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Landslide hazard and community-based risk reduction effort in Karanganyar and the surrounding area, central Java, Indonesia 总被引：1，自引：0，他引：1
KARNAWATI Dwikorita FATHANI Teuku F IGNATIUS Sudarno ANDAYANI Budi LEGONO Djoko BURTON Paul W 《山地科学学报》2011,8(2):149-153
Karanganyar and the surrounding area are situated in a dynamic volcanic arc region, where landslide frequently occurs during
the rainy season. The rain-induced landslide disasters have been resulting in 65 fatalities and a substantial socioeconomical
loss in last December 2007. Again, in early February 2009, 6 more people died, hundreds of people temporary evacuated and
tens of houses damaged due to the rain-induced landslide. Accordingly, inter-disciplinary approach for geological, geotechnical
and social investigations were undertaken with the goal for improving community resilience in the landslide vulnerable villages.
Landslide hazard mapping and community-based landslide mitigation were conducted to reduce the risk of landslides. The hazard
mapping was carried out based on the susceptibility assessment with respect to the conditions of slope inclination, types
and engineering properties of lithology/soil as well as the types of landuse. All of those parameters were analyzed by applying
weighing and scoring system which were calculated by semi qualitative approach (Analytical Hierarchical Process). It was found
that the weathered andesitic-steep slope (steeper than 30o) was identified as the highest susceptible slope for rapid landslide,
whilst the gentle colluvial slope with inter-stratification of tuffaceous clay-silt was found to be the susceptible slope
for creeping. Finally, a programme for landslide risk reduction and control were developed with special emphasize on community-based
landslide mitigation and early warning system. It should be highlighted that the social approach needs to be properly addressed
in order to guarantee the effectiveness of landslide risk reduction. 相似文献
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Sassa Shinji Grilli Stephan T. Tappin David R. Sassa Kyoji Karnawati Dwikorita Gusiakov Viacheslav K. L&#;vholt Finn 《Landslides》2022,19(2):533-535
Landslides - A World Tsunami Awareness Day Special Event was held in hybrid mode on 5 November 2021, during the Fifth World Landslide Forum, in Kyoto, Japan. In this context, a panel discussion was... 相似文献
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Prototyping an experimental early warning system for rainfall-induced landslides in Indonesia using satellite remote sensing and geospatial datasets 总被引：10，自引：2，他引：8
Zonghu Liao Yang Hong Jun Wang Hiroshi Fukuoka Kyoji Sassa Dwikorita Karnawati Faisal Fathani 《Landslides》2010,7(3):317-324
An early warning system has been developed to predict rainfall-induced shallow landslides over Java Island, Indonesia. The
prototyped early warning system integrates three major components: (1) a susceptibility mapping and hotspot identification
component based on a land surface geospatial database (topographical information, maps of soil properties, and local landslide
inventory, etc.); (2) a satellite-based precipitation monitoring system () and a precipitation forecasting model (i.e., Weather Research Forecast); and (3) a physically based, rainfall-induced landslide
prediction model SLIDE. The system utilizes the modified physical model to calculate a factor of safety that accounts for
the contribution of rainfall infiltration and partial saturation to the shear strength of the soil in topographically complex
terrains. In use, the land-surface “where” information will be integrated with the “when” rainfall triggers by the landslide
prediction model to predict potential slope failures as a function of time and location. In this system, geomorphologic data
are primarily based on 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, digital elevation
model (DEM), and 1-km soil maps. Precipitation forcing comes from both satellite-based, real-time National Aeronautics and
Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM), and Weather Research Forecasting (WRF) model forecasts.
The system’s prediction performance has been evaluated using a local landslide inventory, and results show that the system
successfully predicted landslides in correspondence to the time of occurrence of the real landslide events. Integration of
spatially distributed remote sensing precipitation products and in-situ datasets in this prototype system enables us to further
develop a regional, early warning tool in the future for predicting rainfall-induced landslides in Indonesia. 相似文献
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