{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T14:57:05Z","timestamp":1770821825232,"version":"3.50.1"},"reference-count":81,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T00:00:00Z","timestamp":1722211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s12145-024-01428-x","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T15:02:42Z","timestamp":1722265362000},"page":"4809-4829","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Real-time flash flood detection employing the YOLOv8 model"],"prefix":"10.1007","volume":"17","author":[{"given":"Nguyen Hong","family":"Quang","sequence":"first","affiliation":[]},{"given":"Hanna","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Namhoon","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Gihong","family":"Kim","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,29]]},"reference":[{"key":"1428_CR1","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/978-981-19-0213-0_13","volume-title":"Remote sensing application: Regional perspectives in Agriculture and Forestry","author":"T Ahamed","year":"2022","unstructured":"Ahamed T (2022) Big Data Scheme from Remote sensing applications: concluding notes for Agriculture and Forestry Applications. In: Ahamed T (ed) Remote sensing application: Regional perspectives in Agriculture and Forestry. Springer Nature Singapore, Singapore, pp 351\u2013361"},{"issue":"3","key":"1428_CR2","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.5194\/nhess-23-1157-2023","volume":"23","author":"E Alc\u00e2ntara","year":"2023","unstructured":"Alc\u00e2ntara E, Marengo JA, Mantovani J, Londe LR, San RLY, Park E, Cunha AP (2023) Deadly disasters in southeastern South America: flash floods and landslides of February 2022 in Petr\u00f3polis, Rio De Janeiro. Nat Hazards Earth Syst Sci 23(3):1157\u20131175","journal-title":"Nat Hazards Earth Syst Sci"},{"key":"1428_CR3","doi-asserted-by":"crossref","first-page":"101730","DOI":"10.1016\/j.aei.2022.101730","volume":"54","author":"B Alizadeh","year":"2022","unstructured":"Alizadeh B, Li D, Hillin J, Meyer MA, Thompson CM, Zhang Z, Behzadan AH (2022) Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos. Adv Eng Inform 54:101730","journal-title":"Adv Eng Inform"},{"key":"1428_CR4","doi-asserted-by":"crossref","unstructured":"Aly GH, Marey M, El-Sayed SA, Tolba MF (2021) & biomedicine, p. i. YOLO based breast masses detection and classification in full-field digital mammograms. Computer methods programs in biomedicine, 200, 105823","DOI":"10.1016\/j.cmpb.2020.105823"},{"issue":"1","key":"1428_CR5","doi-asserted-by":"crossref","first-page":"171","DOI":"10.5194\/nhess-18-171-2018","volume":"18","author":"D-H Bae","year":"2018","unstructured":"Bae D-H, Lee M-H, Moon S-K (2018) Development of a precipitation\u2013area curve for warning criteria of short-duration flash flood. Nat Hazards Earth Syst Sci 18(1):171\u2013183","journal-title":"Nat Hazards Earth Syst Sci"},{"key":"1428_CR6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2023.11.008","volume":"237","author":"Y Bai","year":"2024","unstructured":"Bai Y, Yu J, Yang S, Ning J (2024) An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings. Biosyst Eng 237:1\u201312","journal-title":"Biosyst Eng"},{"key":"1428_CR7","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3741\/JKWRA.2008.41.2.185","volume":"41","author":"K Byung sik","year":"2008","unstructured":"Byung sik K, Kim H-S (2008) Estimation of the Flash Flood Severity using runoff hydrograph and Flash Flood Index. J Korea Water Resour Association 41:185\u2013196. https:\/\/doi.org\/10.3741\/JKWRA.2008.41.2.185","journal-title":"J Korea Water Resour Association"},{"key":"1428_CR9","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1007\/s00371-020-01831-7","volume":"37","author":"W Chen","year":"2021","unstructured":"Chen W, Huang H, Peng S, Zhou C, Zhang C (2021) YOLO-face: a real-time face detector. Visual Comput 37:805\u2013813","journal-title":"Visual Comput"},{"key":"1428_CR8","doi-asserted-by":"crossref","unstructured":"Chen P, Zhang Z, Huang Y, Dai L, Xu F, Hu H (2024a) Railway obstacle intrusion warning mechanism integrating YOLO-Based detection and Risk Assessment. J Industrial Inform Integr, 100571","DOI":"10.1016\/j.jii.2024.100571"},{"key":"1428_CR10","doi-asserted-by":"crossref","first-page":"108533","DOI":"10.1016\/j.compag.2023.108533","volume":"216","author":"W Chen","year":"2024","unstructured":"Chen W, Liu M, Zhao C, Li X, Wang Y (2024b) MTD-YOLO: multi-task deep convolutional neural network for cherry tomato fruit bunch maturity detection. Computers Electron Agric 216:108533","journal-title":"Computers Electron Agric"},{"key":"1428_CR11","doi-asserted-by":"crossref","first-page":"107742","DOI":"10.1016\/j.engappai.2023.107742","volume":"130","author":"X Chen","year":"2024","unstructured":"Chen X, Wang M, Ling J, Wu H, Wu B, Li C (2024c) Ship imaging trajectory extraction via an aggregated you only look once (YOLO) model. Eng Appl Artif Intell 130:107742","journal-title":"Eng Appl Artif Intell"},{"key":"1428_CR12","doi-asserted-by":"crossref","unstructured":"\u0106orovi\u0107 A, Ili\u0107 V, \u00d0uri\u0107 S, Marijan M, Pavkovi\u0107 B (2018) The real-time detection of traffic participants using YOLO algorithm Paper presented at the 2018 26th Telecommunications Forum (TELFOR)","DOI":"10.1109\/TELFOR.2018.8611986"},{"key":"1428_CR13","doi-asserted-by":"crossref","first-page":"127747","DOI":"10.1016\/j.jhydrol.2022.127747","volume":"609","author":"R Costache","year":"2022","unstructured":"Costache R, Tin TT, Arabameri A, Cr\u0103ciun A, Ajin R, Costache I, Avand M (2022) Flash-flood hazard using deep learning based on H2O R package and fuzzy-multicriteria decision-making analysis. J Hydrol 609:127747","journal-title":"J Hydrol"},{"key":"1428_CR14","doi-asserted-by":"crossref","first-page":"97228","DOI":"10.1109\/ACCESS.2021.3094201","volume":"9","author":"C Dewi","year":"2021","unstructured":"Dewi C, Chen R-C, Liu Y-T, Jiang X, Hartomo KD (2021) Yolo V4 for advanced traffic sign recognition with synthetic training data generated by various GAN. IEEE Access 9:97228\u201397242","journal-title":"IEEE Access"},{"key":"1428_CR15","unstructured":"Duong NT, Kim JB, Bae D-H (2021) Simulation and validation of flash flood in the head-water catchments of the Geum river basin Paper presented at the Proceedings of the Korea Water Resources Association Conference"},{"key":"1428_CR16","doi-asserted-by":"crossref","unstructured":"Felzenszwalb PF, Girshick RB, McAllester D (2010) Cascade object detection with deformable part models Paper presented at the 2010 IEEE Computer society conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2010.5539906"},{"key":"1428_CR17","doi-asserted-by":"crossref","unstructured":"Feng S, Qian H, Wang H, Wang W (2024) Real-time object detection method based on YOLOv5 and efficient mobile network. Journal of Real-Time Image Processing, 21(2), p.56","DOI":"10.1007\/s11554-024-01433-9"},{"issue":"19","key":"1428_CR18","doi-asserted-by":"crossref","first-page":"13895","DOI":"10.1007\/s00521-021-06029-z","volume":"35","author":"R Gai","year":"2023","unstructured":"Gai R, Chen N, Yuan H (2023) A detection algorithm for cherry fruits based on the improved YOLO-v4 model. Neural Comput Appl 35(19):13895\u201313906","journal-title":"Neural Comput Appl"},{"key":"1428_CR19","unstructured":"Girshick (2015) Fast R-CNN. arXiv 2015. arXiv preprint arXiv: 1504. 08083"},{"key":"1428_CR20","doi-asserted-by":"crossref","unstructured":"Hasnaoui Y, Tachi SE, Bouguerra H, Benmamar S, Gilja G, Szczepanek R, Yaseen ZM (2024) Enhanced machine learning models development for flash flood mapping using geospatial data. Euro-Mediterranean J Environ Integr, 1\u201321","DOI":"10.1007\/s41207-024-00553-9"},{"key":"1428_CR21","doi-asserted-by":"crossref","unstructured":"Hou C, Li Z, Shen X, Li G (2024) Real-time defect detection method based on YOLO\u2010GSS at the edge end of a transmission line. IET Image Processing","DOI":"10.1049\/ipr2.13028"},{"key":"1428_CR22","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.ins.2020.02.067","volume":"522","author":"Z Huang","year":"2020","unstructured":"Huang Z, Wang J, Fu X, Yu T, Guo Y, Wang R (2020) DC-SPP-YOLO: dense connection and spatial pyramid pooling based YOLO for object detection. Inf Sci 522:241\u2013258","journal-title":"Inf Sci"},{"key":"1428_CR23","doi-asserted-by":"crossref","unstructured":"Hui Y, Wang J, Li B, Measurement (2024) WSA-YOLO: weak-supervised and adaptive object detection in the low-light environment for YOLOV7. IEEE Trans Instrum","DOI":"10.1109\/TIM.2024.3350120"},{"issue":"9","key":"1428_CR24","doi-asserted-by":"crossref","first-page":"094020","DOI":"10.1088\/1748-9326\/ab3b8f","volume":"14","author":"E-S Im","year":"2019","unstructured":"Im E-S, Thanh N-X, Kim Y-H, Ahn J-B (2019) 2018 summer extreme temperatures in South Korea and their intensification under 3\u00b0 C global warming. Environ Res Lett 14(9):094020","journal-title":"Environ Res Lett"},{"issue":"2","key":"1428_CR25","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.icte.2019.11.001","volume":"6","author":"Y Jamtsho","year":"2020","unstructured":"Jamtsho Y, Riyamongkol P, Waranusast R (2020) Real-time Bhutanese license plate localization using YOLO. ICT Express 6(2):121\u2013124","journal-title":"ICT Express"},{"key":"1428_CR26","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","volume":"199","author":"P Jiang","year":"2022","unstructured":"Jiang P, Ergu D, Liu F, Cai Y, Ma B (2022) A review of Yolo algorithm developments. Procedia Comput Sci 199:1066\u20131073","journal-title":"Procedia Comput Sci"},{"issue":"18","key":"1428_CR27","doi-asserted-by":"crossref","first-page":"3775","DOI":"10.3390\/app9183775","volume":"9","author":"M Ju","year":"2019","unstructured":"Ju M, Luo H, Wang Z, Hui B, Chang Z (2019) The application of improved YOLO V3 in multi-scale target detection. Appl Sci 9(18):3775","journal-title":"Appl Sci"},{"key":"1428_CR28","doi-asserted-by":"crossref","first-page":"121209","DOI":"10.1016\/j.eswa.2023.121209","volume":"237","author":"L Kang","year":"2024","unstructured":"Kang L, Lu Z, Meng L, Gao Z (2024) YOLO-FA: Type-1 fuzzy attention based YOLO detector for vehicle detection. Expert Syst Appl 237:121209","journal-title":"Expert Syst Appl"},{"key":"1428_CR29","doi-asserted-by":"crossref","unstructured":"Khan TA, Alam M, Shahid Z, Ahmed SF, Mazliham M (2018) Artificial Intelligence based Multi-modal sensing for flash flood investigation Paper presented at the 2018 IEEE 5th International Conference on Engineering Technologies and Applied Sciences (ICETAS)","DOI":"10.1109\/ICETAS.2018.8629147"},{"key":"1428_CR30","doi-asserted-by":"crossref","first-page":"19364","DOI":"10.1109\/ACCESS.2020.2967496","volume":"8","author":"TA Khan","year":"2020","unstructured":"Khan TA, Alam MM, Shahid Z, Su\u2019Ud MM (2020) Investigation of flash floods on early basis: a factual comprehensive review. IEEE Access 8:19364\u201319380","journal-title":"IEEE Access"},{"key":"1428_CR31","unstructured":"Khanuja GS (2019) A study of Real Time search in Flood scenes from Uav videos using deep learning techniques. Purdue University"},{"key":"1428_CR32","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.scitotenv.2018.01.266","volume":"627","author":"K Khosravi","year":"2018","unstructured":"Khosravi K, Pham BT, Chapi K, Shirzadi A, Shahabi H, Revhaug I, Bui DT (2018) A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Sci Total Environ 627:744\u2013755","journal-title":"Sci Total Environ"},{"issue":"7","key":"1428_CR35","doi-asserted-by":"crossref","first-page":"2907","DOI":"10.3390\/ijerph8072907","volume":"8","author":"ES Kim","year":"2011","unstructured":"Kim ES, Choi HI (2011) Assessment of vulnerability to extreme flash floods in design storms. Int J Environ Res Public Health 8(7):2907\u20132922","journal-title":"Int J Environ Res Public Health"},{"issue":"4","key":"1428_CR36","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.3390\/ijerph9041507","volume":"9","author":"ES Kim","year":"2012","unstructured":"Kim ES, Choi HI (2012) Estimation of the relative severity of floods in small ungauged catchments for preliminary observations on Flash Flood preparedness: a Case Study in Korea. Int J Environ Res Public Health 9(4):1507\u20131522","journal-title":"Int J Environ Res Public Health"},{"issue":"4","key":"1428_CR33","first-page":"203","volume":"4","author":"B-S Kim","year":"2003","unstructured":"Kim B-S, Kim H-S (2003) FLASH FLOOD GUIDANCE OF A TYPOON RUSA. Water Eng Res 4(4):203\u2013214","journal-title":"Water Eng Res"},{"issue":"4","key":"1428_CR34","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1111\/jfr3.12057","volume":"7","author":"B-S Kim","year":"2014","unstructured":"Kim B-S, Kim H-S (2014) Evaluation of flash flood severity in Korea using the modified flash flood index (MFFI). J Flood Risk Manag 7(4):344\u2013356. https:\/\/doi.org\/10.1111\/jfr3.12057","journal-title":"J Flood Risk Manag"},{"issue":"4","key":"1428_CR37","first-page":"245","volume":"52","author":"H-Y Kim","year":"2019","unstructured":"Kim H-Y, Kim J-B, Bae D-H (2019) Estimation and evaluation on the return period of flash flood for small mountainous watersheds in the Han River basin. J Korea Water Resour Association 52(4):245\u2013253","journal-title":"J Korea Water Resour Association"},{"key":"1428_CR38","unstructured":"Kumbam PR, Vejre KM (2024) FloodLense: A Framework for ChatGPT-based Real-time Flood Detection. arXiv preprint arXiv:2401.15501"},{"key":"1428_CR41","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1016\/j.landusepol.2017.11.019","volume":"70","author":"Y Lee","year":"2018","unstructured":"Lee Y, Brody SD (2018) Examining the impact of land use on flood losses in Seoul, Korea. Land use Policy 70:500\u2013509","journal-title":"Land use Policy"},{"issue":"1","key":"1428_CR40","first-page":"85","volume":"19","author":"Y-H Lee","year":"2020","unstructured":"Lee Y-H, Kim Y (2020) Comparison of CNN and YOLO for object detection. J Semicond Disp Technol 19(1):85\u201392","journal-title":"J Semicond Disp Technol"},{"issue":"3","key":"1428_CR39","doi-asserted-by":"crossref","first-page":"151","DOI":"10.9798\/KOSHAM.2011.11.3.151","volume":"11","author":"J-H Lee","year":"2011","unstructured":"Lee J-H, Jun H-D, Park M-J, Jung J-H (2011) Flash Flood Risk Assessment using PROMETHEE and Entropy Method. J Korean Soc Hazard Mitig 11(3):151\u2013156","journal-title":"J Korean Soc Hazard Mitig"},{"issue":"3","key":"1428_CR42","first-page":"3139","volume":"45","author":"X Li","year":"2022","unstructured":"Li X, Lv C, Wang W, Li G, Yang L, Yang J (2022) Generalized focal loss: towards efficient representation learning for dense object detection. IEEE Trans Pattern Anal Mach Intell 45(3):3139\u20133153","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1428_CR44","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector Paper presented at the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14"},{"key":"1428_CR43","doi-asserted-by":"crossref","unstructured":"Liu C, Tao Y, Liang J, Li K, Chen Y (2018) Object detection based on YOLO network Paper presented at the 2018 IEEE 4th information technology and mechatronics engineering conference (ITOEC)","DOI":"10.1109\/ITOEC.2018.8740604"},{"key":"1428_CR45","doi-asserted-by":"crossref","unstructured":"Liu W, Ren G, Yu R, Guo S, Zhu J, Zhang L (2022) Image-adaptive YOLO for object detection in adverse weather conditions Paper presented at the Proceedings of the AAAI Conference on Artificial Intelligence","DOI":"10.1609\/aaai.v36i2.20072"},{"key":"1428_CR46","doi-asserted-by":"crossref","first-page":"110457","DOI":"10.1016\/j.ecolind.2023.110457","volume":"153","author":"M Nakhaei","year":"2023","unstructured":"Nakhaei M, Nakhaei P, Gheibi M, Chahkandi B, Wac\u0142awek S, Behzadian K, Campos LC (2023) Enhancing community resilience in arid regions: a smart framework for flash flood risk assessment. Ecol Ind 153:110457","journal-title":"Ecol Ind"},{"key":"1428_CR47","doi-asserted-by":"crossref","first-page":"1346104","DOI":"10.3389\/frwa.2024.1346104","volume":"6","author":"PC Oddo","year":"2024","unstructured":"Oddo PC, Bolten JD, Kumar SV, Cleary B (2024) Deep Convolutional LSTM for improved flash flood prediction. Front Water 6:1346104","journal-title":"Front Water"},{"key":"1428_CR48","unstructured":"Open-mmlab (2024) YOLOv8. https:\/\/github.com\/open-mmlab\/mmyolo\/tree\/main\/configs\/yolov8, acscessed 3rd February 2024"},{"key":"1428_CR49","doi-asserted-by":"crossref","first-page":"126655","DOI":"10.1016\/j.neucom.2023.126655","volume":"555","author":"G Oreski","year":"2023","unstructured":"Oreski G (2023) YOLO* C\u2014Adding context improves YOLO performance. Neurocomputing 555:126655","journal-title":"Neurocomputing"},{"issue":"5","key":"1428_CR50","first-page":"656","volume":"49","author":"SJ Park","year":"2014","unstructured":"Park SJ (2014) Generality and specificity of landforms of the Korean peninsula, and its sustainability. J Korean Geographical Soc 49(5):656\u2013674","journal-title":"J Korean Geographical Soc"},{"key":"1428_CR51","doi-asserted-by":"crossref","first-page":"75864","DOI":"10.1109\/ACCESS.2021.3081818","volume":"9","author":"D Pestana","year":"2021","unstructured":"Pestana D, Miranda PR, Lopes JD, Duarte RP, V\u00e9stias MP, Neto HC, De Sousa JT (2021) A full featured configurable accelerator for object detection with YOLO. IEEE Access 9:75864\u201375877","journal-title":"IEEE Access"},{"key":"1428_CR52","doi-asserted-by":"crossref","unstructured":"Potlapally A, Chowdary PSR, Shekhar SR, Mishra N, Madhuri CSVD, Prasad A (2019) Instance segmentation in remote sensing imagery using deep convolutional neural networks Paper presented at the 2019 International Conference on contemporary Computing and Informatics (IC3I)","DOI":"10.1109\/IC3I46837.2019.9055569"},{"key":"1428_CR53","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: Unified, real-time object detection Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2016.91"},{"key":"1428_CR54","unstructured":"Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. Adv Neural Inf Process Syst, 28"},{"key":"1428_CR55","doi-asserted-by":"crossref","unstructured":"Ropero RF, Flores MJ, Rum\u00ed R (2024) Flash floods in Mediterranean catchments: a meta-model decision support system based on bayesian networks. Environ Ecol Stat, 1\u201330","DOI":"10.1007\/s10651-023-00587-2"},{"issue":"2","key":"1428_CR56","first-page":"1","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C, Radin J (2019) Why are we using black box models in AI when we don\u2019t need to? A lesson from an explainable AI competition. Harv Data Sci Rev 1(2):1\u20139","journal-title":"Harv Data Sci Rev"},{"key":"1428_CR57","doi-asserted-by":"publisher","unstructured":"Sazara C, Old Dominion University (2021) Methods for detecting floodwater on roadways from Ground Level images. Comput Model Simul Eng Theses Dissertations Summer 2021. https:\/\/doi.org\/10.25777\/sqnd-rm87","DOI":"10.25777\/sqnd-rm87"},{"key":"1428_CR58","doi-asserted-by":"crossref","unstructured":"Sercl P, Pecha M, Novak P, Kyznarova H, Ledvinka O, Svoboda V, Danhelka J (2023) Flash Flood Indicator. Czech Hydrometeorological Institute, Na Sabatce 2050\/17, 143 06 Prague 12, ISBN 978-80-7653-050-8","DOI":"10.59984\/978-80-7653-050-8"},{"issue":"02","key":"1428_CR59","doi-asserted-by":"crossref","first-page":"p2450019","DOI":"10.1142\/S0219467824500190","volume":"24","author":"SA Shaikh","year":"2024","unstructured":"Shaikh SA, Chopade JJ, Sardey MP (2024) Real-time multi-object detection using enhanced Yolov5-7S on multi-GPU for high-resolution video. Int J Image Graphics 24(02):p2450019","journal-title":"Int J Image Graphics"},{"issue":"4","key":"1428_CR60","first-page":"71","volume":"8","author":"S-Y Shin","year":"2005","unstructured":"Shin S-Y, Yeo C-G, Baek C-H, Kim Y-J (2005) Mapping inundation areas by flash flood and developing rainfall standards for evacuation in urban settings. J Korean Association Geographic Inform Stud 8(4):71\u201380","journal-title":"J Korean Association Geographic Inform Stud"},{"key":"1428_CR61","doi-asserted-by":"crossref","unstructured":"Simon M, Milz S, Amende K, Gross H-M (2018) Complex-yolo: Real-time 3d object detection on point clouds. arXiv preprint arXiv:.06199, 1803.06199","DOI":"10.1109\/CVPRW.2019.00158"},{"key":"1428_CR62","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.jhydrol.2017.07.061","volume":"553","author":"B-J So","year":"2017","unstructured":"So B-J, Kim J-Y, Kwon H-H, Lima CH (2017) Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models. J Hydrol 553:321\u2013337","journal-title":"J Hydrol"},{"key":"1428_CR63","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Rabinovich A (2015) Going deeper with convolutions Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition","DOI":"10.1109\/CVPR.2015.7298594"},{"issue":"4","key":"1428_CR64","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","volume":"5","author":"J Terven","year":"2023","unstructured":"Terven J, C\u00f3rdova-Esparza D-M, Romero-Gonz\u00e1lez J-A (2023) A Comprehensive Review of YOLO Architectures in Computer Vision: from YOLOv1 to YOLOv8 and YOLO-NAS. Mach Learn Knowl Extr 5(4):1680\u20131716","journal-title":"Mach Learn Knowl Extr"},{"key":"1428_CR65","unstructured":"Thuan D (2021) Evolution of Yolo algorithm and Yolov5: The State-of-the-Art object detention algorithm. Bachelor\u2019s Thesis DIN16SP Information Technology, Oulu University of Applied Sciences, Spring 2021, 1\u201361"},{"key":"1428_CR66","doi-asserted-by":"crossref","unstructured":"Tinh LD, Thao DTP, Bui DT, Trong NG (2024) Integrating Harris Hawks optimization and TensorFlow deep learning for flash flood susceptibility mapping using geospatial data. Earth Sci Inf, 1\u201316","DOI":"10.1007\/s12145-024-01351-1"},{"key":"1428_CR67","unstructured":"Ultralytics (2024) Ultralytics YOLOv8 docs. https:\/\/docs.ultralytics.com\/, assessed on 2nd February 2024"},{"key":"1428_CR68","unstructured":"Vreeland N (1975) Area Handbook for South Korea, vol 550. US Government Printing Office"},{"key":"1428_CR69","doi-asserted-by":"crossref","unstructured":"Vu HN, Nguyen HM, Pham CD, Tran AD, Trong KN, Pham C, Nguyen VH (2021) Landslide Detection with Unmanned Aerial Vehicles Paper presented at the, 15\u201316 Oct. 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR)","DOI":"10.1109\/MAPR53640.2021.9585261"},{"key":"1428_CR70","first-page":"101619","volume":"51","author":"W-j Wang","year":"2024","unstructured":"Wang W-j, Kim D, Kim G, Kim KT, Kim S, Kim HS (2024a) Flood risk assessment of the naeseongcheon stream basin, Korea using the grid-based flood risk index. J Hydrology: Reg Stud 51:101619","journal-title":"J Hydrology: Reg Stud"},{"key":"1428_CR71","doi-asserted-by":"crossref","first-page":"122212","DOI":"10.1016\/j.eswa.2023.122212","volume":"238","author":"Z Wang","year":"2024","unstructured":"Wang Z, Hua Z, Wen Y, Zhang S, Xu X, Song H (2024b) E-YOLO: recognition of estrus cow based on improved YOLOv8n model. Expert Syst Appl 238:122212","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1428_CR72","doi-asserted-by":"crossref","first-page":"309","DOI":"10.7848\/ksgpc.2016.34.3.309","volume":"34","author":"S Won","year":"2016","unstructured":"Won S, Lee SW, Paik J, Yune CY, Kim G (2016) Analysis of erosion in debris flow experiment using terrestrial LiDAR. J Korean Soc Surveying Geodesy Photogrammetry Cartography 34(3):309\u2013317","journal-title":"J Korean Soc Surveying Geodesy Photogrammetry Cartography"},{"issue":"5","key":"1428_CR74","first-page":"100","volume":"7","author":"Y Yang","year":"2021","unstructured":"Yang Y, Li B (2021) Water Area object detection based on YOLO-Fusion Network. Int Core J Eng 7(5):100\u2013107","journal-title":"Int Core J Eng"},{"key":"1428_CR73","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.jhydrol.2014.11.028","volume":"520","author":"T-H Yang","year":"2015","unstructured":"Yang T-H, Yang S-C, Ho J-Y, Lin G-F, Hwang G-D, Lee C-S (2015) Flash flood warnings using the ensemble precipitation forecasting technique: a case study on forecasting floods in Taiwan caused by typhoons. J Hydrol 520:367\u2013378","journal-title":"J Hydrol"},{"key":"1428_CR75","doi-asserted-by":"crossref","unstructured":"Yang Y, Miao Z, Zhang H, Wang B, Wu L (2024) Lightweight Attention-Guided YOLO with Level Set Layer for Landslide Detection from Optical Satellite Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024 Jan 9","DOI":"10.1109\/JSTARS.2024.3351277"},{"key":"1428_CR76","doi-asserted-by":"crossref","first-page":"102756","DOI":"10.1016\/j.dsp.2020.102756","volume":"102","author":"Y Yin","year":"2020","unstructured":"Yin Y, Li H, Fu W (2020) Faster-YOLO: an accurate and faster object detection method. Digit Signal Proc 102:102756","journal-title":"Digit Signal Proc"},{"key":"1428_CR77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s42269-019-0259-7","volume":"44","author":"M Yousif","year":"2020","unstructured":"Yousif M, Hussien HM (2020) Flash floods mitigation and assessment of groundwater possibilities using remote sensing and GIS applications: Sharm El Sheikh, South Sinai, Egypt. Bull Natl Res Centre 44:1\u201325","journal-title":"Bull Natl Res Centre"},{"key":"1428_CR78","doi-asserted-by":"crossref","unstructured":"Zheng Z, Wang P, Liu W, Li J, Ye R, Ren D (2020) Distance-IoU loss: Faster and better learning for bounding box regression Paper presented at the Proceedings of the AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"1428_CR79","doi-asserted-by":"crossref","unstructured":"Zhiqiang W, Jun L (2017) A review of object detection based on convolutional neural network Paper presented at the 2017 36th Chinese control conference (CCC)","DOI":"10.23919\/ChiCC.2017.8029130"},{"key":"1428_CR80","doi-asserted-by":"crossref","first-page":"122256","DOI":"10.1016\/j.eswa.2023.122256","volume":"238","author":"Y Zhou","year":"2024","unstructured":"Zhou Y (2024) A YOLO-NL object detector for real-time detection. Expert Syst Appl 238:122256","journal-title":"Expert Syst Appl"},{"key":"1428_CR81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2022.3201146","volume":"60","author":"SZ Ziv","year":"2022","unstructured":"Ziv SZ, Reuveni Y (2022) Flash floods prediction using Precipitable Water Vapor Derived from GPS Tropospheric path delays over the Eastern Mediterranean. IEEE Trans Geoscience Remote Sens 60:1\u201317","journal-title":"IEEE Trans Geoscience Remote Sens"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01428-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01428-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01428-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T18:02:40Z","timestamp":1729101760000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01428-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,29]]},"references-count":81,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1428"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01428-x","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,29]]},"assertion":[{"value":"27 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}