A stacking-based ensemble learning method for earthquake casualty prediction

Abstract The estimation of the loss and prediction of the casualties in earthquake-stricken areas are vital for making rapid and accurate decisions during rescue efforts. The number of casualties is determined by various factors, necessitating a comprehensive system for earthquake-casualty prediction. To obtain accurate prediction results, an effective prediction method based on stacking ensemble learning and improved swarm intelligence algorithm is proposed in this study, which comprises three parts: (1) applying multiple base learners for training, (2) using a stacking strategy to integrate the results generated by multiple base learners to obtain the final prediction results, and (3) developing an improved swarm intelligence algorithm to optimize the key parameters in the prediction model. To verify the effectiveness of the model, we collected data pertaining to earthquake destruction from 1966 to 2017 in China. Experiments were conducted to compare the proposed method with popular machine learning methods. It was found that the stacking ensemble learning method can effectively integrate the prediction results of the base learner to improve the performance of the model, and the improved swarm intelligence algorithm can further improve the prediction accuracy. Moreover, the importance of each feature was evaluated, which has important implications for future work such as casualty prevention and rescue during earthquakes.

[1]  Shen Yin,et al.  Tuning kernel parameters for SVM based on expected square distance ratio , 2016, Inf. Sci..

[2]  Francisco Herrera,et al.  A Review on Ensembles for the Class Imbalance Problem: Bagging-, Boosting-, and Hybrid-Based Approaches , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[3]  Seyed Abolghasem Mirroshandel,et al.  A novel method for predicting kidney stone type using ensemble learning , 2017, Artif. Intell. Medicine.

[4]  Rubén Urraca,et al.  Stacking ensemble with parsimonious base models to improve generalization capability in the characterization of steel bolted components , 2018, Appl. Soft Comput..

[5]  Sankaran Mahadevan,et al.  Ensemble machine learning models for aviation incident risk prediction , 2019, Decis. Support Syst..

[6]  Dursun Delen,et al.  A data analytics approach to building a clinical decision support system for diabetic retinopathy: Developing and deploying a model ensemble , 2017, Decis. Support Syst..

[7]  Gamal Attiya,et al.  Classification of human cancer diseases by gene expression profiles , 2017, Appl. Soft Comput..

[8]  V. S. Shankar Sriram,et al.  An efficient intrusion detection system based on hypergraph - Genetic algorithm for parameter optimization and feature selection in support vector machine , 2017, Knowl. Based Syst..

[9]  Wang Shaoyu,et al.  The prediction model of earthquake casuailty based on robust wavelet v-SVM , 2015, Natural Hazards.

[10]  Wenguo Weng,et al.  A scenario-based model for earthquake emergency management effectiveness evaluation , 2017 .

[11]  Yang Liu,et al.  Symptom severity classification with gradient tree boosting. , 2017, Journal of biomedical informatics.

[12]  Jiwen An,et al.  A quick earthquake disaster loss assessment method supported by dasymetric data for emergency response in China , 2016 .

[13]  Xin-She Yang,et al.  Firefly Algorithms for Multimodal Optimization , 2009, SAGA.

[14]  DelenDursun,et al.  A data analytics approach to building a clinical decision support system for diabetic retinopathy , 2017 .

[15]  Research on Evaluation Models and Empirical Analysis of Earthquake Disaster Losses in China , 2012 .

[16]  An Chen,et al.  Regional disaster risk evaluation of China based on the universal risk model , 2017, Natural Hazards.

[17]  Mustafa Erdik,et al.  Rapid Earthquake Loss Assessment After Damaging Earthquakes , 2011 .

[18]  Hui Wang,et al.  A new dynamic firefly algorithm for demand estimation of water resources , 2018, Inf. Sci..

[19]  Hai xia Wang,et al.  ANN Model for the Estimation of Life Casualties in Earthquake Engineering , 2011 .

[20]  Alípio Mário Jorge,et al.  Ensemble approaches for regression: A survey , 2012, CSUR.

[21]  Yanzhang Wang,et al.  A tree ensemble-based two-stage model for advanced-stage colorectal cancer survival prediction , 2019, Inf. Sci..

[22]  Iztok Fister,et al.  Memetic firefly algorithm for combinatorial optimization , 2012, 1204.5165.

[23]  Baiqing Sun,et al.  Study on earthquake risk reduction from the perspectives of the elderly , 2017 .

[24]  M. Gul,et al.  An artificial neural network-based earthquake casualty estimation model for Istanbul city , 2016, Natural Hazards.

[25]  Irena Koprinska,et al.  Multi-step forecasting for big data time series based on ensemble learning , 2019, Knowl. Based Syst..

[26]  Kamlesh Mistry,et al.  Feature selection using firefly optimization for classification and regression models , 2018, Decis. Support Syst..

[27]  Petter Næss,et al.  Applying gradient boosting decision trees to examine non-linear effects of the built environment on driving distance in Oslo , 2018 .

[28]  Sungzoon Cho,et al.  An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction , 2015, Expert Syst. Appl..

[29]  Mohamed Elhoseny,et al.  Feature selection based on artificial bee colony and gradient boosting decision tree , 2019, Appl. Soft Comput..

[30]  Semra Türkan,et al.  Modeling destructive earthquake casualties based on a comparative study for Turkey , 2014 .

[31]  Alain Abran,et al.  On the value of parameter tuning in heterogeneous ensembles effort estimation , 2017, Soft Computing.

[32]  Shaoze Cui,et al.  A review of emergency response in disasters: present and future perspectives , 2020, Natural Hazards.

[33]  Liu Xiao,et al.  BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification , 2016 .

[34]  Stav Shapira,et al.  An Integrated and Interdisciplinary Model for Predicting the Risk of Injury and Death in Future Earthquakes , 2016, PloS one.

[35]  Asim Imdad Wagan,et al.  Wind turbine micrositing by using the firefly algorithm , 2015, Appl. Soft Comput..

[36]  Yaochu Jin,et al.  An improved support vector machine-based diabetic readmission prediction , 2018, Comput. Methods Programs Biomed..

[37]  F. Sibel Salman,et al.  Deployment of field hospitals in mass casualty incidents , 2014, Comput. Ind. Eng..

[38]  Liu Xiao,et al.  Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data , 2016 .

[39]  Ning Wang,et al.  Application of interpretable machine learning models for the intelligent decision , 2019, Neurocomputing.

[40]  Ying Zhang,et al.  Adverse drug reaction detection on social media with deep linguistic features , 2020, J. Biomed. Informatics.

[41]  Jane Labadin,et al.  Applied Soft Computing , 2014 .

[42]  Jochen Schwarz,et al.  Estimation of Human Casualties from Earthquakes in Pakistan—An Engineering Approach , 2011 .

[43]  Hui Wang,et al.  Firefly algorithm with neighborhood attraction , 2017, Inf. Sci..

[44]  Abdelheq Guettiche,et al.  Economic and Human Loss Empirical Models for Earthquakes in the Mediterranean Region, with Particular Focus on Algeria , 2017, International Journal of Disaster Risk Science.

[45]  Hong-Bin Shen,et al.  Adaptive Firefly Algorithm: Parameter Analysis and its Application , 2014, PloS one.

[46]  Yixiang Huang,et al.  A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification , 2019, Comput. Methods Programs Biomed..

[47]  Sang Won Yoon,et al.  A support vector machine-based ensemble algorithm for breast cancer diagnosis , 2017, Eur. J. Oper. Res..