nach oben

Earth Science Informatics

Erschienen in:

15.10.2020 | Research Article

Flood susceptibility assessment using extreme gradient boosting (EGB), Iran

verfasst von: Sajjad Mirzaei, Mehdi Vafakhah, Biswajeet Pradhan, Seyed Jalil Alavi

Erschienen in: Earth Science Informatics | Ausgabe 1/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Flood occurs as a result of high intensity and long-term rainfalls accompanied by snowmelt which flow out of the main river channel onto the flood prone areas and damage the buildings, roads, and facilities and cause life losses. This study aims to implement extreme gradient boosting (EGB) method for the first time in flood susceptibility modelling and compare its performance with three advanced benchmark models including Frequency Ratio (FR), Random Forest (RF), and Generalized Additive Model (GAM). Flood susceptibility map is an efficient tool to make decision for flood control. To do this, the altitude, slope degree, profile curvature, topographic wetness index (TWI), distance from rivers, normalized difference vegetation index, plan curvature, rainfall, land use, stream power index, and lithology were fed to the models. To run the models, 243 flood locations were detected by field surveys and national reports. The same number of locations were randomly created in the study regions and considered as non-flood locations. The flood and non-flood locations were split in 70% ratio for the training dataset and 30% ratio for the testing dataset. Both flood and non-flood locations were fed into the models and output flood susceptibility maps were produced. In order to evaluate the performance of the algorithms, receiver operating characteristics (ROC) curve was implemented. The results of the current research show that the RF model and EGB have the best performances with the area under ROC curve (AUC) of 0.985, and 0.980, followed by the GAM and FR algorithms with AUC values of 0.97, and 0.953, respectively. The results of variable importance by the RF model show that distance from rivers has an important influence on flood susceptibility mapping (FSM), followed by profile curvature, slope, TWI, and altitude. Considering the high performances of the RF and EGB models in flood susceptibility modelling, application of these models is recommended for such studies.

Vorheriger Artikel Improving flood forecasting through feature selection by a genetic algorithm – experiments based on real data from an Amazon rainforest river

Nächster Artikel Change detection in SAR images based on superpixel segmentation and image regression

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Alganci U, Sertel E, Ozdogan M, Ormeci C (2013) Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in southeastern Turkey. Photogrammetric Engineering & Remote Sensing 79:1053–1065. https://doi.org/10.14358/PERS.79.11.1053

Arabameri A, Pradhan B, Rezaei K (2019) Gully erosion zonation mapping using integrated geographically weighted regression with certainty factor and random forest models in GIS. J Environ Manag 232:928–942

Babajide Mustapha I, Saeed F (2016) Bioactive molecule prediction using extreme gradient boosting. Molecules 21:983. https://doi.org/10.3390/molecules21080983CrossRef

Basukala AK, Oldenburg C, Schellberg J, Sultanov M, Dubovyk O (2017) Towards improved land use mapping of irrigated croplands: performance assessment of different image classification algorithms and approaches. European Journal of Remote Sensing 50:187–201. https://doi.org/10.1080/22797254.2017.1308235CrossRef

Beven K, Kirkby MJ (1979) A physically based, variable contributing area model of basin hydrology. Hydrol Sci J 24:43–69

Bonham-Carter GF (1994) Geographic information systems for geoscientists-modeling with GIS. Computer Methods in the Geoscientists 13:398

Breiman LEO (2001) Random forests. Mach Learn 45:5–32

Bui DT, Ngo P-TT, Pham TD, Jaafari A, Minh NQ, Hoa PV, Samui P (2019) A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping. Catena 179:184–196

Bui DT, Pradhan B, Nampak H et al (2016) Hybrid artificial intelligence approach based on neural fuzzy inference model and metaheuristic optimization for flood susceptibilitgy modeling in a high-frequency tropical cyclone area using GIS. J Hydrol 540:317–330

Catani F, Lagomarsino D, Segoni S, Tofani V (2013) Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues. Nat Hazards Earth Syst Sci 13:2815–2831. https://doi.org/10.5194/nhess-13-2815-2013CrossRef

Chambers JM, Hastie TJ (1992) Statistical models in S. Wadsworth & Brooks/Cole Advanced Books & Software Pacific Grove, CA

Chapi K, Singh VP, Shirzadi A, Shahabi H, Bui DT, Pham BT, Khosravi K (2017) A novel hybrid artificial intelligence approach for flood susceptibility assessment. Environ Model Softw 95:229–245. https://doi.org/10.1016/j.envsoft.2017.06.012CrossRef

Chen T, Guestrin C (2016) Xgboost: a scalable tree boosting system. In: proceedings of the 22nd ACM sigkdd international conference on knowledge discovery and data mining. ACM, pp 785–794

Chen W, Chen X, Peng J, Panahi M, Lee S (2020a) Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and satin bowerbird optimizer. Geosci Front 12:93–107. https://doi.org/10.1016/j.gsf.2020.07.012CrossRef

Chen W, Hong H, Li S, Shahabi H, Wang Y, Wang X, Ahmad BB (2019) Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles. J Hydrol 575:864–873. https://doi.org/10.1016/j.jhydrol.2019.05.089CrossRef

Chen W, Li Y (2020) GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models. Catena 195:104777. https://doi.org/10.1016/j.catena.2020.104777CrossRef

Chen W, Li Y, Tsangaratos P, Shahabi H, Ilia I, Xue W, Bian H (2020b) Groundwater spring potential mapping using artificial intelligence approach based on kernel logistic regression, random forest, and alternating decision tree models. Appl Sci 10:425

Chen W, Zhao X, Tsangaratos P, Shahabi H, Ilia I, Xue W, Wang X, Ahmad BB (2020c) Evaluating the usage of tree-based ensemble methods in groundwater spring potential mapping. J Hydrol 583:124602. https://doi.org/10.1016/j.jhydrol.2020.124602CrossRef

Chen X, Chen W (2021) GIS-based landslide susceptibility assessment using optimized hybrid machine learning methods. Catena 196:104833. https://doi.org/10.1016/j.catena.2020.104833CrossRef

Choubin B, Moradi E, Golshan M, Adamowski J, Sajedi-Hosseini F, Mosavi A (2019) An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Sci Total Environ 651:2087–2096. https://doi.org/10.1016/j.scitotenv.2018.10.064CrossRef

Conoscenti C, Rotigliano E, Cama M, Caraballo-Arias NA, Lombardo L, Agnesi V (2016) Exploring the effect of absence selection on landslide susceptibility models: a case study in Sicily, Italy. Geomorphology 261:222–235. https://doi.org/10.1016/j.geomorph.2016.03.006CrossRef

Darabi H, Choubin B, Rahmati O, Torabi Haghighi A, Pradhan B, Kløve B (2019) Urban flood risk mapping using the GARP and QUEST models: a comparative study of machine learning techniques. J Hydrol 569:142–154. https://doi.org/10.1016/j.jhydrol.2018.12.002CrossRef

Dewan AM, Yamaguchi Y (2008) Effect of land cover changes on flooding: example from greater Dhaka of Bangladesh. Int J Geoinform 4:11–20

Fan J, Wang X, Wu L, Zhou H, Zhang F, Yu X, Lu X, Xiang Y (2018a) Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Convers Manag 164:102–111. https://doi.org/10.1016/j.enconman.2018.02.087CrossRef

Fan J, Wang X, Wu L, Zhou H, Zhang F, Yu X, Lu X, Xiang Y (2018b) Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in China. Energy Convers Manag 164:102–111

Georganos S, Grippa T, Vanhuysse S, Lennert M, Shimoni M, Wolff E (2018) Very high resolution object-based land use–land cover urban classification using extreme gradient boosting. IEEE Geosci Remote Sens Lett 15:607–611. https://doi.org/10.1109/LGRS.2018.2803259CrossRef

Glenn EP, Morino K, Nagler PL, Murray RS, Pearlstein S, Hultine KR (2012) Roles of saltcedar (Tamarix spp.) and capillary rise in salinizing a non-flooding terrace on a flow-regulated desert river. J Arid Environ 79:56–65. https://doi.org/10.1016/j.jaridenv.2011.11.025CrossRef

Goetz JN, Guthrie RH, Brenning A (2011) Integrating physical and empirical landslide susceptibility models using generalized additive models. Geomorphology 129:376–386. https://doi.org/10.1016/j.geomorph.2011.03.001CrossRef

Golkarian A, Naghibi SA, Kalantar B, Pradhan B (2018) Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS. Environ Monit Assess 190:149

Hastie TJ, Tibshirani RJ (1990) Generalized additive models London chapman and hall. Inc

Hong H, Naghibi SA, Dashtpagerdi MM et al (2017) A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping in China. Arab J Geosci 10:167

Hong H, Panahi M, Shirzadi A, Ma T, Liu J, Zhu AX, Chen W, Kougias I, Kazakis N (2018) Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Sci Total Environ 621:1124–1141. https://doi.org/10.1016/j.scitotenv.2017.10.114CrossRef

Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the Tafresh watershed, Iran. Sustainability 11:5426

Kamali Maskooni E, Naghibi SA, Hashemi H, Berndtsson R (2020) Application of advanced machine learning algorithms to assess groundwater potential using remote sensing-derived data. Remote Sens 12:2742

Kantakumar LN, Neelamsetti P (2015) Multi-temporal land use classification using hybrid approach. Egypt J Remote Sens Space Sci 18:289–295

Khosravi K, Pham BT, Chapi K et al (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744–755. https://doi.org/10.1016/j.ejrs.2015.09.003CrossRef

Khosravi K, Shahabi H, Pham BT, Adamowski J, Shirzadi A, Pradhan B, Dou J, Ly HB, Gróf G, Ho HL, Hong H, Chapi K, Prakash I (2019) A comparative assessment of flood susceptibility modeling using multi-criteria decision-making analysis and machine learning methods. J Hydrol 573:311–323. https://doi.org/10.1016/j.jhydrol.2019.03.073CrossRef

Kordestani MD, Naghibi SA, Hashemi H, Ahmadi K, Kalantar B, Pradhan B (2019) Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeol J 27:211–224

Lee S, Lee S, Lee M-J, Jung H-S (2018) Spatial assessment of urban flood susceptibility using data mining and geographic information system (GIS) tools. Sustainability 10:648

Lee SS, Kim J-C, Jung H-S, Lee MJ, Lee S (2017) Spatial prediction of flood susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea. Geomatics, Natural Hazards and Risk 8:1185–1203. https://doi.org/10.1080/19475705.2017.1308971CrossRef

Lei X, Chen W, Avand M, Janizadeh S, Kariminejad N, Shahabi H, Costache R, Shahabi H, Shirzadi A, Mosavi A (2020a) GIS-based machine learning algorithms for gully Erosion susceptibility mapping in a semi-arid region of Iran. Remote Sens 12:2478

Lei X, Chen W, Pham BT (2020b) Performance evaluation of gis-based artificial intelligence approaches for landslide susceptibility modeling and spatial patterns analysis. ISPRS Int J Geo Inf 9:443

Li Y, Chen W (2020) Landslide susceptibility evaluation using hybrid integration of evidential belief function and machine learning techniques. Water 12:113

Maggini R, Lehmann A, Zimmermann NE, Guisan A (2006) Improving generalized regression analysis for the spatial prediction of forest communities. J Biogeogr 33:1729–1749. https://doi.org/10.1111/j.1365-2699.2006.01465.xCrossRef

Maghsood FF, Moradi H, Bavani ARM et al (2019) Climate change impact on flood frequency and source area in northern Iran under CMIP5 scenarios. Water 11:273. https://doi.org/10.3390/w11020273CrossRef

Mirzaei S, Vafakhah M, Pradhan B, Alavi SJ (2020) An improved land use classification scheme using multi-seasonal satellite images and secondary data. ECOPERSIA 8:97–107

Motevalli A, Naghibi SA, Hashemi H, Berndtsson R, Pradhan B, Gholami V (2019) Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater. J Clean Prod 228:1248–1263

Motevalli A, Vafakhah M (2016) Flood hazard mapping using synthesis hydraulic and geomorphic properties at watershed scale. Stoch Env Res Risk A 30:1889–1900. https://doi.org/10.1007/s00477-016-1305-8CrossRef

Mousavi SM, Golkarian A, Naghibi SA et al (2017) GIS-based groundwater spring potential mapping using data mining boosted regression tree and probabilistic frequency ratio models in Iran. AIMS Geosci 3:91–115. https://doi.org/10.3934/geosci.2017.1.91CrossRef

Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q (2011) Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sens Environ 115:1145–1161. https://doi.org/10.1016/j.rse.2010.12.017CrossRef

Naghibi S, Vafakhah M, Hashemi H, Pradhan B, Alavi S (2018) Groundwater augmentation through the site selection of floodwater spreading using a data mining approach (case study: Mashhad plain, Iran). Water 10:1405

Naghibi SA, Dolatkordestani M, Rezaei A, Amouzegari P, Heravi MT, Kalantar B, Pradhan B (2019a) Application of rotation forest with decision trees as base classifier and a novel ensemble model in spatial modeling of groundwater potential. Environ Monit Assess 191:248

Naghibi SA, Hashemi H, Berndtsson R, Lee S (2020) Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors Journal of Hydrology 125197

Naghibi SA, Moghaddam DD, Kalantar B, Pradhan B, Kisi O (2017) A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping. J Hydrol 548:471–483

Naghibi SA, Moradi Dashtpagerdi M (2016) Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features. Hydrogeology journal 1–21. https://doi.org/10.1007/s10040-016-1466-z

Naghibi SA, Pourghasemi HR (2015) A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping. Water Resour Manag 29:5217–5236. https://doi.org/10.1007/s11269-015-1114-8CrossRef

Naghibi SA, Pourghasemi HR, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188:44. https://doi.org/10.1007/s10661-015-5049-6CrossRef

Naghibi SA, Vafakhah M, Hashemi H et al (2019b) Water resources management through flood spreading project suitability mapping using frequency ratio, k-nearest neighbours, and random forest algorithms. Nat Resour Res 29:1915–1933

Nandi A, Mandal A, Wilson M, Smith D (2016) Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ Earth Sci 75:465. https://doi.org/10.1007/s12665-016-5323-0CrossRef

Ngo P-T, Hoang N-D, Pradhan B, Nguyen Q, Tran X, Nguyen Q, Nguyen V, Samui P, Tien Bui D (2018) A novel hybrid swarm optimized multilayer neural network for spatial prediction of flash floods in tropical areas using Sentinel-1 SAR imagery and geospatial data. Sensors 18:3704. https://doi.org/10.3390/s18113704CrossRef

Nourani V, Komasi M (2013) A geomorphology-based ANFIS model for multi-station modeling of rainfall–runoff process. J Hydrol 490:41–55

Nourani V, Pradhan B, Ghaffari H (2014) Landslide susceptibility mapping at Zonouz Plain , Iran using genetic programming and comparison. Nat Hazards 71:523–547. https://doi.org/10.1007/s11069-013-0932-3CrossRef

Pal M (2005) Random forest classifier for remote sensing classification. Int J Remote Sens 26:217–222

Petschko H, Brenning A, Bell R, Goetz J, Glade T (2014) Assessing the quality of landslide susceptibility maps–case study Lower Austria. Nat Hazards Earth Syst Sci 14:95–118. https://doi.org/10.5194/nhess-14-95-2014CrossRef

Pourghasemi HR, Razavi-Termeh SV, Kariminejad N, Hong H, Chen W (2020) An assessment of metaheuristic approaches for flood assessment. J Hydrol 582:124536

Pourtaghi ZS, Pourghasemi HR, Aretano R, Semeraro T (2016) Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol Indic 64:72–84

Rahmati O, Naghibi SA, Shahabi H, Bui DT, Pradhan B, Azareh A, Rafiei-Sardooi E, Samani AN, Melesse AM (2018) Groundwater spring potential modelling: comprising the capability and robustness of three different modeling approaches. J Hydrol 565:248–261

Rahmati O, Pourghasemi HR (2017) Identification of critical flood prone areas in data-scarce and ungauged regions: a comparison of three data mining models. Water Resour Manag 31:1473–1487. https://doi.org/10.1007/s11269-017-1589-6CrossRef

Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province, Iran. Geocarto International 31:42–70. https://doi.org/10.1080/10106049.2015.1041559CrossRef

Sahoo GB, Ray C, De Carlo EH (2006) Use of neural network to predict flash flood and attendant water qualities of a mountainous stream on Oahu, Hawaii. J Hydrol 327:525–538

Sameen MI, Pradhan B, Lee S (2019) Self-learning random forests model for mapping groundwater yield in data-scarce areas. Nat Resour Res 28:757–775

Sangchini EK, Emami SN, Tahmasebipour N, Pourghasemi HR, Naghibi SA, Arami SA, Pradhan B (2016) Assessment and comparison of combined bivariate and AHP models with logistic regression for landslide susceptibility mapping in the Chaharmahal-e-Bakhtiari Province, Iran. Arab J Geosci 9:201. https://doi.org/10.1007/s12517-015-2258-9CrossRef

Shafapour M, Biswajeet T, Tehrany MS et al (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Env Res Risk A 29:1149–1165. https://doi.org/10.1007/s00477-015-1021-9CrossRef

Shin J-Y, Ro Y, Cha J-W, et al (2019) Assessing the Applicability of Random Forest, Stochastic Gradient Boosted Model, and Extreme Learning Machine Methods to the Quantitative Precipitation Estimation of the Radar Data: A Case Study to Gwangdeoksan Radar, South Korea, in 2018. Adv Meteorol 2019:

Tehrany MS, Jones S, Shabani F (2019) Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. Catena 175:174–192. https://doi.org/10.1016/j.catena.2018.12.011CrossRef

Tehrany MS, Lee MJ, Pradhan B, Jebur MN, Lee S (2014a) No title. Environ Earth Sci 72:4001–4015

Tehrany MS, Pradhan B, Jebur MN (2014b) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343

Tehrany MS, Pradhan B, Jebur MN (2013) Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of hydrology 504:. https://doi.org/10.1016/j.jhydrol.2013.09.034

Termeh SVR, Kornejady A, Pourghasemi HR, Keesstra S (2018) Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms. Sci Total Environ 615:438–451

Thakkar AK, Desai VR, Patel A, Potdar MB (2017) Post-classification corrections in improving the classification of land use/land cover of arid region using RS and GIS: the case of Arjuni watershed, Gujarat, India. Egypt J Remote Sens Space Sci 20:79–89

Timofeev A V, Denisov VM (2020) Machine learning based predictive maintenance of infrastructure facilities in the cryolithozone. In: Recent developments on industrial control systems resilience. Springer, pp. 49–74

Vafakhah M, Mohammad Hasani Loor S, Pourghasemi HR, Katebikord A (2020) Comparing performance of random forest and adaptive neuro-fuzzy inference system data mining models for flood susceptibility mapping. Arab J Geosci 13:417. https://doi.org/10.1007/s12517-020-05363-1CrossRef

Vorpahl P, Elsenbeer H, Märker M, Schröder B (2012) How can statistical models help to determine driving factors of landslides ? Ecol Model 239:27–39. https://doi.org/10.1016/j.ecolmodel.2011.12.007CrossRef

Wan S, Tc L, Ty C (2010) A novel data mining technique of analysis and classification for landslide problems. Nat Hazards 52:211–230. https://doi.org/10.1007/s11069-009-9366-3CrossRef

Wang G, Lei X, Chen W, Shahabi H, Shirzadi A (2020) Hybrid computational intelligence methods for landslide susceptibility mapping. Symmetry 12:325

Wang Y, Hong H, Chen W, Li S, Pamučar D, Gigović L, Drobnjak S, Bui DT, Duan H (2019a) A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sens 11:62. https://doi.org/10.3390/rs11010062CrossRef

Wang Y, Hong H, Chen W et al (2019b) A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sens 11:62

Yousefi S, Moradi HR, Pourghasemi HR, Khatami R (2017) Assessment of floodplain landuse and channel morphology within meandering reach of the Talar River in Iran using GIS and aerial photographs. Geocarto International 6049:1–14. https://doi.org/10.1080/10106049.2017.1353645CrossRef

Youssef AM, Pradhan B, Hassan AM (2011) Flash flood risk estimation along the St. Katherine road, southern Sinai, Egypt using GIS based morphometry and satellite imagery. Environ Earth Sci 62:611–623. https://doi.org/10.1007/s12665-010-0551-1CrossRef

Zhao G, Pang B, Xu Z, Peng D, Xu L (2019) Assessment of urban flood susceptibility using semi-supervised machine learning model. Sci Total Environ 659:940–949

Zhao G, Pang B, Xu Z, Yue J, Tu T (2018) Mapping flood susceptibility in mountainous areas on a national scale in China. Sci Total Environ 615:1133–1142

Zhao X, Chen W (2020a) Optimization of computational intelligence models for landslide susceptibility evaluation. Remote Sens 12:2180

Zhao X, Chen W (2020b) GIS-based evaluation of landslide susceptibility models using certainty factors and functional trees-based ensemble techniques. Appl Sci 10:16

Titel: Flood susceptibility assessment using extreme gradient boosting (EGB), Iran
verfasst von: Sajjad Mirzaei
Mehdi Vafakhah
Biswajeet Pradhan
Seyed Jalil Alavi
Publikationsdatum: 15.10.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Earth Science Informatics / Ausgabe 1/2021
Print ISSN: 1865-0473
Elektronische ISSN: 1865-0481
DOI: https://doi.org/10.1007/s12145-020-00530-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2021

Landslides triggered by the 6 September 2018 Mw 6.6 Hokkaido, Japan: an updated inventory and retrospective hazard assessment

Online detection of offsets in GPS time series

Improving flood forecasting through feature selection by a genetic algorithm – experiments based on real data from an Amazon rainforest river

Automatic mapping of river canyons using a digital elevation model and vector river data

Automated survey of selected common plant species in Thai homegardens using Google Street View imagery and a deep neural network

Energy efficiency maximization algorithm for underwater Mobile sensor networks

Premium Partner