[en] A dust storm is a major environmental problem affecting many arid regions worldwide. The novel contribution of this study is combining indicators extracted from RS- and statistic-based predictive models to spatial mapping of land susceptibility to dust emissions in a very important dust source area in the borders of Iran and Iraq (Khuzestan province in Iran and Al-Basrah and Maysan provinces in Iraq). In this research, remote sensing (RS) techniques and machine learning techniques, including multivariate adaptive regression spline (MARS), random forest (RF), and logistic regression (LR), were used for dust source identification and susceptibility map preparation. To this end, 152 DSA for the period of 2005-2020 were identified in the study area. Of these DSA data, 70% was assigned to the Dust Source Susceptibility Mapping (DSSM) (training dataset) and 30% to model validation. Consequently, six factors (i.e., soil, lithology, slope, normalized vegetation differential index (NDVI), geomorphology, and land use units) were prepared as DSA's independent and effective variables. The results of all three models indicated that land use had the most impact on DSA. The validation results of these models using the test data showed sub-curves of 0.92, 0.86, and 0.76 for the RF, MARS, and LR models, respectively. Also, results showed that the RF model outperformed MARS (AUC = 0.89) and LR (AUC = 0.78) methods. In all three models, high and very high susceptibility classes generally covered a large percentage of the case study. The highest percentage of dust source points was also in this susceptibility category. Overall, the results of this study can be useful for planners and managers to control and reduce the risk of negative dust consequences.
Disciplines :
Agriculture & agronomy
Author, co-author :
Pourhashemi, Sima; Department of Geography, Hakim Sabzevari University, Sabzevar, Iran
Asadi, Mohammad Ali Zangane; Department of Geography, Hakim Sabzevari University, Sabzevar, Iran. ma.zanganehasadi@hsu.ac.ir
Boroughani, Mahdi; Research Center for Geosciences and Social Studies, Hakim Sabzevari University, Sabzevar, Iran
Azadi, Hossein ; Université de Liège - ULiège > TERRA Research Centre > Modélisation et développement ; Department of Geography, Ghent University, Ghent, Belgium
Language :
English
Title :
Mapping of dust source susceptibility by remote sensing and machine learning techniques (case study: Iran-Iraq border).
Publication date :
February 2023
Journal title :
Environmental Science and Pollution Research
ISSN :
0944-1344
eISSN :
1614-7499
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Abyat A, Azhdari A, Kia HA, Joudaki M (2019) Khuzestan plain continental sabkhas, southwest Iran. Carbonates Evaporites 34(4):1469–1487
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):16–42
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
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 232:22–36
Arabameri A, Chandra Pal S, Rezaie F, Chakrabortty R, Saha A, Blaschke T, Di Napoli M, Ghorbanzadeh O, Thi Ngo PT (2022) Decision tree based ensemble machine learning approaches for landslide susceptibility mapping. Geocarto International 37(16):4594–4627
Arkian F (2017) Long-term variations of aerosols concentration over ten populated cities in iran based on satellite data. Hydrol Curr Res 810.4172/2157-7587.1000274
Baddock MC, Ginoux P, Bullard JE, Gill TE (2016) Do MODIS-defined dust sources have a geomorphological signature? Geophys Res Lett 43(6):2606–2613
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. 10.1016/j.atmosenv.2020.117299 DOI: 10.1016/j.atmosenv.2020.117299
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):1483–1487
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
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):1103–1121
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
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):567–578
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
Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. CATENA 196:104833
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. Q J Eng Geol Hydrogeol 54(4)
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 74:53–68
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 67:145–152
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 12:134–147. 10.1016/j.apr.2020.08.029 DOI: 10.1016/j.apr.2020.08.029
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 12510.1016/j.ecolind.2021.107499
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 134:21–34
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
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 24:15–31
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. 10.1016/j.atmosres.2020.105364 DOI: 10.1016/j.atmosres.2020.105364
Friedman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–67
Genuer R, Poggi J-M, Tuleau-Malot C, Villa-Vialaneix N (2017) Random forests for big data. Big Data Res 9:28–46
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
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
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
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 104716 https://doi.org/10.1016/j.atmosres.2019.104716
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 6:52–63
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
Goossens D, Buck B (2014) Dynamics of dust clouds produced by off-road vehicle driving. J Earth Sci Geotech Eng 4(2):1–21
Goudie AS (2018) Human impact on the natural environment. John Wiley & Sons
Guo P, Lam JC, Li VO (2018) A novel machine learning approach for identifying the drivers of domestic electricity users’ price responsiveness. University of Cambridge, Faculty of Economics
Hahnenberger M, Nicoll K (2014) Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, USA. Geomorphology 204:657–672
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 87:100–106
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):2772–2794
Heald CL, Spracklen DV (2015) Land use change impacts on air quality and climate. Chem Rev 115(10):4476–4496
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):1113–1124
Hong H, Naghibi SA, Pourghasemi HR, Pradhan B (2016) GIS-based landslide spatial modeling in Ganzhou City, China. Arab J Geosci 9(2):1–26. 10.1007/2Fs12517-015-2094-y DOI: 10.1007/2Fs12517-015-2094-y
Javadian M, Behrangi A, Sorooshian A (2019) Impact of drought on dust storms: case study over Southwest Iran. Environ Res Lett 14(12):124029
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
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):5277–5316
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
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 755:1–13
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 198:438–447
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):274–278
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):4611–4616
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(1–2):18–27
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
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 8:1185–1203
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):4564–4582
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
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 243:1–10
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):711–724. 10.1007/s12517-012-0795-z DOI: 10.1007/s12517-012-0795-z
Martinello C, Cappadonia C, Conoscenti C, Agnesi V, Rotigliano E (2021) Optimal slope units partitioning in landslide susceptibility mapping. J Maps 17(3):152–162
Martinez-Garcia A, Rosell-Mele A, Jaccard SL, Geibert W, Sigman DM, Haug GH (2011) Southern Ocean dust–climate coupling over the past four million years. Nature 476(7360):312–315
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):127–144
Middleton NJ (2017) Desert dust hazards: a global review. Aeolian Res 24:53–63
Miller SD (2003) A consolidated technique for enhancing desert dust storms with MODIS. Geophys Res Lett 30(20)
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. 10.1080/10106049.2020.1829101 DOI: 10.1080/10106049.2020.1829101
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 173:265–276
Namdari M, Lee CS, Haghighat F (2021) Active ozone removal technologies for a safe indoor environment: a comprehensive review. Build Environ 187:107370
Nandi A, Shakoor A (2010) A GIS-based landslide susceptibility evaluation using bivariate and multivariate statistical analyses. Eng Geol 110(1–2):11–20
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
O’brien RM (2007) A caution regarding rules of thumb for variance inflation factors. Qual Quant 41(5):673–690
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. 10.3390/su9071157 DOI: 10.3390/su9071157
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 1–24
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
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 463:552–564
Rashki A, Middleton NJ, Goudie AS (2021) Dust storms in Iran – Distribution, causes, frequencies and impacts. Aeol Res 48:1–17
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
Schepanski K, Tegen I, Macke A (2012) Comparison of satellite based observations of Saharan dust source areas. Remote Sens Environ 123:90–97
Schonlau M, Zou RY (2020) The random forest algorithm for statistical learning. Stand Genomic Sci 20(1):3–29
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. 10.1016/j.atmosenv.2020.117736 DOI: 10.1016/j.atmosenv.2020.117736
Shano L, Raghuvanshi TK, Meten M (2021) Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia
Sissakian V, Al-Ansari N, Knutsson S (2013) Sand and dust storm events in Iraq. J Nat Sci 5(10):1084–1094
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. 10.1016/j.apr.2018.03.001 DOI: 10.1016/j.apr.2018.03.001
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
Vickery K, Eckardt F (2013) Dust emission controls on the lower Kuiseb River valley, central Namib. Aeolian Res 10:125–133. 10.1016/j.aeolia.2013.02.006 DOI: 10.1016/j.aeolia.2013.02.006
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):1–24. 10.1029/2008JD011541 DOI: 10.1029/2008JD011541
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. 10.3390/rs8010076 DOI: 10.3390/rs8010076
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):2763–2775
Wang H, Zhang L, Yin K, Luo H, Li J (2021a) Landslide identification using machine learning. Geosci Front 12(1):351–364
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
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):1432–1452
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
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 146:108–123
Yesilnacar EK (2005) The application of computational intelligence to landslide susceptibility mapping in Turkey. University of Melbourne, Department, 200
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 42:1984–1991
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):852–879
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