Probabilistic ML-Physics Hybrid Framework for Real-Time Coastal Inundation Mapping under Global Change

Probabilistic ML-Physics Hybrid Framework for Real-Time Coastal Inundation Mapping under Global Change | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 4 February 2026 V1 Latest version Share on Probabilistic ML-Physics Hybrid Framework for Real-Time Coastal Inundation Mapping under Global Change Author : Paul Magoulick 0009-0002-9180-4778 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177023585.54545531/v1 96 views 76 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Operational coastal flood forecasting confronts a fundamental “scale gap’: physics-based numerical models provide process fidelity but require computational resources incompatible with real-time decision-making, while machine learning approaches offer speed but lack the spatial physics necessary for inundation mapping. I present a probabilistic ML-physics hybrid framework that bridges this gap by coupling machine learning-derived temporal boundary conditions with a diffusive wave connectivity solver for spatial flood propagation. Validated against 11 official NOAA tide gauge records from Hurricane Isabel (2003) across three Chesapeake Bay sites—Annapolis, Baltimore, and Norfolk-Virginia Beach—my framework achieves a root-mean-square error (RMSE) of 0.015 m, representing a 96\% improvement over the NOAA Storm Surge Total Water Level and Coastal Flooding (STOFS-2D) operational baseline (RMSE = 0.35 m). The physics-based hydraulic connectivity algorithm successfully resolves flood pathways across 1,210 km$^2$ of vulnerable land in Norfolk and 36.8 km$^2$ in Annapolis, eliminating the unrealistic “bathtub’ artifacts that plague simplified approaches. Benchmark comparisons against LISFLOOD-FP demonstrate equivalent flood extent accuracy (Jaccard index 0.85) with 10$\times$ faster runtime, enabling real-time operational deployment. A Monte Carlo uncertainty quantification engine ($n=30$ ensemble members) propagates boundary condition uncertainty through the spatial domain, enabling probabilistic risk assessment. Under climate change scenarios, I demonstrate pronounced nonlinear risk acceleration: a +1.0 m sea level rise amplifies the Norfolk flood risk zone by a factor of 4.8$\times$ (from 1,210 km$^2$ to 5,854 km$^2$). The Python-native implementation achieves 16-second runtime for 42 km$^2$ domains and 139-second runtime for 251 km$^2$ domains, enabling deployment for real-time emergency management and long-term resilience planning. Supplementary Material File (1060838_0_merged_1767898153.pdf) Download 43.85 MB Information & Authors Information Version history V1 Version 1 04 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords coastal flooding hybrid modeling machine learning storm surge uncertainty quantification Authors Affiliations Paul Magoulick 0009-0002-9180-4778 [email protected] United States Naval Academy School of Engineering Computing & Weapons View all articles by this author Metrics & Citations Metrics Article Usage 96 views 76 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Paul Magoulick. Probabilistic ML-Physics Hybrid Framework for Real-Time Coastal Inundation Mapping under Global Change. Authorea . 04 February 2026. DOI: https://doi.org/10.22541/au.177023585.54545531/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177023585.54545531/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe362929d7906f3',t:'MTc3OTE5NjY4MA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();