Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining
暂无分享,去创建一个
B. Pham | H. Ly | M. H. Nguyen | Thuy-Anh Nguyen | Chongchong Qi | Xiang Lu | Wei Zhou | Jiandong Huang | Wei Zhou
[1] Chongchong Qi,et al. Machine learning exploration of the mobility and environmental assessment of toxic elements in mining-associated solid wastes , 2023, Journal of Cleaner Production.
[2] Haoxuan Yu,et al. Environmental hazards posed by mine dust, and monitoring method of mine dust pollution using remote sensing technologies: An overview. , 2022, The Science of the total environment.
[3] Nadhir Al-Ansari,et al. Hybridization of artificial intelligence models with nature inspired optimization algorithms for lake water level prediction and uncertainty analysis , 2021 .
[4] Ali Reza Nafarzadegan,et al. Accuracy, uncertainty, and interpretability assessments of ANFIS models to predict dust concentration in semi-arid regions , 2020, Environmental Science and Pollution Research.
[5] Y. Kerchich,et al. Prediction of the concentrations of PM1, PM2.5, PM4, and PM10 by using the hybrid dragonfly-SVM algorithm , 2020, Air Quality, Atmosphere & Health.
[6] Zaher Mundher Yaseen,et al. Particulate matter concentration from open-cut coal mines: A hybrid machine learning estimation. , 2020, Environmental pollution.
[7] A. Collins,et al. Mapping the spatial sources of atmospheric dust using GLUE and Monte Carlo simulation. , 2020, The Science of the total environment.
[8] O. Udvardy,et al. Concomitant occurrence of anthropogenic air pollutants, mineral dust and fungal spores during long-distance transport of ragweed pollen. , 2019, Environmental pollution.
[9] Christian Soize,et al. Modeling uncertainties in molecular dynamics simulations using a stochastic reduced-order basis , 2019, Computer Methods in Applied Mechanics and Engineering.
[10] K. Nagesha,et al. Development of statistical models to predict emission rate and concentration of particulate matters (PM) for drilling operation in opencast mines , 2019, Air Quality, Atmosphere & Health.
[11] P. Mwaanga,et al. Preliminary review of mine air pollution in Zambia , 2019, Heliyon.
[12] Binh Thai Pham,et al. Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis. , 2019, The Science of the total environment.
[13] K. Sun,et al. Distribution Law and Prediction Model of Dust Concentration under Airflow Adjustment in Fully Mechanized Heading Face , 2019, Mathematical Problems in Engineering.
[14] Hoang Nguyen,et al. Estimating PM10 Concentration from Drilling Operations in Open-Pit Mines Using an Assembly of SVR and PSO , 2019, Applied Sciences.
[15] Tien-Thinh Le,et al. Quantification of Uncertainties on the Critical Buckling Load of Columns under Axial Compression with Uncertain Random Materials , 2019, Materials.
[16] Binh Thai Pham,et al. Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees , 2019, Materials.
[17] Binh Thai Pham,et al. Artificial Intelligence Approaches for Prediction of Compressive Strength of Geopolymer Concrete , 2019, Materials.
[18] Xinbin Feng,et al. Atmospheric deposition of antimony in a typical mercury-antimony mining area, Shaanxi Province, Southwest China. , 2019, Environmental pollution.
[19] Chongchong Qi,et al. A new procedure for recycling waste tailings as cemented paste backfill to underground stopes and open pits , 2018, Journal of Cleaner Production.
[20] Aditya Kumar Patra,et al. Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model , 2016, Air Quality, Atmosphere & Health.
[21] Aditya Kumar Patra,et al. Emissions and human health impact of particulate matter from surface mining operation—A review , 2016 .
[22] Bindhu Lal,et al. Prediction of dust concentration in open cast coal mine using artificial neural network , 2012 .
[23] Christian Soize,et al. A probabilistic model for bounded elasticity tensor random fields with application to polycrystalline microstructures , 2011 .
[24] Anastasia K Paschalidou,et al. Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management , 2011, Environmental science and pollution research international.
[25] Yonggwan Won,et al. Training Single Hidden Layer Feedforward Neural Networks by Singular Value Decomposition , 2009, 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology.
[26] Du-Wu Cui,et al. Research on handwritten numeral recognition method based on improved genetic algorithm and neural network , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.
[27] Fearghal Morgan,et al. Intrinsic Hardware Evolution of Neural Networks in Reconfigurable Analogue and Digital Devices , 2006, 2006 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines.
[28] Xin-She Yang,et al. Engineering Optimizations via Nature-Inspired Virtual Bee Algorithms , 2005, IWINAC.
[29] Gavin C. Cawley,et al. A rigorous inter-comparison of ground-level ozone predictions , 2003 .
[30] Albert Ali Salah,et al. A Selective Attention-Based Method for Visual Pattern Recognition with Application to Handwritten Digit Recognition and Face Recognition , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[31] Wps Dias,et al. NEURAL NETWORKS FOR PREDICTING PROPERTIES OF CONCRETES WITH ADMIXTURES , 2001 .
[32] Christian Soize. A nonparametric model of random uncertainties for reduced matrix models in structural dynamics , 2000 .
[33] Mohammed El-Beltagy,et al. A comparison of various optimization algorithms on a multilevel problem , 1999 .
[34] M. Gardner,et al. Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London , 1999 .
[35] Anil K. Jain,et al. Artificial Neural Networks: A Tutorial , 1996, Computer.
[36] Marija Zlata Boznar,et al. A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain , 1993 .
[37] M. F. Møller. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1990 .
[38] M. J. D. Powell,et al. Restart procedures for the conjugate gradient method , 1977, Math. Program..
[39] Ezzeddin Bakhtavar,et al. Optimization of the transition from open-pit to underground operation in combined mining using (0-1) integer programming , 2012 .
[40] R. Tahboub,et al. GEOMETRICAL FORM RECOGNITION USING “ ONE-STEP-SECANT ” ALGORITHM IN CASE OF NEURAL NETWORK , 2010 .
[41] Rich Caruana,et al. Overfitting in Neural Nets: Backpropagation, Conjugate Gradient, and Early Stopping , 2000, NIPS.