to forecast the areas vulnerable to gully erosion.

FORECASTING CHINA'S FOREIGN TRADE VOLUME WITH A KERNEL-BASED HYBRID ECONOMETRIC-AI ENSEMBLE LEARNING APPROACH

Due to the complexity of economic system and the interactive effects between all kinds of economic variables and foreign trade, it is not easy to predict foreign trade volume. However, the difficulty in predicting foreign trade volume is usually attributed to the limitation of many conventional forecasting models. To improve the prediction performance, the study proposes a novel kernel-based ensemble learning approach hybridizing econometric models and artificial intelligence (AI) models to predict China's foreign trade volume. In the proposed approach, an important econometric model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of all kinds of economic variables on Chinese foreign trade from a multivariate linear anal- ysis perspective. Then an artificial neural network (ANN) based EC-VAR model is used to capture the nonlinear effects of economic variables on foreign trade from the nonlinear viewpoint. Subsequently, for incorporating the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also integrated into the nonlinear ANN-based EC-VAR model. Finally, all kinds of economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as input variables of a typical kernel-based support vector regression (SVR) for en- semble prediction purpose. For illustration, the proposed kernel-based ensemble learning methodology hybridizing econometric techniques and AI methods is applied to China's foreign trade volume predic- tion problem. Experimental results reveal that the hybrid econometric-AI ensemble learning approach can significantly improve the prediction performance over other linear and nonlinear models listed in this study.

A hybrid biometric identification framework for high security applications

Research on biometrics for high security applica- tions has not attracted as much attention as civilian or foren- sic applications. Limited research and deficient analysis so far has led to a lack of general solutions and leaves this as a challenging issue. This work provides a systematic analy- sis and identification of the problems to be solved in order to meet the performance requirements for high security applica- tions, a double low problem. A hybrid ensemble framework is proposed to solve this problem. Setting an adequately high threshold for each matcher can guarantee a zero false accep- tance rate （FAR） and then use the hybrid ensemble framework makes the false reject rate （FRR） as low as possible. Three ex- periments are performed to verify the effectiveness and gener- alization of the framework. First, two fingerprint verification algorithms are fused. In this test only 10.55% of fingerprints are falsely rejected with zero false acceptance rate, this is sig- nificantly lower than other state of the art methods. Second, in face verification, the framework also results in a large re- duction in incorrect classification. Finally, assessing the per- formance of the framework on a combination of face and gait verification using a heterogeneous database show this frame- work can achieve both 0% false rejection and 0% false accep- tance simultaneously.