Establishment and assessment of urban meteorological disaster emergency response capability based on modeling methods
Tài liệu tham khảo
Cappelli, 2021, The trap of climate change-induced “natural” disasters and inequality, Global Environ. Change, 70, 10.1016/j.gloenvcha.2021.102329
Bourdeau-Brien, 2020, Natural disasters and risk aversion, J. Econ. Behav. Organ., 177, 818, 10.1016/j.jebo.2020.07.007
Chai, 2020, Driving factors of natural disasters in belt and road countries, Int. J. Disaster Risk Reduc., 51, 10.1016/j.ijdrr.2020.101774
Xu, 2017, Extreme meteorological disaster effects on grain production in Jilin Province, China, J. Integr. Agric., 16, 486, 10.1016/S2095-3119(15)61285-0
Chen, 2021, Does high risk mean high loss: evidence from flood disaster in southern China, Sci. Total Environ., 785, 10.1016/j.scitotenv.2021.147127
Hu, 2021, Disaster policy and emergency management reforms in China: from Wenchuan earthquake to Jiuzhaigou earthquake, Int. J. Disaster Risk Reduc., 52, 10.1016/j.ijdrr.2020.101964
Wu, 2021, The challenges and countermeasures in emergency management after the establishment of the ministry of emergency management of China: a case study, Int. J. Disaster Risk Reduc., 55, 10.1016/j.ijdrr.2021.102075
Chen, 2020, Emergency rescue capability evaluation on urban fire stations in China, Process Saf. Environ. Protect., 135, 59, 10.1016/j.psep.2019.12.028
Fan, 2019, Embeddedness in cross-agency collaboration and emergency management capability: evidence from Shanghai's urban contingency plans, Govern. Inf. Q., 36
Karam, 2021, Analysis of the barriers to implementing horizontal collaborative transport using a hybrid fuzzy Delphi-AHP approach, J. Clean. Prod., 321, 10.1016/j.jclepro.2021.128943
Yan, 2013, Comparison and enlightenment of USA and Japan's emergency management mechanism, Int. J. Financ. Res., 4, 144
Atrachali, 2019, Toward quantification of seismic resilience in Iran: developing an integrated indicator system, Int. J. Disaster Risk Reduc., 39, 10.1016/j.ijdrr.2019.101231
Deng, 2017, Investigation and analysis of the importance awareness of the factors affecting the earthquake emergency and rescue in different areas: a case study of Yunnan and Jiangsu Provinces, Int. J. Disaster Risk Reduc., 25, 163, 10.1016/j.ijdrr.2017.09.017
Nunes, 2020, Disaster risk assessment: the experience of the city of Rio De Janeiro in developing an impact scale for meteorological-related disasters, Prog. Disaster Sci., 5, 10.1016/j.pdisas.2019.100053
Nadiri, 2021, Predictive groundwater levels modelling by Inclusive Multiple Modelling (IMM) at multiple levels, Earth Sci. Info., 1
Petković, 2017, Estimation of fractal representation of wind speed fluctuation by artificial neural network with different training algorithms, Flow Meas. Instrum., 54, 172, 10.1016/j.flowmeasinst.2017.01.007
Shams, 2021, The evaluation on artificial neural networks (ANN) and multiple linear regressions (MLR) models for predicting SO2 concentration, Urban Clim., 37, 10.1016/j.uclim.2021.100837
Shamshirband, 2014, Support vector regression methodology for wind turbine reaction torque prediction with power-split hydrostatic continuous variable transmission, Energy, 67, 623, 10.1016/j.energy.2014.01.111
Wei, 2018, Research on building fire risk fast assessment method based on fuzzy comprehensive evaluation and SVM, Procedia Eng., 211, 1141, 10.1016/j.proeng.2017.12.121
Cui, 2021, Scientific challenges of research on natural hazards and disaster risk, Geogr. Sustain., 2, 216
Ekmekcioğlu, 2021, Stakeholder perceptions in flood risk assessment: a hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey, Int. J. Disaster Risk Reduc., 60, 10.1016/j.ijdrr.2021.102327
Hoscan, 2021, Determination of emergency assembly point for industrial accidents with AHP analysis, J. Loss Prev. Process. Ind., 69, 10.1016/j.jlp.2020.104386
Kittipongvises, 2020, AHP-GIS analysis for flood hazard assessment of the communities nearby the world heritage site on Ayutthaya Island, Thailand, Int. J. Disaster Risk Reduc., 48, 10.1016/j.ijdrr.2020.101612
Huang, 2019, Thermal hazard assessment of the thermal stability of acne cosmeceutical therapy using advanced calorimetry technology, Process Saf. Environ. Protect., 131, 197, 10.1016/j.psep.2019.09.016
Arora, 2018, Estimation of re-aeration coefficient using MLR for modelling water quality of rivers in urban environment, Groundw. Sustain. Dev., 7, 430, 10.1016/j.gsd.2017.11.006
Zhao, 2016, Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000–2015 using quantile and multiple line regression models, Atmos. Environ., 144, 182, 10.1016/j.atmosenv.2016.08.077
Zhang, 2021, The sources-specific health risk assessment combined with APCS/MLR model for heavy metals in tea garden soils from south Fujian Province, China, Catena, 203, 10.1016/j.catena.2021.105306
Nadiri, 2017, Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models, Environ. Sci. Pollut. Control Ser., 24, 8562, 10.1007/s11356-017-8489-4
Iqbal, 2020, Relating groundwater levels with meteorological parameters using ANN technique, Measurement, 166, 10.1016/j.measurement.2020.108163
Moazamnia, 2020, Vulnerability indexing to saltwater intrusion from models at two levels using artificial intelligence multiple model (AIMM), J. Environ. Manag., 255, 10.1016/j.jenvman.2019.109871
Shao, 2021, Application of BP - ANN model in evaluation of soil quality in the arid area, northwest China, Soil Tillage Res., 208, 10.1016/j.still.2020.104907
Moshkbar-Bakhshayesh, 2021, Identification of the appropriate architecture of multilayer feed-forward neural network for estimation of NPPs parameters using the GA in combination with the LM and the BR learning algorithms, Ann. Nucl. Energy, 156, 10.1016/j.anucene.2021.108222
Heaton, 2008
Balaga, 2015, GA trained parallel hidden layered ANN based differential protection of three phase power transformer, Int. J. Electr. Power Energy Syst., 67, 286, 10.1016/j.ijepes.2014.11.028
Bourinet, 2011, Assessing small failure probabilities by combined subset simulation and Support Vector Machines, Struct. Saf., 33, 343, 10.1016/j.strusafe.2011.06.001
Nikolić, 2017, Wind speed parameters sensitivity analysis based on fractals and neuro-fuzzy selection technique, Knowl. Inf. Syst., 52, 255, 10.1007/s10115-016-1006-0
Huang, 2020, Quantitative contribution of climate change and human activities to vegetation cover variations based on GA-SVM model, J. Hydrol., 584, 10.1016/j.jhydrol.2020.124687
Nadiri, 2018, Mapping aquifer vulnerability indices using artificial intelligence-running multiple frameworks (AIMF) with supervised and unsupervised learning, Water Resour. Manag., 32, 10.1007/s11269-018-1971-z
Chen, 2021, Identification of architectural elements based on SVM with PCA: a case study of sandy braided river reservoir in the Lamadian Oilfield, Songliao Basin, NE China, J. Pet. Sci. Eng., 198, 10.1016/j.petrol.2020.108247
Huang, 2021, Evaluation of multiple reactions in dilute benzoyl peroxide concentrations with additives using calorimetric technology, J. Loss Prev. Process. Ind., 69, 10.1016/j.jlp.2020.104373
Shamshirband, 2015, Sensor data fusion by support vector regression methodology—a comparative study, IEEE Sensor. J., 15, 850, 10.1109/JSEN.2014.2356501
Tan, 2018, Study on bruising degree classification of apples using hyperspectral imaging and GS-SVM, Optik, 154, 581, 10.1016/j.ijleo.2017.10.090
Arshad, 2021, ANN, and PSF modelling approaches for prediction of iron dust minimum ignition temperature (MIT) based on the synergistic effect of dispersion pressure and concentration, Process Saf. Environ. Protect., 152, 375, 10.1016/j.psep.2021.06.001
Mehrabi, 2021, Modeling of condensation heat transfer coefficients and flow regimes in flattened channels, Int. Commun. Heat Mass Tran., 126, 10.1016/j.icheatmasstransfer.2021.105391
Karaağaç, 2021, Experimental analysis of CPV/T solar dryer with nano-enhanced PCM and prediction of drying parameters using ANN and SVM algorithms, Sol. Energy, 218, 57, 10.1016/j.solener.2021.02.028
Agounad, 2019, Analysis of the prediction of a bilayered cylindrical shell's reduced cutoff frequency with data-driven approaches, Mech. Syst. Signal Process., 128, 126, 10.1016/j.ymssp.2019.03.028
Malfatti, 2021, Evaluating and predicting egg quality indicators through principal component analysis and artificial neural networks, LWT--Food Sci. Technol., 148, 10.1016/j.lwt.2021.111720