Development of slope failure spatial model for upper sub-basin of Langat River
| dc.contributor.author | Wan Ibrahim, Wan Mohd Muhiyuddin | |
| dc.date.accessioned | 2014-11-03T02:25:43Z | |
| dc.date.available | 2014-11-03T02:25:43Z | |
| dc.date.issued | 2006 | |
| dc.description | PhD | en_US |
| dc.description.abstract | Increasing pressure for development in Malaysia in recent years due to rapid population growth and urbanization has caused numerous environmental related problems such as slope failure and soil erosion. Increasing of slope failure event in Malaysia has caused degradation to properties, life and environment. Statistically, from 26 slope failure cases recorded in press within 1993 – 2002, 150 died, 30 were injured and thousands were evacuated. This means there are 6 deaths in each case. The objective of this study is to measure spatial factors and their significance that contribute to slope failure and then model the slope failure using integrating GIS and RS technology with statistical logistic regression analysis. 58 slope failure cases and 58 random cases represent 1 and 0 value being used to build the model. Each causative factor mapped and extracted by slope failure point to get attributes from each map. Attributes from each map join together to produce slope failure database. 65.5% of the data was used to develop the model and 34.5% to test the model. From logistic regression analysis, 10 over 14 spatial causative factors have shown the significant value under 0.1 such as slope steepness, landuse, aspect, curvature, degradation zone, annual mean rainfall, shortest distance from slope failure to road, shortest distance from slope failure to major lineament, river density and Antecedent Precipitation Index (API). 96.1% cases were predicted correctly compared to previous study which reached only 82.8%. Model test showed 80% of slope failure cases were predicted correctly. According to R2 value, the model only explained 83% from overall factors that contributed to slope failure. Sensitivity analysis explained that predicted accuracy and test accuracy do not show any significant differences when combination of location and random sample chosen are different. Statistical logistic regression model then is converted to probability which range from 0 (not occur) to 1 (occur). The final product is Slope failure susceptibility zone map. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/297 | |
| dc.language.iso | en | en_US |
| dc.subject | Humanities | en_US |
| dc.subject | Spatial model | en_US |
| dc.subject | Upper sub-basin | en_US |
| dc.title | Development of slope failure spatial model for upper sub-basin of Langat River | en_US |
| dc.type | Thesis | en_US |
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