Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Disaster management requires spatial information as a backbone of preparedness
and mitigation process. In that context, an assessment of landslide susceptibility
becomes essential in an area that is prone to landslide due to its geographical
condition. The Tawangmangu, Jenawi and Ngargoyoso Subdistric in Karanganyar
Regency is the one of such areas, and is the area most frequently hit by landslides in
the Central Java Province of Indonesia.
In this study, three different methods were applied to examine landslide
susceptibility in that area: heuristic, statistical logistic regression and Artificial
Neural Network (ANN). Heuristic method is a knowledge-based approach whereas
the latter two are categorized as data-driven methods due to the involvement of
landslide inventory in their analysis. Eight site-specific available and commonly used
landslide influencing factors (slope, aspect, topographical shape, curvature,
lithology, land use, distance to road and distance to river) were preprocessed in a
GIS environment and then analyzed using statistical and GIS tools to understand the
relationship and significance of each to landslide occurrence, and to generate
landslide susceptibility maps. ILWIS, Idrisi and ArcGIS software were used to
prepare the dataset and visualize the model while PASW was employed to run
prediction models (logistic regression for statistical method and multi-layer
perceptron for ANN). The study employed degree of fit and Receiving Operating
Characteristic (ROC) to assess the models performance.
The region was mapped into five landslide susceptibility classes: very low, low,
moderate, high and very high class. The results also showed that lithology, land use
and topographical are the three most influential factors (i.e., significant in
controlling the landslide to take place). According to degree of fit analysis applied to
all models, ANN performed better than the other models when predicting landslide
susceptibility of the study area. Meanwhile, according to ROC analysis applied to
data-driven methods, ANN shows better performance (AUC 0,988) than statistical
logistic regression (AUC 0,959)
