Abstract
Floods have become increasingly frequent and devastating in recent decades, posing unignorable risks as highly destructive natural hazards. To effectively manage and mitigate these risks, accurate flood hazard mapping is crucial. Machine learning models have emerged as valuable approaches for flood hazard assessment. In this study, six machine learning (ML) models, including Maximum Entropy, Support Vector Machine, Extreme Gradient Boosting (XGB), Random Forest (RF), multi-layer perceptron, and Naive Bayes, were utilized to evaluate urban flood hazard in Zaio, NE Morocco, and estimate the flood presence extent. Nine flood conditioning factors were used as input variables. Historical flood presence and absence data were employed for models training and testing, incorporating 663 flood presence and absence locations dating back to past flood events. Performance evaluation metrics such as Kappa statistic, accuracy, sensitivity, specificity, and area under the curve (AUC) were calculated for each model. RF (AUC = 0.92) and XGB (AUC = 0.9) models showed excellent classification capabilities, surpassing the performance of the other models, while the other models exhibited lower but recognizable performances. Additionally, the hazard presence extent maps generated by the ML models exhibited a decent alignment with a historical flood event maps created by the hydrodynamic and the cellular automata models. The results imply that ML models offer effective solutions for mapping urban flood hazards. The innovative integration of various ensemble and single ML models demonstrates their potential in urban flood hazard susceptibility and extent mapping, effectively surpassing the limitations associated with limited availability of hydrologic/hydraulic data and computational burden. These mapped results can be instrumental for local authorities in shaping mitigation strategies in the city of Zaio.
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Abu El-Magd SA (2022) Random forest and Naïve Bayes approaches as tools for flash flood hazard susceptibility prediction, South Ras El-Zait, Gulf of Suez Coast, Egypt. Arab J Geosci 15(3):217. https://doi.org/10.1007/s12517-022-09531-3
Aden-Antoniów F, Frank WB, Seydoux L (2022) An adaptable random forest model for the declustering of earthquake catalogs. J Geophys Res: Solid Earth 127(2):e2021JB023254. https://doi.org/10.1029/2021JB023254
Youssef AM, Biswajeet Pradhan AD, Mahdi AM (2022) Comparative study of convolutional neural network (CNN) and support vector machine (SVM) for flood susceptibility mapping: a case study at Ras Gharib, Red Sea, Egypt. Geocarto Int 37(26):11088–11115. https://doi.org/10.1080/10106049.2022.2046866
Akay H (2021) Flood hazards susceptibility mapping using statistical, fuzzy logic, and MCDM methods. Soft Comput 25(14):9325–9346. https://doi.org/10.1007/s00500-021-05903-1
Al-Aizari AR, Al-Masnay YA, Aydda A, Zhang J, Ullah K, Islam ARMT, Habib T, Kaku DU, Nizeyimana JC, Al-Shaibah B, Khalil YM, AL-Hameedi WMM, Liu X (2022) Assessment analysis of flood susceptibility in tropical desert area: a case study of Yemen. Remote Sens 14(16):4050. https://doi.org/10.3390/rs14164050
Andaryani S, Nourani V, Haghighi AT, Keesstra S (2021) Integration of hard and soft supervised machine learning for flood susceptibility mapping. J Environ Manag 291:112731. https://doi.org/10.1016/j.jenvman.2021.112731
Aydin HE, Iban MC (2023) Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with shapley additive explanations. Nat Hazards 116(3):2957–2991. https://doi.org/10.1007/s11069-022-05793-y
Balica SF, Popescu I, Beevers L, Wright NG (2013) Parametric and physically based modelling techniques for flood risk and vulnerability assessment: a comparison. Environ Model Softw 41:84–92. https://doi.org/10.1016/j.envsoft.2012.11.002
Biau G, Scornet E (2016) A random forest guided tour. Test 25(2):197–227. https://doi.org/10.1007/s11749-016-0481-7
Biswas S, Mukhopadhyay BP, Bera A (2020) Delineating groundwater potential zones of agriculture dominated landscapes using GIS based AHP techniques: a case study from Uttar Dinajpur district, West Bengal. Environ Earth Sci 79(12):302. https://doi.org/10.1007/s12665-020-09053-9
Booker DJ, Snelder TH (2012) Comparing methods for estimating flow duration curves at ungauged sites. J Hydrol 434–435:78–94. https://doi.org/10.1016/j.jhydrol.2012.02.031
Boushaba F, Grari A, Chourak M, Regad Y, Elkihel B (2021) Numerical simulation of the flood risk of the deviation hydraulic structure at Saidia (North–East Morocco). In: Hajji B, Mellit A, Marco Tina G, Rabhi A, Launay J, Naimi SE (eds), Proceedings of the 2nd international conference on electronic engineering and renewable energy systems, pp 659–665. Springer Singapore
Bravo-López E, Fernández Del Castillo T, Sellers C, Delgado-García J (2022) Landslide susceptibility mapping of landslides with artificial neural networks: multi-approach analysis of backpropagation algorithm applying the neuralnet package in Cuenca, Ecuador. Remote Sens 14(14):3495. https://doi.org/10.3390/rs14143495
Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324
Brunner MI, Swain DL, Wood RR, Willkofer F, Done JM, Gilleland E, Ludwig R (2021) An extremeness threshold determines the regional response of floods to changes in rainfall extremes. Commun Earth Environ 2(1):173. https://doi.org/10.1038/s43247-021-00248-x
Cabrera JS, Lee HS (2020) Flood risk assessment for Davao Oriental in the Philippines using geographic information system-based multi-criteria analysis and the maximum entropy model. J Flood Risk Manag 13(2):e12607. https://doi.org/10.1111/jfr3.12607
Chen M, Liu Q, Chen S, Liu Y, Zhang C-H, Liu R (2019) XGBoost-based algorithm interpretation and application on post-fault transient stability status prediction of power system. IEEE Access 7:13149–13158. https://doi.org/10.1109/ACCESS.2019.2893448
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, pp 785–794. https://doi.org/10.1145/2939672.2939785
Chen W, Li Y, Xue W, Shahabi H, Li S, Hong H, Wang X, Bian H, Zhang S, Pradhan B, Ahmad BB (2020) Modeling flood susceptibility using data-driven approaches of Naïve Bayes tree, alternating decision tree, and random forest methods. Sci Total Environ 701:134979. https://doi.org/10.1016/j.scitotenv.2019.134979
Costache R, Arabameri A, Moayedi H, Pham QB, Santosh M, Nguyen H, Pandey M, Pham BT (2022) Flash-flood potential index estimation using fuzzy logic combined with deep learning neural network, Naïve Bayes, XGBoost and classification and regression tree. Geocarto Int 37(23):6780–6807. https://doi.org/10.1080/10106049.2021.1948109
Costache R, Tien Bui D (2019) Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: a case study at the Putna river catchment of Romania. Sci Total Environ 691:1098–1118. https://doi.org/10.1016/j.scitotenv.2019.07.197
Council NR (2009) Mapping the zone: improving flood map accuracy. The National Academies Press. https://doi.org/10.17226/12573
Damaševičius R (2010) Optimization of SVM parameters for recognition of regulatory DNA sequences. Top 18(2):339–353. https://doi.org/10.1007/s11750-010-0152-x
Darabi H, Haghighi AT, Rahmati O, Shahrood AJ, Rouzbeh S, Pradhan B, Bui DT (2021) A hybridized model based on neural network and swarm intelligence-grey wolf algorithm for spatial prediction of urban flood-inundation. J Hydrol 603:126854. https://doi.org/10.1016/j.jhydrol.2021.126854
Dash S, Vijay R, Gupta R (2022) Steady and unsteady hydrodynamic simulation of pili river as a potential flood warning system using HEC-RAS. In: Laishram B, Tawalare A (eds) Recent advancements in civil engineering. Springer Nature Singapore, pp 929–949
Debie E, Shafi K (2019) Implications of the curse of dimensionality for supervised learning classifier systems: theoretical and empirical analyses. Pattern Anal Appl 22(2):519–536. https://doi.org/10.1007/s10044-017-0649-0
Dhaliwal SS, Nahid A-A, Abbas R (2018) Effective intrusion detection system using XGBoost. Information 9(7):149. https://doi.org/10.3390/info9070149
Dung NB, Long NQ, Goyal R, An DT, Minh DT (2022) The role of factors affecting flood hazard zoning using analytical hierarchy process: a review. Earth Syst Environ 6(3):697–713. https://doi.org/10.1007/s41748-021-00235-4
Eini M, Kaboli HS, Rashidian M, Hedayat H (2020) Hazard and vulnerability in urban flood risk mapping: machine learning techniques and considering the role of urban districts. Int J Dis Risk Reduct 50:101687. https://doi.org/10.1016/j.ijdrr.2020.101687
El Baida M, Boushaba F, Chourak M, Sabar H (2023) Application of physically-based and experimentally calibrated method for flood hazard assessment: case study of Zaio, Morocco. E3S Web Conf 469:00013. https://doi.org/10.1051/e3sconf/202346900013
Fan J, Wang X, Wu L, Zhou H, Zhang F, Yu X, Lu X, Xiang Y (2018) 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.2018.02.087
Feng B, Zhang Y, Bourke R (2021) Urbanization impacts on flood risks based on urban growth data and coupled flood models. Nat Hazards 106(1):613–627. https://doi.org/10.1007/s11069-020-04480-0
Feng L, Hong W (2009) On the principle of maximum entropy and the risk analysis of disaster loss. Appl Math Model 33(7):2934–2938. https://doi.org/10.1016/j.apm.2008.10.002
Ghaffari A, Abdollahi H, Khoshayand MR, Bozchalooi IS, Dadgar A, Rafiee-Tehrani M (2006) Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int J Pharm 327(1):126–138. https://doi.org/10.1016/j.ijpharm.2006.07.056
Grari A, Chourak M, Boushaba F, Cherif S, Alonso EG (2019) Numerical characterization of torrential floods in the plain of Saïdia (North–East of Morocco). Arab J Geosci 12(10):321. https://doi.org/10.1007/s12517-019-4288-1
Guidolin M, Chen AS, Ghimire B, Keedwell EC, Djordjević S, Savić DA (2016) A weighted cellular automata 2D inundation model for rapid flood analysis. Environ Model Softw 84:378–394. https://doi.org/10.1016/j.envsoft.2016.07.008
Guo C, Chen X, Chen Y, Yu C (2022) Multi-stage attentive network for motion deblurring via binary cross-entropy loss. Entropy 24(10):1414. https://doi.org/10.3390/e24101414
Hasan MH, Ahmed A, Nafee KM, Hossen MA (2023) Use of machine learning algorithms to assess flood susceptibility in the coastal area of Bangladesh. Ocean Coast Manag 236:106503. https://doi.org/10.1016/j.ocecoaman.2023.106503
JatiSuroso MIHPB (2019) Prediction of flood areas using the logistic regression method (case study of the provinces Banten, DKI Jakarta, and West Java). J Phys Conf Ser 1367(1):12087. https://doi.org/10.1088/1742-6596/1367/1/012087
Huynh QT, Nguyen UD, Irazabal LB, Ghassemian N, Tran BQ (2015) Optimization of an accelerometer and gyroscope-based fall detection algorithm. J Sens 2015:452078. https://doi.org/10.1155/2015/452078
Ighile EH, Shirakawa H, Tanikawa H (2022) Application of GIS and machine learning to predict flood areas in Nigeria. Sustainability 14(9):5039. https://doi.org/10.3390/su14095039
Janizadeh S, Chandra Pal S, Saha A, Chowdhuri I, Ahmadi K, Mirzaei S, Mosavi AH, Tiefenbacher JP (2021) Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future. J Environ Manag 298:113551. https://doi.org/10.1016/j.jenvman.2021.113551
Jones A, Kuehnert J, Fraccaro P, Meuriot O, Ishikawa T, Edwards B, Stoyanov N, Remy SL, Weldemariam K, Assefa S (2023) AI for climate impacts: applications in flood risk. Npj Clim Atmos Sci 6(1):63. https://doi.org/10.1038/s41612-023-00388-1
Kabenge M, Elaru J, Wang H, Li F (2017) Characterizing flood hazard risk in data-scarce areas, using a remote sensing and GIS-based flood hazard index. Nat Hazards 89(3):1369–1387. https://doi.org/10.1007/s11069-017-3024-y
Kelly DL, Kolstad CD (1999) Bayesian learning, growth, and pollution. J Econ Dyn Control 23(4):491–518. https://doi.org/10.1016/S0165-1889(98)00034-7
Kramer O (2016) Scikit-learn. In: Machine Learning for Evolution Strategies, pp 45–53. Springer International Publishing. https://doi.org/10.1007/978-3-319-33383-0_5
Lei X, Chen W, Panahi M, Falah F, Rahmati O, Uuemaa E, Kalantari Z, Ferreira CSS, Rezaie F, Tiefenbacher JP, Lee S, Bian H (2021) Urban flood modeling using deep-learning approaches in Seoul, South Korea. J Hydrol 601:126684. https://doi.org/10.1016/j.jhydrol.2021.126684
Li Y, Osei FB, Hu T, Stein A (2023) Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustain Cities Soc 88:104307. https://doi.org/10.1016/j.scs.2022.104307
Liu Y, Pang Z, Karlsson M, Gong S (2020) Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control. Build Environ 183:107212. https://doi.org/10.1016/j.buildenv.2020.107212
Ma M, Zhao G, He B, Li Q, Dong H, Wang S, Wang Z (2021) XGBoost-based method for flash flood risk assessment. J Hydrol 598:126382. https://doi.org/10.1016/j.jhydrol.2021.126382
Malik S, Pal SC, Arabameri A, Chowdhuri I, Saha A, Chakrabortty R, Roy P, Das B (2021) GIS-based statistical model for the prediction of flood hazard susceptibility. Environ Dev Sustain 23(11):16713–16743. https://doi.org/10.1007/s10668-021-01377-1
Merghadi A, Yunus AP, Dou J, Whiteley J, ThaiPham B, Bui DT, Avtar R, Abderrahmane B (2020) Machine learning methods for landslide susceptibility studies: a comparative overview of algorithm performance. Earth Sci Rev 207:103225. https://doi.org/10.1016/j.earscirev.2020.103225
Mishra SK, Singh VP (2003) SCS-CN Method. In: soil conservation service curve number (SCS-CN) methodology, pp 84–146. Springer Netherlands. https://doi.org/10.1007/978-94-017-0147-1_2
Mo H, Sun H, Liu J, Wei S (2019) Developing window behavior models for residential buildings using XGBoost algorithm. Energy Build 205:109564. https://doi.org/10.1016/j.enbuild.2019.109564
Mobley W, Sebastian A, Blessing R, Highfield WE, Stearns L, Brody SD (2021) Quantification of continuous flood hazard using random forest classification and flood insurance claims at large spatial scales: a pilot study in southeast Texas. Nat Hazard 21(2):807–822. https://doi.org/10.5194/nhess-21-807-2021
Mohanty N, John AL-S, Manmatha R, Rath TM (2013) Chapter 10—shape-based image classification and retrieval. In: Rao CR, Govindaraju V (eds), Handbook of Statistics (Vol 31, pp 249–267). Elsevier. https://doi.org/10.1016/B978-0-444-53859-8.00010-2
Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Model 62(4):275–293. https://doi.org/10.1016/0304-3800(92)90003-W
Mudashiru RB, Sabtu N, Abustan I, Balogun W (2021) Flood hazard mapping methods: a review. J Hydrol 603:126846. https://doi.org/10.1016/j.jhydrol.2021.126846
Mukherjee F, Singh D (2020) Detecting flood prone areas in Harris County: a GIS based analysis. GeoJournal 85(3):647–663. https://doi.org/10.1007/s10708-019-09984-2
Nigussie TA, Altunkaynak A (2019) Modeling the effect of urbanization on flood risk in Ayamama Watershed, Istanbul, Turkey, using the MIKE 21 FM model. Nat Hazards 99(2):1031–1047. https://doi.org/10.1007/s11069-019-03794-y
Nikolaychuk O, Pestova J, Yurin A (2024) Wildfire susceptibility mapping in baikal natural territory using random forest. Forests 15(1):170. https://doi.org/10.3390/f15010170
Norallahi M, Kaboli HS (2021) Urban flood hazard mapping using machine learning models: GARP, RF MaxEnt and NB. Nat Hazards 106(1):119–137. https://doi.org/10.1007/s11069-020-04453-3
Omid Rahmati HZ, Besharat M (2016) Flood hazard zoning in Yasooj region, Iran, using GIS and multi-criteria decision analysis. Geomat Nat Hazards Risk 7(3):1000–1017. https://doi.org/10.1080/19475705.2015.1045043
Özay B, Orhan O (2023) Flood susceptibility mapping by best–worst and logistic regression methods in Mersin, Turkey. Environ Sci Pollut Res 30(15):45151–45170. https://doi.org/10.1007/s11356-023-25423-9
Pham BT, Tien Bui D, Pourghasemi HR, Indra P, Dholakia MB (2017) Landslide susceptibility assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of Naïve Bayes, multilayer perceptron neural networks, and functional trees methods. Theoret Appl Climatol 128(1):255–273. https://doi.org/10.1007/s00704-015-1702-9
Pham QB, Pal SC, Chakrabortty R, Norouzi A, Golshan M, Ogunrinde AT, Janizadeh S, Khedher KM, Anh DT (2021) Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas. Geomat Nat Hazards Risk 12(1):2607–2628. https://doi.org/10.1080/19475705.2021.1968510
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190(3):231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
Pradhan B, Lee S (2010) Regional landslide susceptibility analysis using back-propagation neural network model at Cameron Highland, Malaysia. Landslides 7(1):13–30. https://doi.org/10.1007/s10346-009-0183-2
Ramkar P, Yadav SM (2021) Flood risk index in data-scarce river basins using the AHP and GIS approach. Nat Hazards 109(1):1119–1140. https://doi.org/10.1007/s11069-021-04871-x
Raza MQ, Khosravi A (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings. Renew Sustain Energy Rev 50:1352–1372. https://doi.org/10.1016/j.rser.2015.04.065
Razavi-Termeh SV, Seo M, Sadeghi-Niaraki A, Choi S-M (2023) Flash flood detection and susceptibility mapping in the Monsoon period by integration of optical and radar satellite imagery using an improvement of a sequential ensemble algorithm. Weather Clim Extrem 41:100595. https://doi.org/10.1016/j.wace.2023.100595
Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: IEEE international conference on neural networks, vol 1, pp 586–591. https://doi.org/10.1109/ICNN.1993.298623
Sahana M, Rehman S, Sajjad H, Hong H (2020) Exploring effectiveness of frequency ratio and support vector machine models in storm surge flood susceptibility assessment: a study of Sundarban Biosphere Reserve, India. CATENA 189:104450. https://doi.org/10.1016/j.catena.2019.104450
Saloux E, Candanedo JA (2018) Forecasting district heating demand using machine learning algorithms. Energy Proc 149:59–68. https://doi.org/10.1016/j.egypro.2018.08.169
Samuel Adelabu OM, Adam E (2015) Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods. Geocarto Int 30(7):810–821. https://doi.org/10.1080/10106049.2014.997303
Shanmuganathan S (2016) Artificial neural network modelling: an introduction. In: Shanmuganathan S, Samarasinghe S (eds), Artificial neural network modelling, pp 1–14. Springer International Publishing. https://doi.org/10.1007/978-3-319-28495-8_1
Shannon CE (2001) A mathematical theory of communication. SIGMOBILE Mob Comput Commun Rev 5(1):3–55. https://doi.org/10.1145/584091.584093
Song S, Zhan Z, Long Z, Zhang J, Yao L (2011) Comparative study of SVM methods combined with voxel selection for object category classification on fMRI data. PLoS ONE 6(2):1–11. https://doi.org/10.1371/journal.pone.0017191
Straatsma M, Huthoff F (2011) Uncertainty in 2D hydrodynamic models from errors in roughness parameterization based on aerial images. Phys Chem Earth Parts a/b/c 36(7):324–334. https://doi.org/10.1016/j.pce.2011.02.009
Lee S, 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 Hazards Risk 8(2):1185–1203. https://doi.org/10.1080/19475705.2017.1308971
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.2018.12.011
Tehrany MS, Kumar L (2018) The application of a Dempster–Shafer-based evidential belief function in flood susceptibility mapping and comparison with frequency ratio and logistic regression methods. Environ Earth Sci 77(13):490. https://doi.org/10.1007/s12665-018-7667-0
Tehrany MS, Pradhan B, Jebur MN (2014) Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J Hydrol 512:332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Terti G, Ruin I, Gourley JJ, Kirstetter P, Flamig Z, Blanchet J, Arthur A, Anquetin S (2019) Toward probabilistic prediction of flash flood human impacts. Risk Anal 39(1):140–161. https://doi.org/10.1111/risa.12921
Toufik Z, Hichame S, Farid B, Chourak M (2023) Mapping the risk of flooding of the national road N°2 at the crossing of the wadi Tamdmadt north of the city of Bni Drar. Mater Today Proc 72:3447–3453. https://doi.org/10.1016/j.matpr.2022.08.089
Tramblay Y, Badi W, Driouech F, El Adlouni S, Neppel L, Servat E (2012) Climate change impacts on extreme precipitation in Morocco. Glob Planet Change 82–83:104–114. https://doi.org/10.1016/j.gloplacha.2011.12.002
Tran VQ (2022) Hybrid gradient boosting with meta-heuristic algorithms prediction of unconfined compressive strength of stabilized soil based on initial soil properties, mix design and effective compaction. J Clean Prod 355:131683. https://doi.org/10.1016/j.jclepro.2022.131683
Vinet F, El Mehdi Saidi M, Douvinet J, Fehri N, Nasrallah W, Menad W, Mellas S (1970) Sub-chapter 3.4.1. urbanization and land use as a driver of flood risk. In: The Mediterranean region under climate change—Sub-chapter 3.4.1. Urbanization and land use as a driver of flood risk. IRD Éditions. https://books.openedition.org/irdeditions/23910
Wang X, Jin J (2001) Assessing the impact of urban growth on flooding with an integrated curve number-flow accumulation approach. Water Int 26(2):215–222. https://doi.org/10.1080/02508060108686907
Wilby RL, Keenan R (2012) Adapting to flood risk under climate change. Progress Phys Geogr: Earth Environ 36(3):348–378. https://doi.org/10.1177/0309133312438908
Wiles JJ, Levine NS (2002) A combined GIS and HEC model for the analysis of the effect of urbanization on flooding; the Swan Creek watershed, Ohio. Environ Eng Geosci 8(1):47–61. https://doi.org/10.2113/gseegeosci.8.1.47
Yabiladi.com (2007) Inondation tragique à Zaio : une jeune fille meurt noyée --- yabiladi.com. https://www.yabiladi.com/articles/details/7/inondation-tragique-zaio-jeune-fille.html
Yang J, Huang Y, Jiang X, Chen H, Liu M, Wang R (2022a) Potential geographical distribution of the edangred plant Isoetes under human activities using MaxEnt and GARP. Glob Ecol Conserv 38:e02186. https://doi.org/10.1016/j.gecco.2022.e02186
Yang S-Y, Chang C-H, Hsu C-T, Wu S-J (2022b) Variation of uncertainty of drainage density in flood hazard mapping assessment with coupled 1D–2D hydrodynamics model. Nat Hazards 111(3):2297–2315. https://doi.org/10.1007/s11069-021-05138-1
Yao X, Tham LG, Dai FC (2008) Landslide susceptibility mapping based on support vector machine: a case study on natural slopes of Hong Kong, China. Geomorphology 101(4):572–582. https://doi.org/10.1016/j.geomorph.2008.02.011
Naiji Z, Mostafa O, Amarjouf N, Rezqi H (2021) Application of two-dimensional hydraulic modelling in flood risk mapping. A case of the urban area of Zaio, Morocco. Geocarto Int 36(2):180–196. https://doi.org/10.1080/10106049.2019.1597389
Zhu R, Hu X, Hou J, Li X (2021) Application of machine learning techniques for predicting the consequences of construction accidents in China. Process Saf Environ Prot 145:293–302. https://doi.org/10.1016/j.psep.2020.08.006
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This work is part of a broader project "Enhancing Disaster Resilience in Arab Countries through Multi-Hazards Modelling and Mapping Using Machine Learning and IoT Sensors" funded by Federation of Arab Scientific Research Councils (FASRC) and Academy of scientific research and technology (ASRT)-Egypt.
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El baida, M., Boushaba, F., Chourak, M. et al. Classification machine learning models for urban flood hazard mapping: case study of Zaio, NE Morocco. Nat Hazards (2024). https://doi.org/10.1007/s11069-024-06596-z
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DOI: https://doi.org/10.1007/s11069-024-06596-z