Abstract
Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes.
Highlights
Landslides are momentary and instantaneously happening vandalize natural hazard in mountains; it turns in to a disaster due to immature geology coupled with external temporal triggering factors causing landscape changes and direct and indirect losses
E test accuracy described by the confusion matrix is presented in Figure 11(a), and a comparison between classi cations based on landslide potential index (LPI) values and predicted classi cation based on the Support Vector Machine (SVM) classi er the same as the confusion matrix is presented in Figure 11(b). e confusion matrix is used to explain the classi cation performance model on a set of testing data for which the original labels are known
0.14 0.19 0.73 1.82 (1) e entire study area was divided in to four respective susceptibility zones, that is, low (3.6%), moderate (58.35%), high (32.61%), and very high (5.44%) susceptibility zones, and landslide areas occupied per zone are 0.34%, 14.35%, 33.71%, and 51.60% for low, moderate, high, and very high susceptible zones, respectively; this outcome was validated on the reasoning of landslide distribution, Genetic Programming method and Particle Swarm Optimization (PSO)-Support Vector Machine (SVM) technique
Summary
Landslides are momentary and instantaneously happening vandalize natural hazard in mountains; it turns in to a disaster due to immature geology coupled with external temporal triggering factors causing landscape changes and direct and indirect losses. E influence of gravity is the key operating force for the landslide to happen. Landslide happens when the slope changes from a steady to an unsteady state. It is a motion of mass of rock, earth, or debris along the slope [1]. Is trend of landslides is expected to continue in the time to come due to sustained deforestation, increment of haphazard urbanization, and changing climatic patterns in the landslide-prone areas [4, 5]. Despite advances in science and technology, landslides continue to result in economic, human, and environmental losses worldwide [6]
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