Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
出版年份 2015 全文链接
标题
Feasibility of ANFIS model for prediction of ground vibrations resulting from quarry blasting
作者
关键词
-
出版物
Environmental Earth Sciences
Volume 74, Issue 4, Pages 2845-2860
出版商
Springer Nature
发表日期
2015-03-24
DOI
10.1007/s12665-015-4305-y
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization
- (2014) M. Hajihassani et al. APPLIED ACOUSTICS
- Creating an advanced backpropagation neural network toolbox within GIS software
- (2014) Sunju Lee et al. Environmental Earth Sciences
- Landslide susceptibility analysis based on ArcGIS and Artificial Neural Network for a large catchment in Three Gorges region, China
- (2014) Renneng Bi et al. Environmental Earth Sciences
- Prediction of pile bearing capacity using a hybrid genetic algorithm-based ANN
- (2014) E. Momeni et al. MEASUREMENT
- Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes
- (2013) Ibrahim Ocak et al. Environmental Earth Sciences
- A comparative study of artificial neural networks, Bayesian neural networks and adaptive neuro-fuzzy inference system in groundwater level prediction
- (2013) Saumen Maiti et al. Environmental Earth Sciences
- Estimating compaction parameters of fine- and coarse-grained soils by means of artificial neural networks
- (2012) Fatih Isik et al. Environmental Earth Sciences
- GIS and ANN-based spatial prediction of DOC in river networks: a case study in Dongjiang, Southern China
- (2012) Yingchun Fu et al. Environmental Earth Sciences
- Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks
- (2012) Nurcihan Ceryan et al. Environmental Earth Sciences
- Prediction of water quality from simple field parameters
- (2012) A. K. Verma et al. Environmental Earth Sciences
- Landslide susceptibility mapping using frequency ratio, analytic hierarchy process, logistic regression, and artificial neural network methods at the Inje area, Korea
- (2012) Soyoung Park et al. Environmental Earth Sciences
- Development of a fuzzy model for predicting ground vibration caused by rock blasting in surface mining
- (2012) Ebrahim Ghasemi et al. JOURNAL OF VIBRATION AND CONTROL
- Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network
- (2012) Masoud Monjezi et al. NEURAL COMPUTING & APPLICATIONS
- Application of multivariate analysis for prediction of blast-induced ground vibrations
- (2012) Turker Hudaverdi SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
- Estimation of elastic constant of rocks using an ANFIS approach
- (2011) Rajesh Singh et al. APPLIED SOFT COMPUTING
- Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations
- (2011) Mostafa Tantawy Mohamed INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
- Comparison of intelligence science techniques and empirical methods for prediction of blasting vibrations
- (2011) M. Mohamadnejad et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Prediction of environmental impacts of quarry blasting operation using fuzzy logic
- (2010) Abdullah Fişne et al. ENVIRONMENTAL MONITORING AND ASSESSMENT
- Predicting blast-induced ground vibration using various types of neural networks
- (2010) M. Monjezi et al. SOIL DYNAMICS AND EARTHQUAKE ENGINEERING
- Prediction of blast-induced ground vibration using artificial neural networks
- (2010) M. Monjezi et al. TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
- Application of soft computing to predict blast-induced ground vibration
- (2009) Manoj Khandelwal et al. ENGINEERING WITH COMPUTERS
- Prediction of blast-induced ground vibration using artificial neural network
- (2009) Manoj Khandelwal et al. INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES
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