Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia

Academy of Disaster Reduction and Emergency Management (2021) Ministry of Emergency Management - Ministry of Education, National Disaster Reduction Center of China, Ministry of Emergency Management, International Federation of Red Cross and Red Crescent Societies. https://www.preventionweb.net/publication/2020-global-natural-disaster-assessment-report. Search in Google Scholar

Adger N (2006) Vulnerability. Global Environmental Change 16: 268–281. doi: 10.1016/j.gloenvcha.2006.02.006. Open DOISearch in Google Scholar

Ahmad H, Ningsheng C, Rahman M, Islam M, Pourghasemi H, Hussain S, Habumugisha J, Liu E, Zheng H, Ni H, Dewan A (2021) Geohazards Susceptibility Assessment along the Upper Indus Basin Using Four Machine Learning and Statistical Models. ISPRS International Journal of Geo-Information 10, (5). doi: 10.3390/IJGI10050315. Open DOISearch in Google Scholar

Alexander D (1995) A survey of the field of natural hazards and disaster studies. In: Carrara A, Guzzetti F, (eds.) Geographical information systems in assessing natural hazards. Advances in Natural and Technological Hazards Research 5. Springer, Dordrecht, pp. 1 19. doi: 10.1007/978-94-015-8404-3_1. Open DOISearch in Google Scholar

ARPA Lombardia (2022) Qualità aria: i dati 2021 provincia per provincia. Available at: <https://www.arpalombardia.it/Pages/Aria/Qualita-aria.aspx>. Search in Google Scholar

Bevere L, Remondi F (2022) Natural catastrophes in 2021: the floodgates are open. Swiss Re Institute. Available at: <https://www.swissre.com/institute/research/sigmaresearch/sigma-2022-01.html>. Search in Google Scholar

Bianchini S, Solari L, Del Soldato M, Raspini F, Montalti R, Ciampalini A, Casagli N (2019) Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic. Remote Sensing 11:2015. doi: 10.3390/rs11172015. Open DOISearch in Google Scholar

Chen S, Yang Y, Deng F, Zhang Y, Liu Y, Liu C, Gao Z (2022) A highresolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations. Atmospheric Measurement Techniques 15, (3):735–756. doi: 10.5194/amt-15-735-2022. Open DOISearch in Google Scholar

Choubin B, Abdolshahnejad M, Moradi E, Querol X, Mosavi A, Shamshirband S, Ghamisi P (2020) Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain. Science of The Total Environment 701:134474. doi: 10.1016/j.scitotenv.2019.134474.31704408 Open DOISearch in Google Scholar

ClimaMI (2019) ClimaMI project. Available at: <https://www.progettoclimami.it/>. Search in Google Scholar

Copernicus (2022) About Copernicus. Available at: <https://www.copernicus.eu/en/about-copernicus>. Search in Google Scholar

Copernicus Atmosphere Monitoring Service (2022) Catalogue. Available at: <https://atmosphere.copernicus.eu/catalogue>. Search in Google Scholar

Copernicus Climate Change Service (2022a) Temperature and precipitation gridded data for global and regional domains derived from in-situ and satellite observations. Available at: <https://cds.climate.copernicus.eu/cdsapp!/dataset/insitugridded-observations-global-and-regional>. Search in Google Scholar

Copernicus Climate Change Service (2022b) Essential climate variables for assessment of climate variability from 1979 to present. Available at: <https://cds.climate.copernicus.eu/cdsapp!/dataset/ecv-for-climate-change>. Search in Google Scholar

Copernicus Climate Change Service (2022c) In situ temperature, relative humidity and wind profiles from 2006 to March 2020 from the GRUAN reference network. Available at: <https://cds.climate.copernicus.eu/cdsapp!/dataset/insituobservations-gruan-reference-network. Search in Google Scholar

Copernicus Climate Change Service (2022d) In situ observations of meteorological variables from the Integrated Global Radiosounding Archive and the Radiosounding Harmonization dataset from 1978 onward. Available at: <https://cds.climate.copernicus.eu/cdsapp!/dataset/insituobservations-igra-baseline-network>. Search in Google Scholar

Copernicus Climate Change Service (2022e) E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations. Available at: <https://cds.climate.copernicus.eu/cdsapp!/dataset/insitugridded-observations-europe>. Search in Google Scholar

Copernicus Climate Change Service (2022f) River discharge and related forecasted data by the European Flood Awareness System. Available at: <https://cds.climate.copernicus.eu/cdsapp!/dataset/efas-forecast>. Search in Google Scholar

Copernicus Emergency Management Service (2022) About. Available at: <https://emergency.copernicus.eu>. Copernicus Land Monitoring Service (2016a) European Digital Elevation Model (EU-DEM), version 1.1. Available at: <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-demv1.1>. Search in Google Scholar

Copernicus Land Monitoring Service (2016b) Slope derived from EU-DEM version 1.0. Available at: <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derivedproducts/slope>. Search in Google Scholar

Copernicus Land Monitoring Service (2016c) Aspect derived from EU-DEM version 1.0. Available at: <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derivedproducts/aspect>. Search in Google Scholar

Copernicus Land Monitoring Service (2016d) Hillshade derived from EU-DEM version 1.0. Available at: <https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1-0-and-derivedproducts/hillshade>. Search in Google Scholar

Copernicus Land Monitoring Service (2016e) Urban Atlas LCLU 2012. Available at: <https://land.copernicus.eu/local/urbanatlas/urban-atlas-2012>. Search in Google Scholar

Copernicus Land Monitoring Service (2018) Corine Land Cover. Available at: <https://land.copernicus.eu/paneuropean/corine-land-cover/clc2018>. Search in Google Scholar

Dang VH, Hoang ND, L-M-D N, Bui D, Samui P (2020) A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility. Forests 11:118. doi: 10.3390/f11010118. Open DOISearch in Google Scholar

Daniell K (2011) Collaborative flood and drought risk management in the Upper Iskar Basin, Bulgaria. In: Grafton R, Hussey K, (eds.) Water Resources Planning and Management. Cambridge University Press, Cambridge, pp. 395–420.10.1017/CBO9780511974304.022 Search in Google Scholar

Dimitrova R, Velizarova M (2021) Assessment of the Contribution of Different Particulate Matter Sources on Pollution in Sofia City. MDPI Atmosphere 12, (4):423. doi: 10.3390/atmos12040423. Open DOISearch in Google Scholar

Dos Santos R (2020) Estimating spatio-temporal air temperature in London (UK) using machine learning and earth observation satellite data. International Journal of Applied Earth Observation and Geoinformation 88:102066. doi: 10.1016/j.jag.2020.102066. Open DOISearch in Google Scholar

Ebrahimi-Khusfi Z, Taghizadeh-Mehrjardi R, Kazemi M, Nafarzadegan A (2021) Predicting the ground-level pollutants concentrations and identifying the influencing factors using machine learning, wavelet transformation, and remote sensing techniques. Atmospheric Pollution Research 12, (5): 101064. doi: 10.1016/j.apr.2021.101064. Open DOISearch in Google Scholar

Ebrahimy H, Feizizadeh B, Salmani S, Azadi H (2020) A comparative study of land subsidence susceptibility mapping of Tasuj plane, Iran, using boosted regression tree, random forest and classification and regression tree methods. Environmental Earth Sciences 79:223. doi: 10.1007/s12665-020-08953-0. Open DOISearch in Google Scholar

Elmahdy S, Ali T, Mohamed M (2020) Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach. Remote Sensing 12, (17):2695. doi: 10.3390/RS12172695. Open DOISearch in Google Scholar

EM-DAT (2020) OFDA/CRED The International Disaster Database, Université catholique de Louvain – Brussels – Belgium. Available at: <https://www.emdat.be/>. Search in Google Scholar

Emami S, Yousefi S, Pourghasemi H, Tavangar S, Santosh M (2020) A comparative study on machine learning modeling for mass movement susceptibility mapping (a case study of Iran. Bulletin of Engineering Geology and the Environment 79, (10): 5291–5308. doi: 10.1007/S10064-020-01915-7. Open DOISearch in Google Scholar

Ghorbanzadeh O, Blaschke T, Aryal J, Gholaminia K (2020) A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping. Journal of Spatial Science 65, (3):401–418. doi: 10.1080/14498596.2018.1505564. Open DOISearch in Google Scholar

Hakim W, Achmad A, Lee CW (2020) Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta- Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data. Remote Sensing 12, (21):3627. doi: 10.3390/rs12213627. Open DOISearch in Google Scholar

HARMONIA (2022) About Harmonia. Available at: <http://harmonia-project.eu/about_us>. Search in Google Scholar

Hong H, Shahabi H, Shirzadi A, Chen W, Chapi K, Ahmad B, Roodposhti M, Yari Hesar A, Tian Y, Tien Bui D (2019) Landslide susceptibility assessment at the Wuning area, China: a comparison between multi-criteria decision making, bivariate statistical and machine learning methods. Natural Hazards 96, (1):173–212. doi: 10.1007/S11069-018-3536-0. Open DOISearch in Google Scholar

Hu H, Wang C, Liang Z, Gao R, Li B (2021) Exploring Complementary Models Consisting of Machine Learning Algorithms for Landslide Susceptibility Mapping. ISPRS International Journal of Geo-Information 10, (10). doi: 10.3390/IJGI10100639. Open DOISearch in Google Scholar

ISPRA (2020) Comuni interessati da subsidenza. Available at: <https://annuario.isprambiente.it/sys_ind/733>. Search in Google Scholar

ISPRA (2021) Progetto IFFI (Inventario dei Fenomeni Franosi in Italia. Available at: <https://www.progettoiffi.isprambiente.it/cartografia-on-line/>. Search in Google Scholar

Ivanov P (2017) Analysis and Mapping the Landslide Hazard in Bulgaria. In: Mikoš M, Tiwari B, Yin Y, Sassa K, (eds.) Advancing Culture of Livingwith Landslides, Volume 2, Advances in Landslide Science. Springer Nature, pp. 1111–1118. doi: 10.1007/978-3-319-53498-5_126. Open DOISearch in Google Scholar

Ivanov P, Dobrev N, Berov B, Krastanov M, Nankin (2020) Assessment of landslide hazard in Bulgaria using GIS. Proceedings International Conference on Cartography and GIS 1, (8). doi: 10.1007/978-3-319-53498-5. Open DOISearch in Google Scholar

Jena R, B P, Alamri A (2020a) Susceptibility to Seismic Amplification and Earthquake Probability Estimation Using Recurrent Neural Network (RNN) Model in Odisha, India. Applied Sciences 10, (15):5355. doi: 10.3390/app10155355. Open DOISearch in Google Scholar

Jena R, Pradhan B, Al-Amri A, Lee C, H-j P (2020b) Earthquake Probability Assessment for the Indian Subcontinent Using Deep Learning. Sensors 20, (16):4369. doi: 10.3390/s20164369.747211732764354 Open DOISearch in Google Scholar

Jena R, Pradhan B, Beydoun G, Alamri A, Nizamuddin A, Sofyan H (2020c) Earthquake hazard and risk assessment using machine learning approaches at Palu, Indonesia. Science of the Total Environment 749:141582. doi: 10.1016/j.scitotenv.2020.141582.33370892 Open DOISearch in Google Scholar

Jena R, Pradhan B, Beydoun G, Ardiansyah N, Sofyan H, Affan M (2020d) Integrated model for earthquake risk assessment using neural network and analytic hierarchy process: Aceh province, Indonesia. Geoscience Frontiers 11, (2):613–634,. doi: 10.1016/j.gsf.2019.07.006. Open DOISearch in Google Scholar

Kelman I, Gaillard J, Mercer J (2015) Climate change’s role in disaster risk reduction’s future: beyond vulnerability and resilience. International Journal of Disaster Risk Science 6: 21–27. doi: 10.1007/s13753-015-0038-5. Open DOISearch in Google Scholar

Lei X, Chen W, Panahi M, Falah F, Rahmati O, Uuemaa E, Kalantari Z, Ferreira C, Rezaie F, Tiefenbacher J, Lee S, Bian H (2021) Urban flood modeling using deep-learning approaches in Seoul, South Korea. Journal of Hydrology 601:126684. doi: 10.1016/J.JHYDROL.2021.126684. Open DOISearch in Google Scholar

Ministry of Regional Development and Public Works (2022) Landslide inventory. Available at: <http://gz-pernik.mrrb.government.bg/landslide/>. Search in Google Scholar

Mohammady M, Pourghasemi H, Amiri M (2019) Land subsidence susceptibility assessment using random forest machine learning algorithm. Environmental Earth Sciences 78:503. doi: 10.1007/s12665-019-8518-3. Open DOISearch in Google Scholar

Na T, Kawamura Y, seung Kang S, Utsuki S (2021) Hazard mapping of ground subsidence in east area of Sapporo using frequency ratio model and GIS. Geomatics, Natural Hazards and Risk 12, (1):347–362. doi: 10.1080/19475705.2021.1873198. Open DOISearch in Google Scholar

Nachappa T, Ghorbanzadeh O, Gholamnia K, Blaschke T (2020) Multi-Hazard Exposure Mapping Using Machine Learning for the State of Salzburg, Austria. Remote Sensing 12, (17):2757. doi: 10.3390/RS12172757. Open DOISearch in Google Scholar

Oh HJ, Syifa M, Lee CW, Lee S (2019) Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta- Ensemble Machine Learning Models. Applied Sciences 9, (6): 1248. doi: 10.3390/app9061248. Open DOISearch in Google Scholar

Okumus D, Terzi F (2021) Evaluating the role of urban fabric on surface urban heat island: The case of Istanbul. Sustainable Cities and Society 73:103128. doi: 10.1016/j.scs.2021.103128. Open DOISearch in Google Scholar

Park S, Im J, Park S, Rhee J (2017) Drought monitoring using high resolution soil moisture through multi-sensor satellite data fusion over the Korean peninsula. Agricultural and Forest Meteorology 237–238:257–269. doi: 10.1016/J.AGRFORMET.2017.02.022. Open DOISearch in Google Scholar

Paskaleva I, Panza G, Vaccari F, Ivanov P (2004) Deterministic modelling for microzonation of Sofia - An expected earthquake scenario. Acta Geodaetica et Geophysica Hungarica 39: 275–295. doi: 10.1556/AGeod.39.2004.2-3.10. Open DOISearch in Google Scholar

Pham B, Luu C, Dao D, Phong T, Nguyen H, Le H, Meding J, Prakash I (2021) Flood risk assessment using deep learning integrated with multi-criteria decision analysis. Knowledge-Based Systems 219:106899. doi: 10.1016/J.KNOSYS.2021.106899. Open DOISearch in Google Scholar

Ranjgar B, Razavi-Termeh S, Foroughnia F, Sadeghi-Niaraki A, Perissin D (2021) Land Subsidence Susceptibility Mapping Using Persistent Scatterer SAR Interferometry Technique and Optimized Hybrid Machine Learning Algorithms. Remote Sensing 13, (7):1326. doi: 10.3390/rs13071326. Open DOISearch in Google Scholar

Raška P, Dolejš M, Pacina J, Popelka J, Píša J, Rybová K (2020) Review of current approaches to spatially explicit urban vulnerability assessments: hazard complexity, data sources, and cartographic representations. Geoscape 14, (1):47–61. doi: 10.2478/geosc-2020-0005. Open DOISearch in Google Scholar

Regione Lombardia (2014) Seismic Zonation. Available at: <https://www.regione.lombardia.it/wps/portal/istituzionale/HP/DettaglioRedazionale/servizi-einformazioni/Enti-e-Operatori/protezione-civile/Rischiosismico/nuova-zonazione-sismica/zonazione-sismica>. Search in Google Scholar

Resilient Cities Network (2022) What is urban resilience. Available at: <https://resilientcitiesnetwork.org/what-isresilience/>. Search in Google Scholar

Sangiorgio V, Fiorito F, Santamouris M (2020) Development of a holistic urban heat island evaluation methodology. Scientific Reports 10:17913. doi: 10.1038/s41598-020-75018-4.757806433087829 Open DOISearch in Google Scholar

Satarzadeh E, Sarraf A, Hajikandi H, Sadeghian M (2022) Flood hazard mapping in western Iran: assessment of deep learning vis-à-vis machine learning models. Natural Hazards 111: 1355–1373. doi: 10.1007/s11069-021-05098-6. Open DOISearch in Google Scholar

Schneider R, Vicedo-Cabrera A, Sera F, Masselot P, Stafoggia M, Hoogh K, Kloog I, Reis S, Vieno M, Gasparrini A (2020) A Satellite-Based Spatio-Temporal Machine Learning Model to Reconstruct Daily PM2.5 Concentrations across Great Britain. Remote Sensing 12:3803. doi: 10.3390/rs12223803.711654733408882 Open DOISearch in Google Scholar

Shogrkhodaei S, Razavi-Termeh SV, Fathnia A (2021) Spatiotemporal modeling of PM2.5 risk mapping using three machine learning algorithms. Environmental Pollution 289: 117859. doi: 10.1016/j.envpol.2021.117859.34340183 Open DOISearch in Google Scholar

Skilodimou H, Bathrellos G (2021) Natural and Technological Hazards in Urban Areas: Assessment, Planning and Solutions. Sustainability 13, (15):8301. doi: 10.3390/su13158301. Open DOISearch in Google Scholar

Skilodimou H, Bathrellos G, Chousianitis K, A Y, Pradhan B (2019) Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environmental Earth Sciences 78:47. doi: 10.1007/s12665-018-8003-4. Open DOISearch in Google Scholar

Sofiaplan (2018) Areas with significant potential flood risk. Available at: <https://api.sofiaplan.bg/datasets/51>. Sofiaplan (2022) Rights to use the published materials. Available at: <https://sofiaplan.bg/>. Search in Google Scholar

Tien Bui D, Shahabi H, Shirzadi A, Chapi K, Pradhan B, Chen W, Khosravi K, Panahi M, Bin Ahmad B, Saro L (2018) Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms. Sensors 18, (8). ISSN 1424-8220. doi: 10.3390/s18082464.611131030065216 Open DOISearch in Google Scholar

United States Environmental Protection Agency (EPA) (2021) Climate Change Indicators: U.S. and Global Precipitation. Available at: <https://www.epa.gov/climate-indicators/climate-change-indicators-us-and-global-precipitation>. Search in Google Scholar

Yao Y, Chang C, Ndayisaba F, Wang S (2020) A New Approach for Surface Urban Heat Island Monitoring Based on Machine Learning Algorithm and Spatiotemporal Fusion Model. IEEE Access 8:164268–164281. doi: 10.1109/ACCESS.2020.3022047. Open DOISearch in Google Scholar

Zheng X, He G, Wang S, Wang Y, Wang G, Yang Z, Yu J, Wang N (2021) Comparison of Machine Learning Methods for Potential Active Landslide Hazards Identification with Multi-Source Data. ISPRS International Journal of Geo-Information 10, (4): 253. doi: 10.3390/IJGI10040253. Open DOISearch in Google Scholar