Abstract
Due to climate change, the frequency and intensity of floods have dramatically increased worldwide. The innate social inequality has been exposed and even exacerbated by increasing flooding. It is imperative to assess flood risk in a comprehensive manner, accounting for both physical exposure and social vulnerability. Harris County in Texas, U.S., is selected as the study area as it has experienced a few devastating floods in recent history, with Hurricane Harvey (2017) being the most impactful. First, this study generates a flood susceptibility map (FSM) by applying a Random Forest (RF) model with 500 flood inventory points and 12 flood conditioning factors. Then, it generates a social vulnerability map (SoVM) by applying Principal Component Analysis (PCA) with ten social variables at the census tract level. Finally, it combines FSM with SoVM to produce a flood risk map (FRM) of Harris County. The findings of this study demonstrate that 9.06% of the area of Harris County has high flood susceptibility and 1.45% of the area has a very high social vulnerability. Combining both flood susceptibility and social vulnerability, this study reveals that 5.59% of the total area has a very high risk for flooding. This study further compares the FRM with the Federal Emergency Management Agency’s (FEMA) 100-year floodplain map and notes major differences. The comparison reveals that 76.7% of very high and 81.8% of high-risk areas in FRM are underestimated by the FEMA 100-year floodplain. This study produces a comprehensive FRM, highlighting areas where flooding can exacerbate social inequality and cause higher economic costs. FEMA’s 100-year floodplain map underestimates a significant portion of high-risk areas suggesting that current zoning and development policy may fail to consider flood risks adequately.
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References
Abedi R, Costache R, Shafizadeh-Moghadam H, Pham QB (2022) Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees. Geocarto Int 37(19):5479–5496. https://doi.org/10.1080/10106049.2021.1920636
Amare S, Langendoen E, Keesstra S, Ploeg MVD, Gelagay H, Lemma H, van der Zee SE (2021) Susceptibility to gully erosion: applying random forest (RF) and frequency ratio (FR) approaches to a small catchment in Ethiopia. Water 13(2):216. https://doi.org/10.3390/w13020216
Andaryani S, Nourani V, Haghighi AT, Keesstra S (2021) Integration of hard and soft supervised machine learning for flood susceptibility mapping. J Environ Manage 291:112731. https://doi.org/10.1016/j.jenvman.2021.112731
Ayalew L, Yamagishi H (2005) The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko mountains central Japan. Geomorphology 65(1–2):15–31
Bevacqua E, Maraun D, Vousdoukas MI, Voukouvalas E, Vrac M, Mentaschi L, Widmann M (2019) Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Sci Adv 5(9):eaaw5531. https://doi.org/10.1126/sciadv.aaw5531
Breiman L (2001) Random Forests. Mach Learn 45:5–32. https://doi.org/10.1023/A:1010933404324
Chakraborty J, Collins TW, Grineski SE (2019) Exploring the Environmental Justice Implications of Hurricane Harvey Flooding in Greater Houston, Texas. Am J Public Health 109:244–250. https://doi.org/10.2105/AJPH.2018.304846
Courtial A, Touya G, Zhang X (2022) Constraint-Based Evaluation of Map Images Generalized by Deep Learning. J Geovisualiz Spat Anal 6:13. https://doi.org/10.1007/s41651-022-00104-2
Crowell M, Coulton K, Johnson C, Westcott J, Bellomo D, Edelman S, Hirsch E (2010) An estimate of the US population living in 100-year coastal flood hazard areas. J Coastal Res 26(2):201–211. https://doi.org/10.2112/JCOASTRES-D-09-00076.1
Cutter SL, Boruff BJ, Shirley WL (2003) Social vulnerability to environmental hazards. Soc Sci Q 84(2):242–261
Das S (2020) Flood susceptibility mapping of the Western Ghat coastal belt using multi-source geospatial data and analytical hierarchy process (AHP). Remote Sens Appl : Soc Environ 20:100379. https://doi.org/10.1016/j.rsase.2020.100379
Dey H, Shao W, Moradkhani H et al (2024) Urban flood susceptibility mapping using frequency ratio and multiple decision tree-based machine learning models. Nat Hazards 1-29. https://doi.org/10.1007/s11069-024-06609-x
Dey H, Shao W, Pan S, Tian H (2023) The spatiotemporal patterns of community vulnerability in the U.S. Mobile Bay from 2000 - 2020. Appl Spat Anal Pol 1–22. https://doi.org/10.1007/s12061-023-09549-4
Dou P, Zeng C (2020) Hyperspectral image classification using feature relations map learning. Remote Sens 12(18):2956. https://doi.org/10.3390/rs12182956
Dou P, Shen H, Li Z, Guan X (2021) Time series remote sensing image classification framework using combination of deep learning and multiple classifiers system. Int J Appl Earth Obs Geoinf 103:102477. https://doi.org/10.1016/j.jag.2021.102477
Dou P, Huang C, Han W, Hou J, Zhang Y, Gu J (2024) Remote sensing image classification using an ensemble framework without multiple classifiers. ISPRS J Photogramm Remote Sens 208:190–209. https://doi.org/10.1016/j.isprsjprs.2023.12.012
Du P, Bai X, Tan K et al (2020) Advances of Four Machine Learning Methods for Spatial Data Handling: a Review. J Geovisualiz Spat Anal 4:13. https://doi.org/10.1007/s41651-020-00048-5
Eiser JR, Bostrom A, Burton I et al (2012) Risk interpretation and action: A conceptual framework for responses to natural hazards. Intl J Disaster Risk Reduct 1:5–16. https://doi.org/10.1016/j.ijdrr.2012.05.002
Elmahdy SI, Mohamed MM, Ali TA, Abdalla JED, Abouleish M (2022) Land subsidence and sinkholes susceptibility mapping and analysis using random forest and frequency ratio models in Al Ain. UAE Geocarto Intl 37(1):315–331. https://doi.org/10.1080/10106049.2020.1716398
Farhadi H, Najafzadeh M (2021) Flood risk mapping by remote sensing data and random forest technique. Water 13(21):3115. https://doi.org/10.3390/w13213115
Flores AB, Collins TW, Grineski SE, Amodeo M, Porter JR, Sampson CC, Wing O (2023) Federally overlooked flood risk inequities in Houston, Texas: Novel insights based on dasymetric mapping and state-of-the-Art flood modeling. Ann Am Assoc Geogr 113(1):240–260. https://doi.org/10.1080/24694452.2022.2085656
Frigerio I, De Amicis M (2016) Mapping social vulnerability to natural hazards in Italy: A suitable tool for risk mitigation strategies. Environ Sci Policy 63:187–196. https://doi.org/10.1016/j.envsci.2016.06.001
Géron A (2022) Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. " O’Reilly Media, Inc.".
Godfroy M, Jonkman B (2017) Fatalities due to hurricane Harvey (2017). 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:95690fdd-b13f-4bf9-a28d-c9b924696a96
Greene G, Paranjothy S, Palmer SR (2015) Resilience and vulnerability to the psychological harm from flooding: The role of social cohesion. Am J Public Health 105(9):1792–1795
Han J, Kim J, Park S, Son S, Ryu M (2020) Seismic vulnerability assessment and mapping of Gyeongju, South Korea using frequency ratio, decision tree, and random forest. Sustainability 12(18):7787. https://doi.org/10.3390/su12187787
Haque MM, Islam S, Sikder MB, Islam MS (2022) Community flood resilience assessment in Jamuna floodplain: A case study in Jamalpur District Bangladesh. Intl J Disaster Risk Reduct 72:102861. https://doi.org/10.1016/j.ijdrr.2022.102861
Haque MM, Islam S, Sikder MB, Islam MS, Tabassum A (2023) Assessment of flood vulnerability in Jamuna floodplain: a case study in Jamalpur district. Bangladesh Nat Hazards 116(1):341–363. https://doi.org/10.1007/s11069-022-05677-1
Hasan MA, Mia MB, Khan MR, Alam MJ, Chowdury T, Al Amin M, Ahmed KMU (2023) Temporal changes in land cover, land surface temperature, soil moisture, and evapotranspiration using remote sensing techniques—a case study of Kutupalong Rohingya Refugee Camp in Bangladesh. J Geovisualiz Spat Anal 7(1):11. https://doi.org/10.1007/s41651-023-00140-6
Hengl T (2018) Soil texture classes (USDA system) for 6 soil depths (0, 10, 30, 60, 100 and 200 cm) at 250 m (Version v02). Zenodo. https://doi.org/10.5281/zenodo.1475451
Highfield WE, Norman SA, Brody SD (2013) Examining the 100-year floodplain as a metric of risk, loss, and household adjustment. Risk Anal : an Intl J 33(2):186–191. https://doi.org/10.1111/j.1539-6924.2012.01840.x
Huang X, Wang C (2020) Estimates of exposure to the 100-year floods in the conterminous United States using national building footprints. Intl J Disaster Risk Reduct 50:101731. https://doi.org/10.1016/j.ijdrr.2020.101731
Iban MC, Sekertekin A (2022) Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces. Turkey Ecol Inform 69:101647. https://doi.org/10.1016/j.ecoinf.2022.101647
Islam ARMT, Talukdar S, Mahato S et al (2021) Flood susceptibility modelling using advanced ensemble machine learning models. Geosci Front 12(3):101075. https://doi.org/10.1016/j.gsf.2020.09.006
Joyce KE, Belliss SE, Samsonov SV, McNeill SJ, Glassey PJ (2009) A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog Phys Geogr 33(2):183–207. https://doi.org/10.1177/0309133309339563
Kadri CB, Nasrallah Y (2023) GIS-based AHP technique for assessment of desertification in western highlands of Algeria. J Geovisualiz Spat Anal 7(2):18. https://doi.org/10.1007/s41651-023-00147-z
Khajehei S, Ahmadalipour A, Shao W, Moradkhani H (2020) A place-based assessment of flash flood hazard and vulnerability in the contiguous United States. Sci Rep 10:448. https://doi.org/10.1038/s41598-019-57349-z
Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River Basin. Malaysia Environ Earth Sci 67(1):251–264. https://doi.org/10.1007/s12665-011-1504-z
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 Haz Risk 8(2):1185–1203. https://doi.org/10.1080/19475705.2017.1308971
Merghadi A, Yunus AP, Dou J et al (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
Merz B, Thieken AH, Gocht M (2007) Flood risk mapping at the local scale: concepts and challenges. Flood risk management in Europe: innovation in policy and practice 231–251. https://doi.org/10.1007/978-1-4020-4200-3_13
Mosavi A, Ozturk P, Chau KW (2018) Flood prediction using machine learning models: Literature review. Water 10(11):1536. https://doi.org/10.3390/w10111536
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
Nachappa TG, Piralilou ST, Gholamnia K, Ghorbanzadeh O, Rahmati O, Blaschke T (2020) Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory. J Hydrol 590:125275. https://doi.org/10.1016/j.jhydrol.2020.125275
Park K, Lee MH (2019) The development and application of the urban flood risk assessment model for reflecting upon urban planning elements. Water 11(5):920. https://doi.org/10.3390/w11050920
Pekel JF, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422. https://doi.org/10.1038/nature20584
Pralle S (2019) Drawing lines: FEMA and the politics of mapping flood zones. Clim Change 152(2):227–237. https://doi.org/10.1007/s10584-018-2287-y
Pulcinella JA, Winguth AM, Allen DJ, DasaGangadhar N (2019) Analysis of flood vulnerability and transit availability with a changing climate in Harris County. Texas Transp Res Rec 2673(6):258–266. https://doi.org/10.1177/0361198119839346
Qin H, Wang J, Mao X et al (2024) An Improved Faster R-CNN Method for Landslide Detection in Remote Sensing Images. Journal of Geovisualization and Spatial Analysis 8:2. https://doi.org/10.1007/s41651-023-00163-z
Rahman M, Ningsheng C, Islam MM, Dewan A, Iqbal J, Washakh RMA, Shufeng T (2019) Flood susceptibility assessment in Bangladesh using machine learning and multi-criteria decision analysis. Earth Syst Environ 3(3):585–601. https://doi.org/10.1007/s41748-019-00123-y
Rahman M, Ningsheng C, Mahmud GI et al (2021) Flooding and its relationship with land cover change, population growth, and road density. Geosci Front 12(6):101224. https://doi.org/10.1016/j.gsf.2021.101224
Rahmati O, Pourghasemi HR, Zeinivand H (2016) Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province. Iran Geocarto Intl 31(1):42–70. https://doi.org/10.1080/10106049.2015.1041559
Rahmati O, Darabi H, Panahi M et al (2020) Development of novel hybridized models for urban flood susceptibility mapping. Sci Rep 10(1):12937. https://doi.org/10.1038/s41598-020-69703-7
Reckien D (2018) What is in an index? Construction method, data metric, and weighting scheme determine the outcome of composite social vulnerability indices in New York City. Reg Environ Change 18:1439–1451. https://doi.org/10.1007/s10113-017-1273-7
Rufat S, Tate E, Burton CG, Maroof AS (2015) Social vulnerability to floods: Review of case studies and implications for measurement. Intl J Disaster Risk Red 14:470–486. https://doi.org/10.1016/j.ijdrr.2015.09.013
Samanta S, Pal DK, Palsamanta B (2018) Flood susceptibility analysis through remote sensing, GIS and frequency ratio model. Appl Water Sci 8(2):66. https://doi.org/10.1007/s13201-018-0710-1
Sarkar D, Mondal P (2020) Flood vulnerability mapping using frequency ratio (FR) model: a case study on Kulik river basin. Indo-Bangladesh Barind Region Appl Water Sci 10(1):1–13. https://doi.org/10.1007/s13201-019-1102-x
Seydi ST, Kanani-Sadat Y, Hasanlou M, Sahraei R, Chanussot J, Amani M (2022) Comparison of Machine Learning Algorithms for Flood Susceptibility Mapping. Remote Sensing 15(1):192. https://doi.org/10.3390/rs15010192
Shah AA, Ye J, Abid M, Khan J, Amir SM (2018) Flood hazards: household vulnerability and resilience in disaster-prone districts of Khyber Pakhtunkhwa province, Pakistan. Nat Hazards 93:147–165. https://doi.org/10.1007/s11069-018-3293-0
Shahabi H, Shirzadi A, Ghaderi K et al (2020) Flood detection and susceptibility mapping using sentinel-1 remote sensing data and a machine learning approach: Hybrid intelligence of bagging ensemble based on k-nearest neighbor classifier. Remote Sens 12(2):266. https://doi.org/10.3390/rs12020266
Shao W, Xian S, Lin N, Kunreuther H, Jackson N, Goidel K (2017) Understanding the effects of past flood events, perceived and estimated flood risks on individuals’ voluntary flood insurance purchase behaviors. Water Res 108:391–400. https://doi.org/10.1016/j.watres.2016.11.021
Shao W, Feng K, Lin N (2019) Predicting support for flood mitigation based on flood insurance purchase behavior. Environ Res Lett 14(5):054014. https://doi.org/10.1088/1748-9326/ab195a
Shao W, Jackson NP, Ha H, Winemiller T (2020) Assessing community vulnerability to floods and hurricanes along the Gulf Coast of the United States. Disasters 44(3):518–547. https://doi.org/10.1111/disa.12383
de Sherbinin A, Bardy G (2015) Social vulnerability to floods in two coastal megacities: New York City and Mumbai. Vienna yearbook of population research 131–165. https://www.jstor.org/stable/24770028
Sotiropoulou KF, Vavatsikos AP (2023) A decision-making framework for spatial multicriteria suitability analysis using PROMETHEE II and k nearest neighbor machine learning models. J Geovisualiz Spat Anal 7(2):20. https://doi.org/10.1007/s41651-023-00151-3
Tabassum A, Basak R, Shao W, Haque MM, Chowdhury TA, Dey H (2023) Exploring the Relationship Between Land Use Land Cover and Land Surface Temperature: a Case Study in Bangladesh and the Policy Implications for the Global South. J Geovisualiz Spat Anal 7(2):25. https://doi.org/10.1007/s41651-023-00155-z
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 77:1–24. https://doi.org/10.1007/s12665-018-7667-0
Tehrany MS, Pradhan B, Jebur MN (2015) Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stoch Env Res Risk Assess 29(4):1149–1165. https://doi.org/10.1007/s00477-015-1021-9
Thanh NN, Chotpantarat S, Trung NH, Ngu NH (2022) Mapping groundwater potential zones in Kanchanaburi Province, Thailand by integrating of analytic hierarchy process, frequency ratio, and random forest. Ecol Ind 145:109591. https://doi.org/10.1016/j.ecolind.2022.109591
VanDyke MS, King AJ (2018) Using the CAUSE model to understand public communication about water risks: Perspectives from Texas groundwater district officials on drought and availability. Risk Anal 38(7):1378–1389. https://doi.org/10.1111/risa.12950
VanDyke MS, Armstrong CL, Bareford K (2021) How risk decision-makers interpret and use flood forecast information: Assessing the Mississippi River outlook email product. J Risk Res 24(10):1239–1250. https://doi.org/10.1080/13669877.2020.1819390
Vojtek M, Vojteková J (2016) Flood hazard and flood risk assessment at the local spatial scale: a case study. Geomat Nat Haz Risk 7(6):1973–1992. https://doi.org/10.1080/19475705.2016.1166874
Wong PP, Losada IJ, Gattuso JP et al (2014) Coastal systems and low-lying areas. Clim Change 2104:361–409
Youssef AM, Pradhan B, Dikshit A, 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 Intl 1–28. https://doi.org/10.1080/10106049.2022.2046866
Acknowledgements
Funding for this project was provided by the National Oceanic and Atmospheric Administration (NOAA), awarded to the Cooperative Institute for Research on Hydrology (CIROH) through the NOAA Cooperative Agreement with The University of Alabama, NA22NWS4320003. The authors declare they have no financial interests. On behalf of all authors, the corresponding author states that there is no conflict of interest. The research was conducted in compliance with ethical standards. All data come from publicly available sources. Thus, no ethical approval or informed consent was needed during the data compilation process.
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Dey, H., Shao, W., Haque, M. et al. Enhancing Flood Risk Analysis in Harris County: Integrating Flood Susceptibility and Social Vulnerability Mapping. J geovis spat anal 8, 19 (2024). https://doi.org/10.1007/s41651-024-00181-5
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DOI: https://doi.org/10.1007/s41651-024-00181-5