Natural Hazards

, Volume 37, Issue 3, pp 331–372 | Cite as

Predicting Lahar-Inundation Zones: Case Study in West Mount Pinatubo, Philippines

  • Emmanuel John M. Carranza
  • Ofelia T. Castro


This paper demonstrates techniques for pre-eruption prediction of lahar-inundation zones in areas where a volcano has not erupted within living memory and/or where baseline geological information about past lahars could be scarce or investigations to delimit past lahars might be incomplete. A lahar source (or proximal lahar-inundation) zone is predicted based on ratio of vertical descent to horizontal run-out of eruptive deposits that spawn lahars. Immediate post-eruption distal lahar-inundation zones are predicted based on “pre-eruption” distal lahar-inundation zones and on spatial factors derived from a digital elevation model. Susceptibility to distal lahar-inundation is estimated by weights-of-evidence, by logistic regression and by evidential belief functions. Predictive techniques are applied using a geographic information system and are tested in western part of Pinatubo volcano (Philippines). Predictive maps are compared with a forecast volcanic-hazard map through validation against a field-based volcanic-hazard map. The predictive model of proximal lahar-inundation zone has “true positive” prediction accuracy, “true negative” prediction accuracy, “false positive” prediction error and “false negative” prediction error that are similar to those of the forecast volcanic-hazard map. The predictive models of distal lahar inundation zones have higher “true positive” prediction accuracy and lower “false negative” prediction error than the forecast volcanic-hazard map, although the latter has higher “true negative” prediction accuracy and lower “false positive” prediction error than the former. The results illustrate utility of proposed predictive techniques in providing geo-information could be used, howbeit with caution, for planning to mitigate potential lahar hazards well ahead of an eruption that could generate substantial source materials for lahar formation.


lahars predictive modeling weights-of-evidence logistic regression evidential belief functions DEM GIS 


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Copyright information

© Springer 2006

Authors and Affiliations

  • Emmanuel John M. Carranza
    • 1
  • Ofelia T. Castro
    • 2
  1. 1.Department of Earth Systems AnalysisInternational Institute for Geo-Information Science and Earth Observation (ITC)EnschedeThe Netherlands
  2. 2.Mapping DepartmentNational Mapping and Resource Information Authority (NAMRIA)MakatiPhilippines

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