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This study demonstrates that a proposed machine learning model,\n                    <jats:italic>MaxFloodCast<\/jats:italic>\n                    , trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$R^2$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:msup>\n                            <mml:mi>R<\/mml:mi>\n                            <mml:mn>2<\/mml:mn>\n                          <\/mml:msup>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical\u00a0Storm Imelda,\n                    <jats:italic>MaxFloodCast<\/jats:italic>\n                    shows the potential in supporting near-time floodplain management and emergency operations. The model\u2019s interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The\n                    <jats:italic>MaxFloodCast<\/jats:italic>\n                    model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.\n                  <\/jats:p>","DOI":"10.1038\/s41598-024-65570-8","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T04:11:07Z","timestamp":1719461467000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Predicting peak inundation depths with a physics informed machine learning model"],"prefix":"10.1038","volume":"14","author":[{"given":"Cheng-Chun","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lipai","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Antolini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthew","family":"Garcia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Juan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel D.","family":"Brody","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Mostafavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,27]]},"reference":[{"issue":"6","key":"65570_CR1","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1061\/(ASCE)1084-0699(2003)8:6(308)","volume":"8","author":"Philip B Bedient","year":"2003","unstructured":"Bedient, Philip B., Holder, Anthony, Benavides, Jude A. & Vieux, Baxter E. 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