{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T08:57:29Z","timestamp":1772182649256,"version":"3.50.1"},"reference-count":79,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2021,10,18]],"date-time":"2021-10-18T00:00:00Z","timestamp":1634515200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"publisher","award":["Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"],"award-info":[{"award-number":["Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)"]}],"id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["Researchers Supporting Project number RSP-2021\/14"],"award-info":[{"award-number":["Researchers Supporting Project number RSP-2021\/14"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In Australia, droughts are recurring events that tremendously affect environmental, agricultural and socio-economic activities. Southern Queensland is one of the most drought-prone regions in Australia. Consequently, a comprehensive drought vulnerability mapping is essential to generate a drought vulnerability map that can help develop and implement drought mitigation strategies. The study aimed to prepare a comprehensive drought vulnerability map that combines drought categories using geospatial techniques and to assess the spatial extent of the vulnerability of droughts in southern Queensland. A total of 14 drought-influencing criteria were selected for three drought categories, specifically, meteorological, hydrological and agricultural. The specific criteria spatial layers were prepared and weighted using the fuzzy analytical hierarchy process. Individual categories of drought vulnerability maps were prepared from their specific indices. Finally, the overall drought vulnerability map was generated by combining the indices using spatial analysis. Results revealed that approximately 79.60% of the southern Queensland region is moderately to extremely vulnerable to drought. The findings of this study were validated successfully through the receiver operating characteristics curve (ROC) and the area under the curve (AUC) approach using previous historical drought records. Results can be helpful for decision makers to develop and apply proactive drought mitigation strategies.<\/jats:p>","DOI":"10.3390\/s21206896","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T21:31:26Z","timestamp":1634765486000},"page":"6896","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Drought Vulnerability Assessment Using Geospatial Techniques in Southern Queensland, Australia"],"prefix":"10.3390","volume":"21","author":[{"given":"Muhammad","family":"Hoque","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Department of Geography and Environment, Jagannath University, Dhaka 1100, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, Australia"},{"name":"Earth Observation Centre, Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2775-0592","authenticated-orcid":false,"given":"Naser","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Department of Geography and Environment, Jagannath University, Dhaka 1100, Bangladesh"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11362, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.jaridenv.2019.04.007","article-title":"Agricultural drought assessment based on multiple soil moisture products","volume":"167","author":"Baik","year":"2019","journal-title":"J. Arid Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"134230","DOI":"10.1016\/j.scitotenv.2019.134230","article-title":"Machine learning approaches for spatial modeling of agricultural droughts in south-east region of Queensland Australia","volume":"699","author":"Rahmati","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1007\/s11069-016-2149-8","article-title":"Drought indicators-based integrated assessment of drought vulnerability: A case study of Bundelkhand droughts in central India","volume":"81","author":"Thomas","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s00704-015-1706-5","article-title":"Application of effective drought index for quantification of meteorological drought events: A case study in Australia","volume":"128","author":"Deo","year":"2017","journal-title":"Theor. Appl. Climatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1016\/j.jenvman.2014.12.040","article-title":"Drought risk mapping of south-western state in the Indian peninsula\u2013A web based application","volume":"161","author":"Gopinath","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s00704-017-2182-x","article-title":"Spatiotemporal analysis of the agricultural drought risk in Heilongjiang Province, China","volume":"133","author":"Pei","year":"2018","journal-title":"Theor. Appl. Climatol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1175\/BAMS-88-10-Schubert","article-title":"Predicting drought on seasonal-to-decadal time scales","volume":"88","author":"Schubert","year":"2007","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.ijdrr.2015.01.004","article-title":"Geospatial analysis of agricultural drought vulnerability using a composite index based on exposure, sensitivity and adaptive capacity","volume":"12","author":"Murthy","year":"2015","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.ijdrr.2018.12.008","article-title":"Decision making under crisis: Lessons from the Millennium Drought in Australia","volume":"34","author":"Caball","year":"2019","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.agrformet.2019.01.008","article-title":"A new multi-sensor integrated index for drought monitoring","volume":"268","author":"Jiao","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1007\/s11269-019-02225-8","article-title":"A Novel Method for Agricultural Drought Risk Assessment","volume":"33","author":"Pei","year":"2019","journal-title":"Water Resour. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.jher.2013.07.003","article-title":"Assessment of drought hazard, vulnerability, and risk: A case study for administrative districts in South Korea","volume":"9","author":"Kim","year":"2015","journal-title":"J. Hydro-Environ. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008GL036801","article-title":"What causes southeast Australia\u2019s worst droughts?","volume":"36","author":"Ummenhofer","year":"2009","journal-title":"Geophys. Res. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1023\/B:CLIM.0000018515.46344.6d","article-title":"The changing nature of Australian droughts","volume":"63","author":"Nicholls","year":"2004","journal-title":"Clim. Chang."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"143600","DOI":"10.1016\/j.scitotenv.2020.143600","article-title":"Agricultural drought risk assessment of Northern New South Wales, Australia using geospatial techniques","volume":"756","author":"Hoque","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1002\/wrcr.20123","article-title":"The Millennium Drought in southeast Australia (2001\u20132009): Natural and human causes and implications for water resources, ecosystems, economy, and society","volume":"49","author":"Beck","year":"2013","journal-title":"Water Resour. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1080\/13241583.2007.11465319","article-title":"Long-term drought risk assessment in the Lachlan River Valley\u2013A paleoclimate perspective","volume":"11","author":"Verdon","year":"2007","journal-title":"Australas. J. Water Resour."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s10584-016-1798-7","article-title":"Natural hazards in Australia: Droughts","volume":"139","author":"Kiem","year":"2016","journal-title":"Clim. Chang."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.agsy.2019.03.015","article-title":"Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia","volume":"173","author":"Feng","year":"2019","journal-title":"Agric. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s11069-018-3326-8","article-title":"Spatio-temporal drought risk mapping approach and its application in the drought-prone region of south-east Queensland, Australia","volume":"93","author":"Dayal","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"05017029","DOI":"10.1061\/(ASCE)HE.1943-5584.0001593","article-title":"Investigating drought duration-severity-intensity characteristics using the Standardized Precipitation-Evapotranspiration Index: Case studies in drought-prone Southeast Queensland","volume":"23","author":"Dayal","year":"2017","journal-title":"J. Hydrol. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1016\/j.atmosres.2014.10.016","article-title":"Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia","volume":"153","author":"Deo","year":"2015","journal-title":"Atmos. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"111291","DOI":"10.1016\/j.rse.2019.111291","article-title":"Remote sensing for drought monitoring & impact assessment: Progress, past challenges and future opportunities","volume":"232","author":"West","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1007\/s12665-016-5822-z","article-title":"Drought vulnerability mapping using AHP method in arid and semiarid areas: A case study for Taft Township, Yazd Province, Iran","volume":"75","author":"Ekrami","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s11069-014-1502-z","article-title":"Spatio-temporal assessment of vulnerability to drought","volume":"76","author":"Jain","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shaw, R., Mallick, F., and Islam, A. (2013). Understanding Vulnerability and Risks. Disaster Risk Reduction Approaches in Bangladesh, Springer.","DOI":"10.1007\/978-4-431-54252-0"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.gloenvcha.2008.07.013","article-title":"A place-based model for understanding community resilience to natural disasters","volume":"18","author":"Cutter","year":"2008","journal-title":"Glob. Environ. Chang."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"104898","DOI":"10.1016\/j.ocecoaman.2019.104898","article-title":"Assessment of coastal vulnerability to multi-hazardous events using geospatial techniques along the eastern coast of Bangladesh","volume":"181","author":"Hoque","year":"2019","journal-title":"Ocean Coast. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s12517-012-0707-2","article-title":"Drought risk assessment using remote sensing and GIS techniques","volume":"7","author":"Belal","year":"2014","journal-title":"Arab. J. Geosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s11069-012-0093-9","article-title":"Drought hazard assessment using geoinformatics over parts of Chotanagpur plateau region, Jharkhand, India","volume":"63","author":"Pandey","year":"2012","journal-title":"Nat. Hazards"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1905","DOI":"10.1007\/s11069-016-2526-3","article-title":"Application of AHP with GIS in drought risk assessment for Puruliya district, India","volume":"84","author":"Palchaudhuri","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1007\/s00704-014-1234-8","article-title":"Assessment of drought vulnerability of the Tarim River basin, Xinjiang, China","volume":"121","author":"Zhang","year":"2015","journal-title":"Theor. Appl. Climatol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1007\/s11269-017-1594-9","article-title":"Assessment of agricultural drought vulnerability in the Guanzhong Plain, China","volume":"31","author":"Wu","year":"2017","journal-title":"Water Resour. Manag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1080\/15481603.2017.1287838","article-title":"Monitoring drought vulnerability using multispectral indices observed from sequential remote sensing (Case Study: Tuy Phong, Binh Thuan, Vietnam)","volume":"54","author":"Tran","year":"2017","journal-title":"GISci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.landusepol.2017.11.027","article-title":"Assessing drought vulnerability and adaptation among farmers in Gadaref region, Eastern Sudan","volume":"70","author":"Mohmmed","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1177\/0309133317695158","article-title":"Using Fuzzy Analytic Hierarchy Process multi-criteria and automatic computation to analyse coastal vulnerability","volume":"41","author":"Tahri","year":"2017","journal-title":"Prog. Phys. Geogr."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1007\/s11069-018-3244-9","article-title":"Integration of multi-parametric fuzzy analytic hierarchy process and GIS along the UNESCO World Heritage: A flood hazard index, Mombasa County, Kenya","volume":"92","author":"Hategekimana","year":"2018","journal-title":"Nat. Hazards"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Demirel, T., Demirel, N.\u00c7., and Kahraman, C. (2008). Fuzzy Analytic Hierarchy Process and Its Application. Fuzzy Multi-Criteria Decision Making, Springer.","DOI":"10.1007\/978-0-387-76813-7_3"},{"key":"ref_39","first-page":"4511","article-title":"Seismic vulnerability assessment of school buildings in Tehran city based on AHP and GIS","volume":"1","author":"Panahi","year":"2013","journal-title":"Nat. Hazards Earth Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s10346-014-0521-x","article-title":"Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh","volume":"12","author":"Ahmed","year":"2015","journal-title":"Landslides"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.apgeog.2018.07.004","article-title":"Assessing tropical cyclone risks using geospatial techniques","volume":"98","author":"Hoque","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.scitotenv.2019.07.132","article-title":"Tropical cyclone risk assessment using geospatial techniques for the eastern coastal region of Bangladesh","volume":"692","author":"Hoque","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Dayal, K., Deo, R., and Apan, A.A. (2017). Drought Modelling Based on Artificial Intelligence and Neural Network Algorithms: A Case Study in Queensland, Australia. Climate Change Adaptation in Pacific Countries, Springer.","DOI":"10.1007\/978-3-319-50094-2_11"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1002\/joc.1649","article-title":"Comparison of suitable drought indices for climate change impacts assessment over Australia towards resource management","volume":"28","author":"Mpelasoka","year":"2008","journal-title":"Int. J. Climatol. J. R. Meteorol. Soc."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1007\/s00477-010-0424-x","article-title":"Current drought and future hydroclimate projections in southeast Australia and implications for water resources management","volume":"25","author":"Chiew","year":"2011","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1864","DOI":"10.1002\/joc.6307","article-title":"Spatial assessment of meteorological drought features over different climate regions in Iran","volume":"40","author":"Sharafati","year":"2019","journal-title":"Int. J. Clim."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"107611","DOI":"10.1016\/j.agrformet.2019.06.010","article-title":"Copula based assessment of meteorological drought characteristics: Regional investigation of Iran","volume":"276","author":"Nabaei","year":"2019","journal-title":"Agric. For. Meteorol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s10584-016-1644-y","article-title":"Perceived and projected flood risk and adaptation in coastal Southeast Queensland, Australia","volume":"136","author":"Mills","year":"2016","journal-title":"Clim. Chang."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1071\/RJ18052","article-title":"Overcoming drought vulnerability in rangeland communities: Lessons from central-western Queensland","volume":"41","author":"Phelps","year":"2019","journal-title":"Rangel. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1318","DOI":"10.1007\/s12665-016-6124-1","article-title":"Statistical and spatial analysis of landslide susceptibility maps with different classification systems","volume":"75","author":"Baeza","year":"2016","journal-title":"Environ. Earth Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1038\/nclimate2067","article-title":"Global warming and changes in drought","volume":"4","author":"Trenberth","year":"2014","journal-title":"Nat. Clim. Chang."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.jhydrol.2010.07.012","article-title":"A review of drought concepts","volume":"391","author":"Mishra","year":"2010","journal-title":"J. Hydrol."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"135957","DOI":"10.1016\/j.scitotenv.2019.135957","article-title":"Assessing drought vulnerability using geospatial techniques in northwestern part of Bangladesh","volume":"705","author":"Hoque","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/j.jenvman.2016.10.050","article-title":"Development and evaluation of a comprehensive drought index","volume":"185","author":"Esfahanian","year":"2017","journal-title":"J. Environ. Manag."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"369","DOI":"10.5194\/isprsarchives-XL-1-W5-369-2015","article-title":"Mapping Regional Drought Vulnerability: A Case Study","volume":"40","author":"Karamouz","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s12517-018-3971-y","article-title":"Assessment of drought risk index using drought hazard and vulnerability indices","volume":"11","author":"Nasrollahi","year":"2018","journal-title":"Arab. J. Geosci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Dikshit, A., Pradhan, B., and Alamri, A.M. (2020). Temporal Hydrological Drought Index Forecasting for New South Wales, Australia Using Machine Learning Approaches. Atmosphere, 11.","DOI":"10.3390\/atmos11060585"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6243","DOI":"10.1002\/2016WR019106","article-title":"A probabilistic prediction network for hydrological drought identification and environmental flow assessment","volume":"52","author":"Liu","year":"2016","journal-title":"Water Resour. Res."},{"key":"ref_59","first-page":"5216","article-title":"Surface runoff estimation and mapping using remote sensing and geographic information system","volume":"3","author":"Pal","year":"2011","journal-title":"Int. J. Adv. Sci. Technol."},{"key":"ref_60","first-page":"97","article-title":"GIS-based approach to estimate surface runoff in small catchments: A case study","volume":"35","author":"Vojtek","year":"2016","journal-title":"Quaest. Geogr."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.agrformet.2018.06.027","article-title":"Diverse responses of vegetation growth to meteorological drought across climate zones and land biomes in northern China from 1981 to 2014","volume":"262","author":"Xu","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_63","unstructured":"Stone, R.C., and Potgieter, A. (2008). Drought risks and vulnerability in rainfed agriculture: Example of a case study in Australia. Options Mediterr., 29\u201340."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"125960","DOI":"10.1016\/j.jhydrol.2021.125960","article-title":"Drought monitoring using high spatial resolution soil moisture data over Australia in 2015\u20132019","volume":"594","author":"Fang","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_65","first-page":"55","article-title":"Assessment of SMADI and SWDI agricultural drought indices using remotely sensed root zone soil moisture","volume":"380","author":"Pablos","year":"2018","journal-title":"Proc. Int. Assoc. Hydrol. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s11135-009-9223-1","article-title":"Global supplier selection using fuzzy analytic hierarchy process and fuzzy goal programming","volume":"44","author":"Ku","year":"2010","journal-title":"Qual. Quant."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.jclepro.2013.02.010","article-title":"Integrated fuzzy multi criteria decision making method and multi-objective programming approach for supplier selection and order allocation in a green supply chain","volume":"47","author":"Kannan","year":"2013","journal-title":"J. Clean. Prod."},{"key":"ref_68","first-page":"83","article-title":"Decision making with the analytic hierarchy process","volume":"1","author":"Saaty","year":"2008","journal-title":"Int. J. Serv. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/0377-2217(95)00300-2","article-title":"Applications of the extent analysis method on fuzzy AHP","volume":"95","author":"Chang","year":"1996","journal-title":"Eur. J. Oper. Res."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Saaty, T. (1980). The Analytic Hierarchy Process, McGraw-Hill International.","DOI":"10.21236\/ADA214804"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1007\/s11069-014-1491-y","article-title":"Coastal vulnerability assessment using analytical hierarchical process for South Gujarat coast, India","volume":"76","author":"Mahapatra","year":"2015","journal-title":"Nat. Hazards"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.scitotenv.2019.02.422","article-title":"Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods","volume":"668","author":"Bui","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"134656","DOI":"10.1016\/j.scitotenv.2019.134656","article-title":"Capability and robustness of novel hybridized models used for drought hazard modeling in southeast Queensland, Australia","volume":"718","author":"Rahmati","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jhydrol.2014.10.047","article-title":"Human and climate impacts on the 21st century hydrological drought","volume":"526","author":"Wanders","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"463","DOI":"10.2166\/nh.2014.105","article-title":"Assessing droughts using meteorological drought indices in Victoria, Australia","volume":"46","author":"Rahmat","year":"2015","journal-title":"Hydrol. Res."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1175\/JHM544.1","article-title":"Modeling the recent evolution of global drought and projections for the twenty-first century with the Hadley Centre climate model","volume":"7","author":"Burke","year":"2006","journal-title":"J. Hydrometeorol."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.eja.2014.10.003","article-title":"Heat stress in cereals: Mechanisms and modelling","volume":"64","author":"Rezaei","year":"2015","journal-title":"Eur. J. Agron."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zeng, Z., Wu, W., Li, Z., Zhou, Y., Guo, Y., and Huang, H. (2019). Agricultural Drought Risk Assessment in Southwest China. Water, 11.","DOI":"10.3390\/w11051064"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"100686","DOI":"10.1016\/j.ejrh.2020.100686","article-title":"Learning from the past\u2013Using palaeoclimate data to better understand and manage drought in South East Queensland (SEQ), Australia","volume":"29","author":"Kiem","year":"2020","journal-title":"J. Hydrol. Reg. 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