Abstract
Floods are the most devastating global hazard which affect the environment and economy of several regions in the world. Flood management requires the identification of areas susceptible to flooding and measuring the impact of flood conditioning parameters. This study examines the application of bivariate relative frequency ratio (RFR) and multivariate logistic regression (L.R.) models to identify flood-susceptible regions in three districts of northern Kerala, India. A comprehensive flood susceptibility study utilizing high-resolution terrain information and past flood inventory is conducted in the study area. The current study generated the flood inventory of August 2018 during the Kerala floods using Sentinel-imagery of 10 m resolution. Thirteen flood conditioning parameters related to the terrain, land usage, climate and vegetation are used as independent variables in the statistical modeling. The terrain-related parameters such as elevation, slope, curvature, flow accumulation, topographic wetness index (TWI), and stream power index (SPI) are derived from CartoDEM of 10 m resolution. The other independent variables used are rainfall, normalized difference vegetation index (NDVI), waterbody distance, drainage density, soil type, and geology. In GIS, the dependent and independent variables are spatially combined, and SPSS and R are used for statistical modeling and validation. The final flood susceptibility map is divided into the risk categories of very low, low, medium, high, and very high. Both RFR and L.R. model results were found reliable, and the low-lying coastal wetlands are highly susceptible to flooding in the study area. The area under curve values show that the L.R. model performs better with 92.7% accuracy than the RFR model with 85.6% accuracy. The generated flood susceptibility map can be a valuable tool for sustainable planning and development in the target region.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Not applicable.
References
Acharya TD, Lee DH (2019) Landslide susceptibility mapping using relative frequency and predictor rate along Araniko highway. KSCE J Civ Eng 23(2):763–776. https://doi.org/10.1007/s12205-018-0156-x
Ahmadlou M et al (2019) Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). Geocarto Int 34(11):1252–1272. https://doi.org/10.1080/10106049.2018.1474276
Ajjur SB, Mogheir YK (2020) Flood hazard mapping using a multi-criteria decision analysis and GIS (case study Gaza Governorate, Palestine). Arab J Geosci 13(2):44. https://doi.org/10.1007/s12517-019-5024-6
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-Abadi AM, Shahid S, Al-Ali AK (2016) A GIS-based integration of catastrophe theory and analytical hierarchy process for mapping flood susceptibility: a case study of Teeb area, Southern Iraq. Environ Earth Sci 75(8):687. https://doi.org/10.1007/s12665-016-5523-7
Ali SA et al (2019) Application of GIS-based analytic hierarchy process and frequency ratio model to flood vulnerable mapping and risk area estimation at Sundarban region, India. Model Earth Syst Environ 5(3):1083–1102. https://doi.org/10.1007/s40808-019-00593-z
Al-Juaidi AEM, Nassar AM, Al-Juaidi OEM (2018) Evaluation of flood susceptibility mapping using logistic regression and GIS conditioning factors. Arab J Geosci 11(24):765. https://doi.org/10.1007/s12517-018-4095-0
Althuwaynee OF et al (2014) A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. CATENA 114:21–36. https://doi.org/10.1016/j.catena.2013.10.011
Anandalekshmi A et al (2019) Modeling the concurrent impact of extreme rainfall and reservoir storage on Kerala floods 2018: a Copula approach. Model Earth Syst Environ 5(4):1283–1296. https://doi.org/10.1007/s40808-019-00635-6
Arabameri A et al (2019) A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran. Sci Total Environ 660:443–458. https://doi.org/10.1016/j.scitotenv.2019.01.021
Binoy BV, Naseer MA, Anil Kumar PP (2022) Factors affecting land value in an Indian city. J Prop Res 39(3):268–292. https://doi.org/10.1080/09599916.2021.2014937
Bouamrane A et al (2020) A comparison of the analytical hierarchy process and the fuzzy logic approach for flood susceptibility mapping in a semi-arid ungauged basin (Biskra basin: Algeria). Int J River Basin Manag. https://doi.org/10.1080/15715124.2020.1830786
Cao C et al (2016) Flash flood hazard susceptibility mapping using frequency ratio and statistical index methods in coalmine subsidence areas. Sustainability 8(9):948. https://doi.org/10.3390/su8090948
Chakrabortty R et al (2021) Flash-flood hazard susceptibility mapping in Kangsabati River Basin, India. Geocarto Int. https://doi.org/10.1080/10106049.2021.1953618
Chithra K, Binoy BV, Bimal P (2022) Spatial mapping of the flood-affected regions of Northern Kerala: a case study of 2018 Kerala floods. J Indian Soc Remote Sens 50(4):677–688. https://doi.org/10.1007/s12524-021-01485-5
Chowdhuri I, Pal SC, Chakrabortty R (2020) Flood susceptibility mapping by ensemble evidential belief function and binomial logistic regression model on river basin of eastern India. Adv Space Res 65(5):1466–1489. https://doi.org/10.1016/j.asr.2019.12.003
Costache R (2019) Flash-flood Potential Index mapping using weights of evidence, decision trees models and their novel hybrid integration. Stoch Env Res Risk Assess 33(7):1375–1402. https://doi.org/10.1007/s00477-019-01689-9
Dano U et al (2019) Flood susceptibility mapping using GIS-based analytic network process: a case study of Perlis, Malaysia. Water 11(3):615. https://doi.org/10.3390/w11030615
Danumah JH et al (2016) Flood risk assessment and mapping in Abidjan district using multi-criteria analysis (AHP) model and geoinformation techniques, (cote d’ivoire). Geoenviron Disasters 3(1):10. https://doi.org/10.1186/s40677-016-0044-y
Darabi H et al (2021) Development of a novel hybrid multi-boosting neural network model for spatial prediction of urban flood. Geocarto Int. https://doi.org/10.1080/10106049.2021.1920629
Das S (2018) Geographic information system and AHP-based flood hazard zonation of Vaitarna basin, Maharashtra, India. Arab J Geosci 11(19):576. https://doi.org/10.1007/s12517-018-3933-4
Das S (2019) Geospatial mapping of flood susceptibility and hydro-geomorphic response to the floods in Ulhas basin, India. Remote Sens Appl: Soc Environ 14(September 2018):60–74. https://doi.org/10.1016/j.rsase.2019.02.006
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(April):100379. https://doi.org/10.1016/j.rsase.2020.100379
Domakinis C et al (2020) Flood susceptibility mapping in Erythropotamos river basin with the aid of Remote Sensing and GIS. AUC Geogr 55(2):149–164. https://doi.org/10.14712/23361980.2020.11
Eini M et al (2020) Hazard and vulnerability in urban flood risk mapping: machine learning techniques and considering the role of urban districts. Int J Disaster Risk Reduct 50(May):101687. https://doi.org/10.1016/j.ijdrr.2020.101687
Feizizadeh B, Gheshlaghi HA, Bui DT (2021) An integrated approach of GIS and hybrid intelligence techniques applied for flood risk modeling. J Environ Plan Manag 64(3):485–516. https://doi.org/10.1080/09640568.2020.1775561
Giovannettone J et al (2018) A statistical approach to mapping flood susceptibility in the Lower Connecticut River Valley Region. Water Resour Res 54(10):7603–7618. https://doi.org/10.1029/2018WR023018
Goumrasa A et al (2021) Flood hazard susceptibility assessment in Chiffa wadi watershed and along the first section of Algeria North-South highway using GIS and AHP method. Appl Geomat. https://doi.org/10.1007/s12518-021-00381-4
Gourav P et al (2020) Flood hazard zonation of Bhagirathi river basin using multi-criteria decision-analysis in Uttarakhand, India. Int J Emerg Technol 11(1):62–71
Gudiyangada Nachappa T, Meena SR (2020) A novel per pixel and object-based ensemble approach for flood susceptibility mapping. Geomat Nat Haz Risk 11(1):2147–2175. https://doi.org/10.1080/19475705.2020.1833990
Hammami S et al (2019) Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arab J Geosci 12(21):653. https://doi.org/10.1007/s12517-019-4754-9
Hong H et al (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.2017.10.114
Hossain MK, Meng Q (2020) A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood. Land Use Policy 99(May):104829. https://doi.org/10.1016/j.landusepol.2020.104829
Hunt KMR, Menon A (2020) The 2018 Kerala floods: a climate change perspective. Clim Dyn 54(3–4):2433–2446. https://doi.org/10.1007/s00382-020-05123-7
Hussain M et al (2021) GIS-based multi-criteria approach for flood vulnerability assessment and mapping in District Shangla: Khyber Pakhtunkhwa, Pakistan. Sustainability 13(6):3126. https://doi.org/10.3390/su13063126
Jacinth Jennifer J, Saravanan S, Abijith D (2020) Integration of SAR and multi-spectral imagery in flood inundation mapping—a case study on Kerala floods 2018. ISH J Hydraul Eng. https://doi.org/10.1080/09715010.2020.1791265
James G et al (2013) An introduction to statistical learning, book. Springer texts in statistics. Springer, New York. https://doi.org/10.1007/978-1-4614-7138-7
Jones S et al (2021a) Impact of anthropogenic activities on landslide occurrences in southwest India: an investigation using spatial models. J Earth Syst Sci 130(2):70. https://doi.org/10.1007/s12040-021-01566-6
Jones S et al (2021b) Landslide susceptibility investigation for Idukki district of Kerala using regression analysis and machine learning. Arab J Geosci 14(10):838. https://doi.org/10.1007/s12517-021-07156-6
Kanani-Sadat Y et al (2019) A new approach to flood susceptibility assessment in data-scarce and ungauged regions based on GIS-based hybrid multi criteria decision-making method. J Hydrol 572:17–31. https://doi.org/10.1016/j.jhydrol.2019.02.034
Khosravi K, Nohani E et al (2016a) A GIS-based flood susceptibility assessment and its mapping in Iran: a comparison between frequency ratio and weights-of-evidence bivariate statistical models with multi-criteria decision-making technique. Nat Hazards 83(2):947–987. https://doi.org/10.1007/s11069-016-2357-2
Khosravi K, Pourghasemi HR et al (2016b) Flash flood susceptibility analysis and its mapping using different bivariate models in Iran: a comparison between Shannon’s entropy, statistical index, and weighting factor models. Environ Monit Assess 188(12):656. https://doi.org/10.1007/s10661-016-5665-9
Khosravi 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.scitotenv.2018.01.266
Komolafe AA et al (2021) Enhanced flood hazard modelling using hydraulic, analytical hierarchical process and height above nearest drainage models in Ogunpa river basin, Ibadan, Southwestern Nigeria. Model Earth Syst Environ 7(2):967–981. https://doi.org/10.1007/s40808-020-01037-9
Krishnan S, Sajikumar N, Sumam KS (2016) DEM generation using Cartosat-I stereo data and its comparison with publically available DEM. Procedia Technol 24:295–302. https://doi.org/10.1016/j.protcy.2016.05.039
Lal P et al (2020) Evaluating the 2018 extreme flood hazard events in Kerala, India. Remote Sens Lett 11(5):436–445. https://doi.org/10.1080/2150704X.2020.1730468
Lee S, Pradhan B (2007) Landslide hazard mapping at Selangor, Malaysia using frequency ratio and logistic regression models. Landslides 4(1):33–41. https://doi.org/10.1007/s10346-006-0047-y
Lee S, Sambath T (2006) Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environ Geol 50(6):847–855. https://doi.org/10.1007/s00254-006-0256-7
Liuzzo L, Sammartano V, Freni G (2019) Comparison between different distributed methods for flood susceptibility mapping. Water Resour Manag 33(9):3155–3173. https://doi.org/10.1007/s11269-019-02293-w
Luu C et al (2021) Flood-prone area mapping using machine learning techniques: a case study of Quang Binh province, Vietnam. Nat Hazards 108(3):3229–3251. https://doi.org/10.1007/s11069-021-04821-7
Mahmoud SH, Gan TY (2018) Urbanization and climate change implications in flood risk management: developing an efficient decision support system for flood susceptibility mapping. Sci Total Environ 636:152–167. https://doi.org/10.1016/j.scitotenv.2018.04.282
Marconi M et al (2016) A rapid method for flood susceptibility mapping in two districts of Phatthalung Province (Thailand): present and projected conditions for 2050. Nat Hazards 81(1):329–346. https://doi.org/10.1007/s11069-015-2082-2
Mishra AK (2021) Observing a severe flooding over southern part of India in monsoon season of 2019. J Earth Syst Sci 130(1):2. https://doi.org/10.1007/s12040-020-01509-7
Mohamed SA (2019) Application of satellite image processing and GIS-spatial modeling for mapping urban areas prone to flash floods in Qena governorate, Egypt. J Afr Earth Sc 158(April):103507. https://doi.org/10.1016/j.jafrearsci.2019.05.015
Morea H, Samanta S (2020) Multi-criteria decision approach to identify flood vulnerability zones using geospatial technology in the Kemp-Welch Catchment, Central Province, Papua New Guinea. Appl Geomat 12(4):427–440. https://doi.org/10.1007/s12518-020-00315-6
Msabi MM, Makonyo M (2021) Flood susceptibility mapping using GIS and multi-criteria decision analysis: a case of Dodoma region, central Tanzania. Remote Sens Appl: Soc Environ 21:100445. https://doi.org/10.1016/j.rsase.2020.100445
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
Nair A, Ajith Joseph K, Nair KS (2014) Spatio-temporal analysis of rainfall trends over a maritime state (Kerala) of India during the last 100 years. Atmos Environ 88(May):123–132. https://doi.org/10.1016/j.atmosenv.2014.01.061
Nandi A et al (2016) Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environ Earth Sci 75(6):465. https://doi.org/10.1007/s12665-016-5323-0
Narimani R et al (2021) Application of a novel hybrid method for flood susceptibility mapping with satellite images: a case study of Seoul, Korea. Remote Sens 13(14):2786. https://doi.org/10.3390/rs13142786
Natarajan L et al (2021) Flood susceptibility analysis in Chennai corporation using frequency ratio model. J Indian Soc Remote Sens 49(7):1533–1543. https://doi.org/10.1007/s12524-021-01331-8
Nhu V-H et al (2020) A new hybrid firefly–PSO optimized random subspace tree intelligence for torrential rainfall-induced flash flood susceptible mapping. Remote Sens 12(17):2688. https://doi.org/10.3390/rs12172688
Periyasamy P, Yagoub MM, Sudalaimuthu M (2018) Flood vulnerable zones in the rural blocks of Thiruvallur district, South India. Geoenviron Disasters 5(1):21. https://doi.org/10.1186/s40677-018-0113-5
Popa MC et al (2019) Flood hazard mapping using the flood and flash-flood potential index in the Buzău River catchment, Romania. Water 11(10):2116. https://doi.org/10.3390/w11102116
Pourghasemi HR et al (2020) Is multi-hazard mapping effective in assessing natural hazards and integrated watershed management? Geosci Front 11(4):1203–1217. https://doi.org/10.1016/j.gsf.2019.10.008
Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2):1–18
Pradhan B, Youssef AM (2010) Manifestation of remote sensing data and GIS on landslide hazard analysis using spatial-based statistical models. Arab J Geosci 3:319–326
Prasad P et al (2021) Novel ensemble machine learning models in flood susceptibility mapping. Geocarto Int. https://doi.org/10.1080/10106049.2021.1892209
Rahman M et al (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 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 Int 31(1):42–70. https://doi.org/10.1080/10106049.2015.1041559
Ramesh V, Iqbal SS (2020) Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio and fuzzy gamma operator models in GIS: a case study of Greater Mumbai, Maharashtra, India. Geocarto Int. https://doi.org/10.1080/10106049.2020.1730448
Rasyid AR, Bhandary NP, Yatabe R (2016) Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia. Geoenviron Disasters 3(1):19. https://doi.org/10.1186/s40677-016-0053-x
Samanta RK et al (2018) Flood susceptibility mapping using geospatial frequency ratio technique: a case study of Subarnarekha River Basin, India. Model Earth Syst Environ 4(1):395–408. https://doi.org/10.1007/s40808-018-0427-z
Sarhadi A, Soltani S, Modarres R (2012) Probabilistic flood inundation mapping of ungauged rivers: linking GIS techniques and frequency analysis. J Hydrol 458–459:68–86. https://doi.org/10.1016/j.jhydrol.2012.06.039
Shafapour Tehrany M et al (2017) GIS-based spatial prediction of flood prone areas using standalone frequency ratio, logistic regression, weight of evidence and their ensemble techniques. Geomat Nat Haz Risk 8(2):1538–1561. https://doi.org/10.1080/19475705.2017.1362038
Siahkamari S et al (2018) Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models. Geocarto Int 33(9):927–941. https://doi.org/10.1080/10106049.2017.1316780
Souissi D et al (2020) GIS-based MCDM–AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int 35(9):991–1017. https://doi.org/10.1080/10106049.2019.1566405
Swain KC, Singha C, Nayak L (2020) Flood susceptibility mapping through the GIS-AHP technique using the cloud. ISPRS Int J Geo Inf 9(12):720. https://doi.org/10.3390/ijgi9120720
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 et al (2014) Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ Earth Sci 72(10):4001–4015. https://doi.org/10.1007/s12665-014-3289-3
Tehrany MS et al (2015) Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. CATENA 125:91–101. https://doi.org/10.1016/j.catena.2014.10.017
Tehrany MS, Jones S, Shabani F (2019) Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques. CATENA 175(December):174–192. https://doi.org/10.1016/j.catena.2018.12.011
Tella A, Balogun A-L (2020) Ensemble fuzzy MCDM for spatial assessment of flood susceptibility in Ibadan, Nigeria. Nat Hazards 104(3):2277–2306. https://doi.org/10.1007/s11069-020-04272-6
Tiwari V et al (2020) Flood inundation mapping-Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS ONE. Edited by G. J.-P. Schumann 15(8):e0237324. https://doi.org/10.1371/journal.pone.0237324
Ullah K, Zhang J (2020) GIS-based flood hazard mapping using relative frequency ratio method: a case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. PLoS ONE. Edited by M. L. Tan 15(3):e0229153. https://doi.org/10.1371/journal.pone.0229153
Vanama VSK et al (2021) Inundation mapping of Kerala flood event in 2018 using ALOS-2 and temporal Sentinel-1 SAR images. Curr Sci 120(5):915–925. https://doi.org/10.18520/cs/v120/i5/915-925
Vijith H, Madhu G (2008) Estimating potential landslide sites of an upland sub-watershed in Western Ghat’s of Kerala (India) through frequency ratio and GIS. Environ Geol 55(7):1397–1405. https://doi.org/10.1007/s00254-007-1090-2
Vishnu CL et al (2019) Satellite-based assessment of the August 2018 flood in parts of Kerala, India. Geomat Nat Hazard Risk 10(1):758–767. https://doi.org/10.1080/19475705.2018.1543212
Vishnu CL et al (2020) Catastrophic flood of August 2018, Kerala, India: study of partitioning role of lineaments in modulating flood level using remote sensing data. Remote Sens Appl: Soc Environ 20(October):100426. https://doi.org/10.1016/j.rsase.2020.100426
Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11(2):364. https://doi.org/10.3390/w11020364
Vojtek M et al (2021) Comparison of multi-criteria-analytical hierarchy process and machine learning-boosted tree models for regional flood susceptibility mapping: a case study from Slovakia. Geomat Nat Hazard Risk 12(1):1153–1180. https://doi.org/10.1080/19475705.2021.1912835
Wang Y et al (2018) A hybrid GIS multi-criteria decision-making method for flood susceptibility mapping at Shangyou, China. Remote Sens 11(1):62. https://doi.org/10.3390/rs11010062
World Population Prospects (2019) New York, United Nations
Yariyan P et al (2020) Flood susceptibility mapping using an improved analytic network process with statistical models. Geomat Nat Hazard Risk 11(1):2282–2314. https://doi.org/10.1080/19475705.2020.1836036
Youssef AM, Pradhan B, Sefry SA (2016) Flash flood susceptibility assessment in Jeddah city (Kingdom of Saudi Arabia) using bivariate and multivariate statistical models. Environ Earth Sci 75(1):12. https://doi.org/10.1007/s12665-015-4830-8
Acknowledgements
This research is undertaken as part of the DST-NRDMS project funded by the Department of Science and technology, GOI. The authors thank the funding agency for providing the necessary guidance and financial support.
Funding
This study was funded by the Natural Resources Data Management System.
Author information
Authors and Affiliations
Contributions
The project, the key conceptual concepts and the proof framework were created by KC. Nearly all of the technical details and numerical computations for this paper were worked out by BVB and PB. The article was written by BVB and was reviewed by all authors.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Conflict of interest
The authors declare that they have acknowledged all financial and technical support received for the work presented in this article.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chithra, K., Binoy, B.V. & Bimal, P. Modeling flood susceptibility on the onset of the Kerala floods of 2018. Environ Earth Sci 83, 123 (2024). https://doi.org/10.1007/s12665-023-11412-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12665-023-11412-1