ANN Model for the Estimation of Life Casualties in Earthquake Engineering

Wang, Hai xia; Niu, Jin xin; Wu, Jiang feng

doi:10.1016/j.sepro.2011.08.010

Cited by 18 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANN model has been developed to estimate life casualties in earthquake prone area where it applies eight parameters including earthquake magnitude, depth of hypocenter, intensity of epicenter, level of preparedness, earthquake acceleration, population to form, and disaster forecasting rate. The casualties' prediction value, resulted by the ANN model, shows 20% error compared with the experimental value 26) .…”

Section: Literature Studymentioning

confidence: 90%

Determining the Prioritized Victim of Earthquake Disaster Using Fuzzy Logic and Decision Tree Approach

Berawi¹,

Siahaan²,

Gunawan³

et al. 2020

Evergreen

View full text Add to dashboard Cite

The purpose of this study is twofold: to create the foundation for the application development capable to connect rescuers to the victims of earthquake disaster and to determine who needs help the most. To achieve these objectives, the fuzzy expert method, decision tree, and expert knowledge bases are used. The results from fuzzy logic and decision tree analysis show that there is a high accuracy in the decision used to determine the prioritized victims that urgently need to be rescued during emergency response phase of disaster management.

show abstract

Section: Literature Studymentioning

confidence: 90%

Determining the Prioritized Victim of Earthquake Disaster Using Fuzzy Logic and Decision Tree Approach

Berawi¹,

Siahaan²,

Gunawan³

et al. 2020

Evergreen

View full text Add to dashboard Cite

show abstract

“…e model examined the key factors, such as earthquake magnitude, focal depth, epicenter intensity, disaster preparedness level, earthquake acceleration, population density, and disaster prediction, and used 37 severe earthquake disasters to train the network. e results show that the model is applicable to most earthquake situations [5]. Zhang et al proposed to use the grey discrete Verhulst model to predict the drug supplies for emergency rescue of large-scale earthquake disasters [2].…”

Section: Forecasting the Wounded In Massive Earthquakementioning

confidence: 99%

“…According to (5), the grey prediction model of upper and lower bounds of interval grey number ⊗ (k + 1) ∈ [a(k), b(k)] can be obtained.…”

Section: Prediction Model Of Upper and Lower Bounds Of Continuous Interval Grey Numbermentioning

confidence: 99%

Forecasting the Number of the Wounded after an Earthquake Disaster Based on the Continuous Interval Grey Discrete Verhulst Model

Zhang

Wang

Chang

et al. 2021

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Earthquake disaster causes serious casualties, so the prediction of casualties is conducive to the reasonable and efficient allocation of emergency relief materials, which plays a significant role in emergency rescue. In this paper, a continuous interval grey discrete Verhulst model based on kernels and measures (CGDVM-KM), different from the previous forecasting methods, can help us to efficiently predict the number of the wounded in a very short time, that is, an “S-shape” curve for the numbers of the sick and wounded. That is, the continuous interval sequence is converted into the kernel and measure sequences with equal information quantity by the interval whitening method, and it is combined with the classical grey discrete Verhulst model, and then the grey discrete Verhulst models of the kernel and measure sequences are presented, respectively. Finally, CGDVM-KM is developed. It can effectively overcome the systematic errors caused by the discrete form equation for parameter estimation and continuous form equation for simulation and prediction in classical grey Verhulst model, so as to improve the prediction accuracy. At the same time, the rationality and validity of the model are verified by examples. A comparison with other forecasting models shows that the model has higher prediction accuracy and better simulation effect in forecasting the wounded in massive earthquake disasters.

show abstract

“…Wang [50] uses a genetic algorithm based on GM (1,1) and Fourier series to predict food demand after snow disasters. Wang et al [48] build a back propagation (BP) neutral network model to forecast earthquake casualties, by considering earthquake magnitude, depth of hypocenter, intensity of epicenter, level of preparedness, earthquake acceleration, population density, and disaster forecasting as the experimental features. Dogan and Akgungor [10] use nonlinear multiple regression (NLMR) and artificial neural network (ANN) methods to predict road injuries in Turkey.…”

Section: Literature Reviewmentioning

confidence: 99%

An XGBoost-based casualty prediction method for terrorist attacks

Feng

Wang

Yin

et al. 2020

Complex Intell. Syst.

View full text Add to dashboard Cite

Terrorist attacks have been becoming one of the severe threats to national public security and world peace. Ascertaining whether the behaviors of terrorist attacks will threaten the lives of innocent people is vital in dealing with terrorist attacks, which has a profound impact on the resource optimization configuration. For this purpose, we propose an XGBoost-based casualty prediction algorithm, namely RP-GA-XGBoost, to predict whether terrorist attacks will cause the casualties of innocent civilians. In the proposed RP-GA-XGBoost algorithm, a novel method that incorporates random forest (RF) and principal component analysis (PCA) is devised for selecting features, and a genetic algorithm is used to tune the hyperparameters of XGBoost. The proposed method is evaluated on the public dataset (Global Terrorism Database, GTD) and the terrorist attack dataset in China. Experimental results demonstrate that the proposed algorithm achieves area under curve (AUC) of 87.00%, and accuracy of 86.33% for the public dataset, and sensitivity of 94.00%, AUC of 94.90% for the terrorist attack dataset in China, which proves the superiority and higher generalization ability of the proposed algorithm. Our study, to the best of our knowledge, is the first to apply machine learning in the management of terrorist attacks, which can provide early warning and decision support information for terrorist attack management.

show abstract

ANN Model for the Estimation of Life Casualties in Earthquake Engineering

Cited by 18 publications

References 0 publications

Determining the Prioritized Victim of Earthquake Disaster Using Fuzzy Logic and Decision Tree Approach

Determining the Prioritized Victim of Earthquake Disaster Using Fuzzy Logic and Decision Tree Approach

Forecasting the Number of the Wounded after an Earthquake Disaster Based on the Continuous Interval Grey Discrete Verhulst Model

An XGBoost-based casualty prediction method for terrorist attacks

Contact Info

Product

Resources

About