A decision framework to assess urban fire vulnerability in cities of developing nations: empirical evidence from Mumbai
暂无分享,去创建一个
[1] Kirsi Virrantaus,et al. Discovering spatio-temporal relationships in the distribution of building fires , 2013 .
[2] Anka Lisec,et al. Estimating the Performance of Random Forest versus Multiple Regression for Predicting Prices of the Apartments , 2018, ISPRS Int. J. Geo Inf..
[3] P. Christenson. Variable selection in multiple regression , 1982 .
[4] Ananto Yudono,et al. Expert System Development for Urban Fire Hazard Assessment. Study Case: Kendari City, Indonesia , 2017 .
[5] Charles Jennings,et al. Social and economic characteristics as determinants of residential fire risk in urban neighborhoods: A review of the literature , 2013 .
[6] Hyung-Sup Jung,et al. Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea , 2017 .
[7] Marco Piras,et al. An INSPIRE-compliant open-source GIS for fire-fighting management , 2017, Natural Hazards.
[8] Weiguo Song,et al. A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence , 2017 .
[9] Nicklas Guldåker,et al. Spatio-temporal patterns of intentional fires,social stress and socio-economic determinants: A case study of Malmö,Sweden , 2014 .
[10] Akira Imada,et al. A Literature Review: Forest Management with Neural Network and Artificial Intelligence , 2014 .
[11] O. Viedma,et al. Recent land-use and land-cover changes and its driving factors in a fire-prone area of southwestern Turkey. , 2017, Journal of environmental management.
[12] Charles R. Jennings. Socioeconomic Characteristics and Their Relationship to Fire Incidence: A Review of the Literature , 1999 .
[13] Rajashree Kotharkar,et al. Measuring Compact Urban Form: A Case of Nagpur City, India , 2014 .
[14] Shiqiang Du,et al. Analyzing explanatory factors of urban pluvial floods in Shanghai using geographically weighted regression , 2017, Stochastic Environmental Research and Risk Assessment.
[15] Lara Wiesche,et al. Time-dependent ambulance allocation considering data-driven empirically required coverage , 2015, Health care management science.
[16] J. Pereira,et al. Relationships between Human Population Density and Burned Area at Continental and Global Scales , 2013, PloS one.
[17] Xiang Chen,et al. Image Based Forest Fire Detection Using Dynamic Characteristics with Artificial Neural Networks , 2009, 2009 International Joint Conference on Artificial Intelligence.
[18] M. Pereira,et al. Negligent and intentional fires in Portugal: Spatial distribution characterization. , 2018, The Science of the total environment.
[19] Jianguo Wu,et al. Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns , 2009, Landscape Ecology.
[20] Ronan A Lyons,et al. Risk factors associated with unintentional house fire incidents, injuries and deaths in high-income countries: a systematic review , 2017, Injury Prevention.
[21] J. Pereira,et al. Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest , 2012 .
[22] Arnab Jana,et al. Resource allocation for handling emergencies considering dynamic variations and urban spaces: fire fighting in Mumbai , 2019, ICTD.
[23] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[24] C. Purushothaman,et al. Quantitative analysis of plastic debris on recreational beaches in Mumbai, India. , 2013, Marine pollution bulletin.
[25] Onisimo Mutanga,et al. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm , 2012, Int. J. Appl. Earth Obs. Geoinformation.
[26] Catherine Linard,et al. Geographical random forests: a spatial extension of the random forest algorithm to address spatial heterogeneity in remote sensing and population modelling , 2019, Geocarto International.
[27] R. Searle,et al. Socio-economic and demographic predictors of accidental dwelling fire rates , 2016 .
[28] Jing Yao,et al. Exploring Spatiotemporal Dynamics of Urban Fires: A Case of Nanjing, China , 2018, ISPRS Int. J. Geo Inf..