{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T10:41:03Z","timestamp":1788432063234,"version":"build-2803163510"},"reference-count":92,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T00:00:00Z","timestamp":1668643200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100005692","name":"Hakim Sabzevari University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005692","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Environ Sci Pollut Res"],"DOI":"10.1007\/s11356-022-23982-x","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T11:03:08Z","timestamp":1668682988000},"page":"27965-27979","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border)"],"prefix":"10.1007","volume":"30","author":[{"given":"Sima","family":"Pourhashemi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ali Zangane","family":"Asadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahdi","family":"Boroughani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein","family":"Azadi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,17]]},"reference":[{"issue":"4","key":"23982_CR1","doi-asserted-by":"crossref","first-page":"1469","DOI":"10.1007\/s13146-019-00494-3","volume":"34","author":"A Abyat","year":"2019","unstructured":"Abyat A, Azhdari A, Kia HA, Joudaki M (2019) Khuzestan plain continental sabkhas, southwest Iran. Carbonates Evaporites 34(4):1469\u20131487","journal-title":"Carbonates Evaporites"},{"issue":"26","key":"23982_CR2","first-page":"16","volume":"7","author":"M Akbari","year":"2017","unstructured":"Akbari M, Bashiri M, Rangavar A (2017) Application of Data Mining Algorithms to Appreciate Sensitivity and Spatial Zoning Prone to Floating View in Khorasan Razavi Display Basins. J Environ Erosion Res 7(26):16\u201342","journal-title":"J Environ Erosion Res"},{"issue":"9","key":"23982_CR3","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.3390\/su9091526","volume":"9","author":"A Al-Dousari","year":"2017","unstructured":"Al-Dousari A, Doronzo D, Ahmed M (2017) Types, indications and impact evaluation of sand and dust storms trajectories in the Arabian Gulf. Sustainability 9(9):1526","journal-title":"Sustainability"},{"key":"23982_CR4","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.jenvman.2018.11.019","volume":"232","author":"H Alilou","year":"2019","unstructured":"Alilou H, Rahmati O, Singh VP, Choubin B, Pradhan B, Keesstra S, Sadeghi SH (2019) Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. J Environ Manage 232:22\u201336","journal-title":"J Environ Manage"},{"key":"23982_CR5","doi-asserted-by":"crossref","unstructured":"Arabameri A, Chandra Pal S, Rezaie F, Chakrabortty R, Saha A, Blaschke T, Di Napoli M, Ghorbanzadeh O, Thi Ngo PT (2022)\u00a0Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International 37(16):4594\u20134627","DOI":"10.1080\/10106049.2021.1892210"},{"key":"23982_CR6","doi-asserted-by":"publisher","unstructured":"Arkian F (2017) Long-term variations of aerosols concentration over ten populated cities in iran based on satellite data. Hydrol Curr Res 8https:\/\/doi.org\/10.4172\/2157-7587.1000274","DOI":"10.4172\/2157-7587.1000274"},{"issue":"6","key":"23982_CR7","doi-asserted-by":"crossref","first-page":"2606","DOI":"10.1002\/2015GL067327","volume":"43","author":"MC Baddock","year":"2016","unstructured":"Baddock MC, Ginoux P, Bullard JE, Gill TE (2016) Do MODIS-defined dust sources have a geomorphological signature? Geophys Res Lett 43(6):2606\u20132613","journal-title":"Geophys Res Lett"},{"key":"23982_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2020.117299","author":"AD Boloorani","year":"2020","unstructured":"Boloorani AD, Kazemi Y, Sadeghi A, Shorabeh SN, Argany M (2020) Identification of dust sources using long term satellite and climatic data: A case study of Tigris and Euphrates basin. Atmos Environ. https:\/\/doi.org\/10.1016\/j.atmosenv.2020.117299","journal-title":"Atmos Environ"},{"issue":"9","key":"23982_CR9","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1109\/LGRS.2019.2949132","volume":"17","author":"M Boroughani","year":"2019","unstructured":"Boroughani M, Hashemi H, Hosseini SH, Pourhashemi S, Berndtsson R (2019) Desiccating Lake Urmia: a new dust source of regional importance. IEEE Geosci Remote Sens Lett 17(9):1483\u20131487","journal-title":"IEEE Geosci Remote Sens Lett"},{"key":"23982_CR10","doi-asserted-by":"crossref","first-page":"101059","DOI":"10.1016\/j.ecoinf.2020.101059","volume":"56","author":"M Boroughani","year":"2020","unstructured":"Boroughani M, Pourhashemi S, Hashemi H, Salehi M, Amirahmadi A, Asadi MAZ, Berndtsson R (2020) Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping. Eco Inform 56:101059","journal-title":"Eco Inform"},{"issue":"11","key":"23982_CR11","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1007\/s40333-021-0023-3","volume":"13","author":"M Boroughani","year":"2021","unstructured":"Boroughani M, Pourhashemi S, Gholami H, Kaskaoutis DG (2021) Predicting of dust storm source by combining remote sensing, statistic-based predictive models and game theory in the Sistan watershed, southwestern Asia. J Arid Land 13(11):1103\u20131121","journal-title":"J Arid Land"},{"key":"23982_CR12","doi-asserted-by":"crossref","first-page":"106605","DOI":"10.1016\/j.compag.2021.106605","volume":"192","author":"M Boroughani","year":"2022","unstructured":"Boroughani M, Mohammadi M, Mirchooli F, Fiedler S (2022) Assessment of the impact of dust aerosols on crop and water loss in the Great Salt Desert in Iran. Comput Electron Agric 192:106605","journal-title":"Comput Electron Agric"},{"key":"23982_CR13","doi-asserted-by":"crossref","unstructured":"Cao H, Liu J, Wang G, Yang G, Luo L (2015) Identification of sand and dust storm source areas in Iran. Journal of Arid Land 7(5):567\u2013578","DOI":"10.1007\/s40333-015-0127-8"},{"issue":"5","key":"23982_CR14","doi-asserted-by":"crossref","first-page":"e0239690","DOI":"10.1371\/journal.pone.0239690","volume":"16","author":"A Chaudhary","year":"2021","unstructured":"Chaudhary A, Mriganka Sh, Bhupendra SA, Gopal SR (2021) Ageratina adenophora and Lantana camara in Kailash Sacred Landscape, India: Current distribution and future climatic scenarios through modeling. PLoS One 16(5):e0239690","journal-title":"PLoS One"},{"key":"23982_CR15","doi-asserted-by":"crossref","first-page":"104833","DOI":"10.1016\/j.catena.2020.104833","volume":"196","author":"X Chen","year":"2021","unstructured":"Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833","journal-title":"CATENA"},{"key":"23982_CR16","doi-asserted-by":"crossref","unstructured":"Crawford MM, Dortch JM, Koch HJ, Killen AA, Zhu J, Zhu Y, Bryson LS, Haneberg WC (2021) Using landslide-inventory mapping for a combined bagged-trees and logistic-regression approach to determining landslide susceptibility in eastern Kentucky, USA.\u00a0Q J Eng Geol Hydrogeol\u00a054(4)","DOI":"10.1144\/qjegh2020-177"},{"key":"23982_CR17","first-page":"53","volume":"74","author":"S Darvand","year":"2021","unstructured":"Darvand S, Khosravi H, Keshtkar H et al (2021) Comparison of machine learning models to prioritize susceptible areas to dust production. J Range Watershed Manag 74:53\u201368","journal-title":"J Range Watershed Manag"},{"key":"23982_CR18","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.pce.2014.02.002","volume":"67","author":"F Dube","year":"2014","unstructured":"Dube F, Nhapi I, Murwira A, Gumindoga W, Goldin J, Mashauri DA (2014) Potential of weight of evidence modelling for gully erosion hazard assessment in Mbire District-Zimbabwe. Phys Chem Earth, Parts a\/b\/c 67:145\u2013152","journal-title":"Phys Chem Earth, Parts a\/b\/c"},{"key":"23982_CR19","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.apr.2020.08.029","volume":"12","author":"Z Ebrahimi-khusfi","year":"2021","unstructured":"Ebrahimi-khusfi Z, Taghizadeh-mehrjardi R, Mirakbari M (2021) Evaluation of machine learning models for predicting the temporal variations of dust storm index in arid regions of Iran. Atmos Pollut Res 12:134\u2013147. https:\/\/doi.org\/10.1016\/j.apr.2020.08.029","journal-title":"Atmos Pollut Res"},{"key":"23982_CR20","doi-asserted-by":"publisher","unstructured":"Ebrahimi-Khusfi Z, Nafarzadegan AR, Dargahian F (2021) Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques. Ecol Ind 125https:\/\/doi.org\/10.1016\/j.ecolind.2021.107499","DOI":"10.1016\/j.ecolind.2021.107499"},{"key":"23982_CR21","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.buildenv.2018.02.038","volume":"134","author":"C Feng","year":"2018","unstructured":"Feng C, Janssen H (2018) Hygric properties of porous building materials (III): Impact factors and data processing methods of the capillary absorption test. Build Environ 134:21\u201334","journal-title":"Build Environ"},{"issue":"1","key":"23982_CR22","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3390\/rs11010004","volume":"11","author":"S Feuerstein","year":"2019","unstructured":"Feuerstein S, Schepanski K (2019) Identification of dust sources in a Saharan dust hotspot and their implementation in a dust-emission model. Remote Sens 11(1):4","journal-title":"Remote Sens"},{"key":"23982_CR23","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.aeolia.2016.11.001","volume":"24","author":"DBK Francis","year":"2017","unstructured":"Francis DBK, Flamant C, Chaboureau JP, Banks J, Cuesta J, Brindley H, Oolman L (2017) Dust emission and transport over Iraq associated with the summer Shamal winds. Aeol Res 24:15\u201331","journal-title":"Aeol Res"},{"key":"23982_CR24","doi-asserted-by":"publisher","first-page":"105364","DOI":"10.1016\/j.atmosres.2020.105364","volume":"250","author":"D Francis","year":"2021","unstructured":"Francis D, Chaboureau J-P, Nelli N, Cuesta J, Alshamsi N, Temimi M, Pauluis O, Xue L (2021) Summertime dust storms over the Arabian Peninsula and impacts on radiation, circulation, cloud development and rain. Atmos Res 250:105364. https:\/\/doi.org\/10.1016\/j.atmosres.2020.105364","journal-title":"Atmos Res"},{"key":"23982_CR25","first-page":"1","volume":"19","author":"JH Friedman","year":"1991","unstructured":"Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1\u201367","journal-title":"Ann Stat"},{"key":"23982_CR26","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.bdr.2017.07.003","volume":"9","author":"R Genuer","year":"2017","unstructured":"Genuer R, Poggi J-M, Tuleau-Malot C, Villa-Vialaneix N (2017) Random forests for big data. Big Data Res 9:28\u201346","journal-title":"Big Data Res"},{"key":"23982_CR27","doi-asserted-by":"crossref","first-page":"138090","DOI":"10.1016\/j.scitotenv.2020.138090","volume":"723","author":"H Gholami","year":"2020","unstructured":"Gholami H, Rahimi S, Fathabadi A, Habibi S, Collins AL (2020a) Mapping the spatial sources of atmospheric dust using GLUE and Monte Carlo simulation. Sci Total Environ 723:138090","journal-title":"Sci Total Environ"},{"key":"23982_CR28","doi-asserted-by":"crossref","first-page":"104716","DOI":"10.1016\/j.atmosres.2019.104716","volume":"233","author":"H Gholami","year":"2020","unstructured":"Gholami H, Mohamadifar A, Collins AL (2020b) Spatial mapping of the provenance of storm dust: application of data mining and ensemble modelling. Atmos Res 233:104716","journal-title":"Atmos Res"},{"issue":"9","key":"23982_CR29","doi-asserted-by":"crossref","first-page":"101173","DOI":"10.1016\/j.apr.2021.101173","volume":"12","author":"H Gholami","year":"2021","unstructured":"Gholami H, Mohammadifar A, Malakooti H, Esmaeilpour Y, Golzari S, Mohammadi F, Li Y, Song Y, Kaskaoutis DG, Fitzsimmons KE, Collins AL (2021) Integrated modelling for mapping spatial sources of dust in central Asia-An important dust source in the global atmospheric system. Atmos Pollut Res 12(9):101173","journal-title":"Atmos Pollut Res"},{"key":"23982_CR30","doi-asserted-by":"publisher","unstructured":"Gholami H, Mohammadifar A, Collins AL (2019) Spatial mapping of the provenance of storm dust: application of data mining and ensemble modelling Hamid. Atmos Res 104716https:\/\/doi.org\/10.1016\/j.atmosres.2019.104716","DOI":"10.1016\/j.atmosres.2019.104716"},{"key":"23982_CR31","first-page":"52","volume":"6","author":"PQ Giang","year":"2020","unstructured":"Giang PQ, Trang NTM, Anh TTH, Binh NT (2020) Prediction of economic loss of rice production due to flood inundation under climate change impacts using a modeling approach: A case study in Ha Tinh Province, Vietnam. Clim Chang 6:52\u201363","journal-title":"Clim Chang"},{"issue":"4","key":"23982_CR32","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1037\/xge0000920","volume":"150","author":"R Gomila","year":"2021","unstructured":"Gomila R (2021) Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J Exp Psychol Gen 150(4):700","journal-title":"J Exp Psychol Gen"},{"issue":"2","key":"23982_CR33","first-page":"1","volume":"4","author":"D Goossens","year":"2014","unstructured":"Goossens D, Buck B (2014) Dynamics of dust clouds produced by off-road vehicle driving. J Earth Sci Geotech Eng 4(2):1\u201321","journal-title":"J Earth Sci Geotech Eng"},{"key":"23982_CR34","unstructured":"Goudie AS (2018) Human impact on the natural environment. John Wiley & Sons"},{"key":"23982_CR35","doi-asserted-by":"crossref","unstructured":"Guo P, Lam JC, Li VO (2018) A novel machine learning approach for identifying the drivers of domestic electricity users\u2019 price responsiveness. University of Cambridge, Faculty of Economics","DOI":"10.1016\/j.apenergy.2018.11.014"},{"key":"23982_CR36","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.geomorph.2013.09.013","volume":"204","author":"M Hahnenberger","year":"2014","unstructured":"Hahnenberger M, Nicoll K (2014) Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, USA. Geomorphology 204:657\u2013672","journal-title":"Geomorphology"},{"key":"23982_CR37","doi-asserted-by":"crossref","unstructured":"Hao F, Tan W, Jiang LI, Zhang L, Zhao X, Zou Y, Hu Y, Luo X, Jiang X, McIntyre RS, Tran B (2020) Do psychiatric patients experience more psychiatric symptoms during COVID-19 pandemic and lockdown? A case-control study with service and research implications for immunopsychiatry. Brain Behav Immun 87:100\u2013106","DOI":"10.1016\/j.bbi.2020.04.069"},{"key":"23982_CR38","doi-asserted-by":"crossref","unstructured":"Hassangavyar MB, Damaneh HE, Pham QB, Linh NTT, Tiefenbacher J, Bach QV (2022) Evaluation of re-sampling methods on performance of machine learning models to predict landslide susceptibility. Geocarto International 37(10):2772\u20132794","DOI":"10.1080\/10106049.2020.1837257"},{"issue":"10","key":"23982_CR39","doi-asserted-by":"crossref","first-page":"4476","DOI":"10.1021\/cr500446g","volume":"115","author":"CL Heald","year":"2015","unstructured":"Heald CL, Spracklen DV (2015) Land use change impacts on air quality and climate. Chem Rev 115(10):4476\u20134496","journal-title":"Chem Rev"},{"issue":"7","key":"23982_CR40","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s12524-018-0774-2","volume":"46","author":"P Heidarian","year":"2018","unstructured":"Heidarian P, Azhdari A, Joudaki M, Khatooni JD, Firoozjaei SF (2018) Integrating remote sensing, GIS, and sedimentology techniques for identifying dust storm sources: a case study in Khuzestan, Iran. J Indian Soc Remote Sens 46(7):1113\u20131124","journal-title":"J Indian Soc Remote Sens"},{"issue":"2","key":"23982_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/2Fs12517-015-2094-y","volume":"9","author":"H Hong","year":"2016","unstructured":"Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9(2):1\u201326. https:\/\/doi.org\/10.1007\/2Fs12517-015-2094-y","journal-title":"Arab J Geosci"},{"issue":"12","key":"23982_CR42","doi-asserted-by":"crossref","first-page":"124029","DOI":"10.1088\/1748-9326\/ab574e","volume":"14","author":"M Javadian","year":"2019","unstructured":"Javadian M, Behrangi A, Sorooshian A (2019) Impact of drought on dust storms: case study over Southwest Iran. Environ Res Lett 14(12):124029","journal-title":"Environ Res Lett"},{"key":"23982_CR43","doi-asserted-by":"crossref","first-page":"147040","DOI":"10.1016\/j.scitotenv.2021.147040","volume":"783","author":"C Jiang","year":"2021","unstructured":"Jiang C, Fan W, Yu N, Liu E (2021) Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model. Sci Total Environ 783:147040","journal-title":"Sci Total Environ"},{"issue":"14","key":"23982_CR44","doi-asserted-by":"crossref","first-page":"5277","DOI":"10.1080\/01431161.2021.1912433","volume":"42","author":"P Jiao","year":"2021","unstructured":"Jiao P, Wang J, Chen X, Ruan J, Ye X, Alavi AH (2021) Next-generation remote sensing and prediction of sand and dust storms: State-of-the-art and future trends. Int J Remote Sens 42(14):5277\u20135316","journal-title":"Int J Remote Sens"},{"key":"23982_CR45","doi-asserted-by":"crossref","first-page":"107019","DOI":"10.1016\/j.geomorph.2019.107019","volume":"353","author":"T Kandakji","year":"2020","unstructured":"Kandakji T, Thomas E, Jeffrey A (2020) Identifying and characterizing dust point sources in the southwestern United States using remote sensing and GIS. Geomorphology 353:107019","journal-title":"Geomorphology"},{"key":"23982_CR46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.scitotenv.2020.142461","volume":"755","author":"T Kandakji","year":"2021","unstructured":"Kandakji T, Gill T, Lee J (2021) Drought and land use\/land cover impact on dust sources in Southern Great Plains and Chihuahuan Desert of the U.S.: Inferring anthropogenic effect. Sci Total Environ 755:1\u201313","journal-title":"Sci Total Environ"},{"key":"23982_CR47","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.atmosenv.2018.10.063","volume":"198","author":"B Karimi","year":"2019","unstructured":"Karimi B, Samadi S (2019) Mortality and hospitalizations due to cardiovascular and respiratory diseases associated with air pollution in Iran: A systematic review and meta-analysis. Atmos Environ 198:438\u2013447","journal-title":"Atmos Environ"},{"key":"23982_CR48","doi-asserted-by":"crossref","unstructured":"Kok JF, Ridley DA, Zhou Q, Miller RL, Zhao C, Heald CL, Ward DS, Albani S, Haustein K (2017) Smaller desert dust cooling effect estimated from analysis of dust size and abundance. Nat Geosci 10(4):274\u2013278","DOI":"10.1038\/ngeo2912"},{"issue":"27","key":"23982_CR49","doi-asserted-by":"crossref","first-page":"4611","DOI":"10.1016\/j.atmosenv.2011.05.065","volume":"45","author":"EH Lee","year":"2011","unstructured":"Lee EH, Sohn BJ (2011) Recent increasing trend in dust frequency over Mongolia and Inner Mongolia regions and its association with climate and surface condition change. Atmos Environ 45(27):4611\u20134616","journal-title":"Atmos Environ"},{"issue":"1\u20132","key":"23982_CR50","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.geomorph.2007.12.016","volume":"105","author":"JA Lee","year":"2009","unstructured":"Lee JA, Gill TE, Mulligan KR, Acosta MD, Perez AE (2009) Land use\/land cover and point sources of the 15 December 2003 dust storm in southwestern North America. Geomorphology 105(1\u20132):18\u201327","journal-title":"Geomorphology"},{"issue":"3","key":"23982_CR51","doi-asserted-by":"crossref","first-page":"456","DOI":"10.3390\/rs13030456","volume":"13","author":"J Lee","year":"2021","unstructured":"Lee J, Shi YR, Cai C, Ciren P, Wang J, Gangopadhyay A, Zhang Z (2021) Machine learning based algorithms for global dust aerosol detection from satellite images: inter-comparisons and evaluation. Remote Sens 13(3):456","journal-title":"Remote Sens"},{"key":"23982_CR52","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/19475705.2017.1308971","volume":"8","author":"JC Lee-Sunmin Kim","year":"2017","unstructured":"Lee-Sunmin Kim JC, Jung HS, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomat Nat Hazard Risk 8:1185\u20131203","journal-title":"Geomat Nat Hazard Risk"},{"issue":"9","key":"23982_CR53","doi-asserted-by":"crossref","first-page":"4564","DOI":"10.1029\/2017JD027667","volume":"123","author":"L Li","year":"2018","unstructured":"Li L, Sokolik IN (2018) The dust direct radiative impact and its sensitivity to the land surface state and key minerals in the WRF-Chem-DuMo Model: a case study of dust storms in Central Asia. J Geophys Res: Atmospheres 123(9):4564\u20134582","journal-title":"J Geophys Res: Atmospheres"},{"issue":"21","key":"23982_CR54","doi-asserted-by":"crossref","first-page":"e2020GL088950","DOI":"10.1029\/2020GL088950","volume":"47","author":"X Lin","year":"2020","unstructured":"Lin X, Chang H, Wang K, Zhang G, Meng G (2020) Machine learning for source identification of dust on the Chinese Loess Plateau. Geophys Res Lett 47(21):e2020GL088950","journal-title":"Geophys Res Lett"},{"key":"23982_CR55","first-page":"1","volume":"243","author":"Y Liu","year":"2020","unstructured":"Liu Y, Wang G, Hu Z, Shi P, Lyu Y, Zhang G, Gu Y, Liu Y, Hong C, Guo L, Hu X, Yang Y, Zhang X, Zheng H, Liu L (2020) Dust storm susceptibility on different land surface types in arid and semiarid regions of northern China. Atmos Res 243:1\u201310","journal-title":"Atmos Res"},{"issue":"2","key":"23982_CR56","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s12517-012-0795-z","volume":"7","author":"MA Manap","year":"2014","unstructured":"Manap MA, Nampak H, Pradhan B, Lee S, Sulaiman WNA, Ramli MF (2014) Application of probabilistic-based frequency ratio model in groundwater potential mapping using remote sensing data and GIS. Arab J Geosci 7(2):711\u2013724. https:\/\/doi.org\/10.1007\/s12517-012-0795-z","journal-title":"Arab J Geosci"},{"issue":"3","key":"23982_CR57","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1080\/17445647.2020.1805807","volume":"17","author":"C Martinello","year":"2021","unstructured":"Martinello C, Cappadonia C, Conoscenti C, Agnesi V, Rotigliano E (2021) Optimal slope units partitioning in landslide susceptibility mapping. J Maps 17(3):152\u2013162","journal-title":"J Maps"},{"issue":"7360","key":"23982_CR58","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1038\/nature10310","volume":"476","author":"A Martinez-Garcia","year":"2011","unstructured":"Martinez-Garcia A, Rosell-Mele A, Jaccard SL, Geibert W, Sigman DM, Haug GH (2011) Southern Ocean dust\u2013climate coupling over the past four million years. Nature 476(7360):312\u2013315","journal-title":"Nature"},{"issue":"5","key":"23982_CR59","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1029\/2019GH000187","volume":"3","author":"J Martinich","year":"2019","unstructured":"Martinich J, Roman H, Mickley LJ (2019) Effects of increasing aridity on ambient dust and public health in the US southwest under climate change. GeoHealth 3(5):127\u2013144","journal-title":"GeoHealth"},{"key":"23982_CR60","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.aeolia.2016.12.001","volume":"24","author":"NJ Middleton","year":"2017","unstructured":"Middleton NJ (2017) Desert dust hazards: a global review. Aeolian Res 24:53\u201363","journal-title":"Aeolian Res"},{"key":"23982_CR61","doi-asserted-by":"crossref","unstructured":"Miller SD (2003) A consolidated technique for enhancing desert dust storms with MODIS. Geophys Res Lett 30(20)","DOI":"10.1029\/2003GL018279"},{"key":"23982_CR62","doi-asserted-by":"publisher","DOI":"10.1080\/10106049.2020.1829101","author":"A Mosavi","year":"2020","unstructured":"Mosavi A, Golshan M, Janizadeh S et al (2020) Ensemble models of GLM, FDA, MARS, and RF for flood and erosion susceptibility mapping: a priority assessment of sub-basins. Geocarto Int. https:\/\/doi.org\/10.1080\/10106049.2020.1829101","journal-title":"Geocarto Int"},{"key":"23982_CR63","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.atmosenv.2017.11.016","volume":"173","author":"S Namdari","year":"2018","unstructured":"Namdari S, Karimi N, Sorooshian A, Mohammadi G, Sehatkashani S (2018) Impacts of climate and synoptic fluctuations on dust storm activity over the Middle East. Atmos Environ 173:265\u2013276","journal-title":"Atmos Environ"},{"key":"23982_CR64","doi-asserted-by":"crossref","first-page":"107370","DOI":"10.1016\/j.buildenv.2020.107370","volume":"187","author":"M Namdari","year":"2021","unstructured":"Namdari M, Lee CS, Haghighat F (2021) Active ozone removal technologies for a safe indoor environment: a comprehensive review. Build Environ 187:107370","journal-title":"Build Environ"},{"key":"23982_CR65","doi-asserted-by":"crossref","unstructured":"Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1\u20132):11\u201320","DOI":"10.1016\/j.enggeo.2009.10.001"},{"issue":"8","key":"23982_CR66","doi-asserted-by":"crossref","first-page":"830","DOI":"10.3390\/f11080830","volume":"11","author":"VH Nhu","year":"2020","unstructured":"Nhu VH, Mohammadi A, Shahabi H, Ahmad BB, Al-Ansari N, Shirzadi A, Geertsema M, Kress VR, Karimzadeh S, Valizadeh Kamran K, Chen W (2020) Landslide detection and susceptibility modeling on cameron highlands (Malaysia): a comparison between random forest, logistic regression and logistic model tree algorithms. Forests 11(8):830","journal-title":"Forests"},{"issue":"5","key":"23982_CR67","doi-asserted-by":"crossref","first-page":"673","DOI":"10.1007\/s11135-006-9018-6","volume":"41","author":"RM O\u2019brien","year":"2007","unstructured":"O\u2019brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673\u2013690","journal-title":"Qual Quant"},{"key":"23982_CR68","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.3390\/su9071157","volume":"9","author":"S Park","year":"2017","unstructured":"Park S, Hamm S-Y, Jeon H-T, Kim J (2017) Evaluation of logistic regression and multivariate adaptive regression spline models for groundwater potential mapping using R and GIS. Sustainability 9:1157. https:\/\/doi.org\/10.3390\/su9071157","journal-title":"Sustainability"},{"key":"23982_CR69","doi-asserted-by":"crossref","unstructured":"Quevedo RP, Maciel DA, Uehara TDT, Vojtek M, Renno CD, Pradhan B, Vojtekova J, Pham QB (2021) Consideration of spatial heterogeneity in landslide susceptibility mapping using geographical random forest model. Geocarto International 1\u201324","DOI":"10.1080\/10106049.2021.1996637"},{"key":"23982_CR70","doi-asserted-by":"crossref","first-page":"139508","DOI":"10.1016\/j.scitotenv.2020.139508","volume":"737","author":"O Rahmati","year":"2020","unstructured":"Rahmati O, Mohammadi F, Ghiasi SS, Tiefenbacher J, Moghaddam DD, Coulon F, Nalivan OA, Bui DT (2020) Identifying sources of dust aerosol using a new framework based on remote sensing and modelling. Sci Total Environ 737:139508","journal-title":"Sci Total Environ"},{"key":"23982_CR71","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.scitotenv.2013.06.045","volume":"463","author":"A Rashki","year":"2013","unstructured":"Rashki A, Kaskaoutis DG, Goudie AS, Kahn RA (2013) Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Sci Total Environ 463:552\u2013564","journal-title":"Sci Total Environ"},{"key":"23982_CR72","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aeolia.2020.100655","volume":"48","author":"A Rashki","year":"2021","unstructured":"Rashki A, Middleton NJ, Goudie AS (2021) Dust storms in Iran \u2013 Distribution, causes, frequencies and impacts. Aeol Res 48:1\u201317","journal-title":"Aeol Res"},{"issue":"20","key":"23982_CR73","doi-asserted-by":"crossref","first-page":"3284","DOI":"10.3390\/rs12203284","volume":"12","author":"P Roy","year":"2020","unstructured":"Roy P, Chandra Pal S, Arabameri A, Chakrabortty R, Pradhan B, Chowdhuri I, Lee S, Tien Bui D (2020) Novel ensemble of multivariate adaptive regression spline with spatial logistic regression and boosted regression tree for gully erosion susceptibility. Remote Sens 12(20):3284","journal-title":"Remote Sens"},{"key":"23982_CR74","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.rse.2012.03.019","volume":"123","author":"K Schepanski","year":"2012","unstructured":"Schepanski K, Tegen I, Macke A (2012) Comparison of satellite based observations of Saharan dust source areas. Remote Sens Environ 123:90\u201397","journal-title":"Remote Sens Environ"},{"issue":"1","key":"23982_CR75","first-page":"3","volume":"20","author":"M Schonlau","year":"2020","unstructured":"Schonlau M, Zou RY (2020) The random forest algorithm for statistical learning. Stand Genomic Sci 20(1):3\u201329","journal-title":"Stand Genomic Sci"},{"key":"23982_CR76","doi-asserted-by":"publisher","DOI":"10.1016\/j.atmosenv.2020.117736","author":"A Shaheen","year":"2020","unstructured":"Shaheen A, Wu R, Aldabash M (2020) Long-term AOD trend assessment over the Eastern Mediterranean region: a comparative study including a new merged aerosol product. Atmos Environ. https:\/\/doi.org\/10.1016\/j.atmosenv.2020.117736","journal-title":"Atmos Environ"},{"key":"23982_CR77","doi-asserted-by":"crossref","unstructured":"Shano L, Raghuvanshi TK, Meten M (2021) Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia","DOI":"10.21203\/rs.3.rs-191158\/v1"},{"issue":"10","key":"23982_CR78","first-page":"1084","volume":"5","author":"V Sissakian","year":"2013","unstructured":"Sissakian V, Al-Ansari N, Knutsson S (2013) Sand and dust storm events in Iraq. J Nat Sci 5(10):1084\u20131094","journal-title":"J Nat Sci"},{"key":"23982_CR79","doi-asserted-by":"publisher","DOI":"10.1016\/j.apr.2018.03.001","author":"M Soni","year":"2018","unstructured":"Soni M, Payra S, Verma S (2018) Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters. Atmos Poll Res. https:\/\/doi.org\/10.1016\/j.apr.2018.03.001","journal-title":"Atmos Poll Res"},{"key":"23982_CR80","doi-asserted-by":"crossref","first-page":"104028","DOI":"10.1016\/j.jaridenv.2019.104028","volume":"173","author":"F Taheri","year":"2020","unstructured":"Taheri F, Forouzani M, Yazdanpanah M, Ajili A (2020) How farmers perceive the impact of dust phenomenon on agricultural production activities: A Q-methodology study. J Arid Environ 173:104028","journal-title":"J Arid Environ"},{"key":"23982_CR81","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.aeolia.2013.02.006","volume":"10","author":"K Vickery","year":"2013","unstructured":"Vickery K, Eckardt F (2013) Dust emission controls on the lower Kuiseb River valley, central Namib. Aeolian Res 10:125\u2013133. https:\/\/doi.org\/10.1016\/j.aeolia.2013.02.006","journal-title":"Aeolian Res"},{"issue":"18","key":"23982_CR82","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1029\/2008JD011541","volume":"114","author":"AL Walker","year":"2009","unstructured":"Walker AL, Liu M, Miller SD, Richardson KA, Westphal DL (2009) Development of a dust source database for mesoscale forecasting in Southwest Asia. J Geophys Res 114(18):1\u201324. https:\/\/doi.org\/10.1029\/2008JD011541","journal-title":"J Geophys Res"},{"issue":"1","key":"23982_CR83","doi-asserted-by":"publisher","first-page":"76","DOI":"10.3390\/rs8010076","volume":"8","author":"L Wang","year":"2016","unstructured":"Wang L, Tremblay D, Zhang B, Han Y (2016) Fast and accurate collocation of the visible infrared imaging radiometer suite measurements with cross-track infrared sounder. Remote Sens 8(1):76. https:\/\/doi.org\/10.3390\/rs8010076","journal-title":"Remote Sens"},{"issue":"6","key":"23982_CR84","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1007\/s10064-020-01730-0","volume":"79","author":"L Wang","year":"2020","unstructured":"Wang L, Wu C, Gu X, Liu H, Mei G, Zhang W (2020) Probabilistic stability analysis of earth dam slope under transient seepage using multivariate adaptive regression splines. Bull Eng Geol Env 79(6):2763\u20132775","journal-title":"Bull Eng Geol Env"},{"issue":"1","key":"23982_CR85","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.gsf.2020.02.012","volume":"12","author":"H Wang","year":"2021","unstructured":"Wang H, Zhang L, Yin K, Luo H, Li J (2021a) Landslide identification using machine learning. Geosci Front 12(1):351\u2013364","journal-title":"Geosci Front"},{"key":"23982_CR86","doi-asserted-by":"crossref","first-page":"103182","DOI":"10.1016\/j.coldregions.2020.103182","volume":"181","author":"T Wang","year":"2021","unstructured":"Wang T, Ma H, Liu J, Luo Q, Wang Q, Zhan Y (2021b) Assessing frost heave susceptibility of gravelly soils based on multivariate adaptive regression splines model. Cold Reg Sci Technol 181:103182","journal-title":"Cold Reg Sci Technol"},{"issue":"3","key":"23982_CR87","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1002\/2016MS000723","volume":"8","author":"C Wu","year":"2016","unstructured":"Wu C, Lin Z, He J, Zhang M, Liu X, Zhang R, Brown H (2016) A process-oriented evaluation of dust emission parameterizations in CESM: Simulation of a typical severe dust storm in E ast A sia. J Adv Model Earth Syst 8(3):1432\u20131452","journal-title":"J Adv Model Earth Syst"},{"key":"23982_CR88","first-page":"102475","volume":"103","author":"H Wu","year":"2021","unstructured":"Wu H, Lin A, Xing X, Song D, Li Y (2021) Identifying core driving factors of urban land use change from global land cover products and POI data using the random forest method. Int J Appl Earth Obs Geoinf 103:102475","journal-title":"Int J Appl Earth Obs Geoinf"},{"key":"23982_CR89","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2018.09.006","volume":"146","author":"L Yang","year":"2018","unstructured":"Yang L, Jin S, Danielson P, Homer C, Gass L, Bender SM, Case A, Costello C, Dewitz J, Fry J, Funk M, Granneman B, Liknes GC, Rigge M, Xian G (2018) A new generation of the United States National Land Cover Database: requirements, research priorities, design, and implementation strategies. ISPRS J Photogramm Remote Sens 146:108\u2013123","journal-title":"ISPRS J Photogramm Remote Sens"},{"key":"23982_CR90","unstructured":"Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey. University of Melbourne, Department, 200"},{"key":"23982_CR91","doi-asserted-by":"crossref","unstructured":"Yu H, Chin M, Yuan T, Bian Hremer LA, Prospero JM, Omar A, Winker D, Yang Y, Zhang Y, Zhang Z, Zhao C (2015) The fertilizing role of African dust in the Amazon rainforest: a first multiyear assessment based on CALIPSO LIDAR observations. Geophys Res Lett 42:1984\u20131991","DOI":"10.1002\/2015GL063040"},{"issue":"1","key":"23982_CR92","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1080\/19475705.2021.1896584","volume":"12","author":"S Zhang","year":"2021","unstructured":"Zhang S, Li C, Peng J, Peng D, Xu Q, Zhang Q, Bate B (2021) GIS-based soil planar slide susceptibility mapping using logistic regression and neural networks: a typical red mudstone area in southwest China. Geomat Nat Haz Risk 12(1):852\u2013879","journal-title":"Geomat Nat Haz Risk"}],"container-title":["Environmental Science and Pollution Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-022-23982-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11356-022-23982-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11356-022-23982-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,1]],"date-time":"2023-12-01T09:10:15Z","timestamp":1701421815000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11356-022-23982-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,17]]},"references-count":92,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["23982"],"URL":"https:\/\/doi.org\/10.1007\/s11356-022-23982-x","relation":{},"ISSN":["1614-7499"],"issn-type":[{"value":"1614-7499","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,17]]},"assertion":[{"value":"12 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors have not submitted the manuscript to a preprint server before submitting it to\u00a0Environmental Science and Pollution Research. We confirm that this manuscript has not been published elsewhere and is not under consideration by another journal. All authors have approved the manuscript and agreed with its submission to\u00a0Environmental Science and Pollution Research.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}