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This study details a comprehensive probabilistic landslide hazard assessment at a regional scale in central Vietnam, analyzing spatial, temporal, and magnitude probabilities of landslides and proposes a methodology for quantitative landslide risk assessment. This study utilized a total of 21,234 landslide occurrences from three inventories created from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020) for the landslide hazard and risk assessment. The landslide spatial probability was delineated for three landslide size classes (\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$100\\ge {m}^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>100<\/mml:mn>\n                            <mml:mo>\u2265<\/mml:mo>\n                            <mml:msup>\n                              <mml:mrow>\n                                <mml:mi>m<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$1000\\ge {m}^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mn>1000<\/mml:mn>\n                            <mml:mo>\u2265<\/mml:mo>\n                            <mml:msup>\n                              <mml:mrow>\n                                <mml:mi>m<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    , and\n                    <jats:inline-formula>\n                      <jats:alternatives>\n                        <jats:tex-math>$$\\text{10,000}\\ge {m}^{2}$$<\/jats:tex-math>\n                        <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                          <mml:mrow>\n                            <mml:mtext>10,000<\/mml:mtext>\n                            <mml:mo>\u2265<\/mml:mo>\n                            <mml:msup>\n                              <mml:mrow>\n                                <mml:mi>m<\/mml:mi>\n                              <\/mml:mrow>\n                              <mml:mn>2<\/mml:mn>\n                            <\/mml:msup>\n                          <\/mml:mrow>\n                        <\/mml:math>\n                      <\/jats:alternatives>\n                    <\/jats:inline-formula>\n                    ) by developing landslide susceptibility models using nine geo-factors and a Random Forest machine learning algorithm. The temporal landslide probability at the district level was determined by integrating recurrence intervals, calculated from the past six years\u2019 landslide incidences, into a Poisson model to estimate the likelihood of experiencing one or more landslides at a location in the next ten years. Landslide size probabilities were derived from frequency\u2013area distributions of the landslides from the inventories. The landslide hazard model was developed by synthesizing spatial, temporal, and size probabilities\u2014presuming their independence. Nine landslide hazard scenarios were developed considering three time frames (2, 5, and 10\u00a0years) and three landslide size categories. Finally, the landslide risk models for streams and roads over the next 2-, 5-, and 10\u00a0years were computed by integrating the spatial, temporal, and size probabilities of landslides with the index of  connectivity\u00a0(IC). Landslide risk models indicate increased landslide risk for road networks and streams (hydropower infrastructure) for longer periods.\n                  <\/jats:p>","DOI":"10.1007\/s11356-025-37189-3","type":"journal-article","created":{"date-parts":[[2025,12,18]],"date-time":"2025-12-18T02:28:52Z","timestamp":1766024932000},"page":"30776-30793","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Quantitative assessment of landslide hazard and risk at regional-scale: a case study from central Vietnam"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3220-8899","authenticated-orcid":false,"given":"Raja","family":"Das","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pham","family":"Van Tien","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karl W.","family":"Wegmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,18]]},"reference":[{"issue":"1","key":"37189_CR1","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1080\/19475705.2019.1710580","volume":"11","author":"SK Aksha","year":"2020","unstructured":"Aksha SK, Resler LM, Juran L, Carstensen LW Jr (2020) A geospatial analysis of multi-hazard risk in Dharan. 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