A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

This paper summarizes findings of landslide hazard analysis on Penang Island, Malaysia, using frequency ratio, logistic regression, and artificial neural network models with the aid of GIS tools and remote sensing data. Landslide locations were identified and an inventory map was constructed by trained geomorphologists using photo-interpretation from archived aerial photographs supported by field surveys. A SPOT 5 satellite pan sharpened image acquired in January 2005 was used for land-cover classification supported by a topographic map. The above digitally processed images were subsequently combined in a GIS with ancillary data, for example topographical (slope, aspect, curvature, drainage), geological (litho types and lineaments), soil types, and normalized difference vegetation index (NDVI) data, and used to construct a spatial database using GIS and image processing. Three landslide hazard maps were constructed on the basis of landslide inventories and thematic layers, using frequency ratio, logistic regression, and artificial neural network models. Further, each thematic layer’s weight was determined by the back-propagation training method and landslide hazard indices were calculated using the trained back-propagation weights. The results of the analysis were verified and compared using the landslide location data and the accuracy observed was 86.41, 89.59, and 83.55% for frequency ratio, logistic regression, and artificial neural network models, respectively. On the basis of the higher percentages of landslide bodies predicted in very highly hazardous and highly hazardous zones, the results obtained by use of the logistic regression model were slightly more accurate than those from the other models used for landslide hazard analysis. The results from the neural network model suggest the effect of topographic slope is the highest and most important factor with weightage value (1.0), which is more than twice that of the other factors, followed by the NDVI (0.52), and then precipitation (0.42). Further, the results revealed that distance from lineament has the lowest weightage, with a value of 0. This shows that in the study area, fault lines and structural features do not contribute much to landslide triggering.

Delineation of landslide hazard areas on Penang Island, Malaysia, by using frequency ratio, logistic regression, and artificial neural network models

Pharmaceutical applications of chitosan

Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area

Landslides are one of the most frequent and common natural hazards in Malaysia. Preparation of landslide susceptibility maps is one of the first and most important steps in the landslide hazard mitigation. However, due to complex nature of landslides, producing a reliable susceptibility map is not easy. For this reason, a number of different approaches have been used, including direct and indirect heuristic approaches, deterministic, probabilistic, statistical, and data mining approaches. Moreover, these landslides can be systematically assessed and mapped through a traditional mapping framework using geoinformation technologies. Since the early 1990s, several mathematical models have been developed and applied to landslide hazard mapping using geographic information system (GIS). Among various approaches, fuzzy logic relation for mapping landslide susceptibility is one of the techniques that allows to describe the role of each predisposing factor (landslide-conditioning parameters) and their optimal combination. This paper presents a new attempt at landslide susceptibility mapping using fuzzy logic relations and their cross application of membership values to three study areas in Malaysia using a GIS. The possibility of capturing the judgment and the modeling of conditioning factors are the main advantages of using fuzzy logic. These models are capable to capture the conditioning factors directly affecting the landslides and also the inter-relationship among them. In the first stage of the study, a landslide inventory was complied for each of the three study areas using both field surveys and airphoto studies. Using total 12 topographic and lithological variables, landslide susceptibility models were developed using the fuzzy logic approach. Then the landslide inventory and the parameter maps were analyzed together using the fuzzy relations and the landslide susceptibility maps produced. Finally, the prediction performance of the susceptibility maps was checked by considering field-verified landslide locations in the studied areas. Further, the susceptibility maps were validated using the receiver-operating characteristics (ROC) success rate curves. The ROC curve technique is based on plotting model sensitivity—true positive fraction values calculated for different threshold values versus model specificity—true negative fraction values on a graph. The ROC curves were calculated for the landslide susceptibility maps obtained from the application and cross application of fuzzy logic relations. Qualitatively, the produced landslide susceptibility maps showed greater than 82% landslide susceptibility in all nine cases. The results indicated that, when compared with the landslide susceptibility maps, the landslides identified in the study areas were found to be located in the very high and high susceptibility zones. This shows that as far as the performance of the fuzzy logic relation approach is concerned, the results appeared to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.

Use of GIS-based fuzzy logic relations and its cross application to produce landslide susceptibility maps in three test areas in Malaysia

The predominantly hilly terrain of Penang Island combined with average maximum daily temperatures ranging between 27-35oC and peak rainfall as high as 647 cm makes the overall area potentially susceptible to landslips. Over the recent past construction industry has shown a rapid growth mainly due to increase in the inflow of international tourists and other economic reasons. Eventually, the magnitude of disaster associated with landslides has also increased and that is one of the major concerns of engineering geologists and geotechnical engineers. With this background this paper attempts to characterize the largely granitic residual soils of Penang Island by discussing the nature, structural features, engineering behavior and field properties of soil samples extracted from 8 sites. These sites are distinctly chosen from a database of 31 sites for they are located over different prime geological formations. The mean values of various design properties at different depths are calculated and plotted to identify the property trend with depth and important behavioral features relevant to landslides are discussed. Similarly, compression index values are plotted against initial void ratio and liquid limit separately and resulting correlations are compared with the established ones. Correlations given by Azzous are found to hold good. Lastly, in the light of the lessons learnt from the past landslides and the current characterization results some improvements regarding slope instability problem are discussed.

Characterization and Geotechnical Properties of Penang Residual Soils with Emphasis on Landslides

Energy harvesting from pavements and roadways: A comprehensive review of technologies, materials, and challenges

Resol based chitosan/nano-hydroxyapatite nanoensemble for effective bone tissue engineering.

Penang Island of Malaysia is vulnerable to various natural hazards due to its hilly terrain and weather. Due to recent rapid population and economic growth, the probability of disaster associated with natural hazards has increased. This article characterizes geological and geotechnical parameters collected from field data that relate to natural hazard susceptibility. The areas of investigation were chosen so that each area lay on different geological formations. The variations in selected parameters with depth were obtained. The correlation within parameters was established and was found to be in agreement with that from other researchers. The investigation identifies parameters that can be used to evaluate the degree of hazard susceptibility.

Characterization and Hazard Study of Two Areas of Penang Island, Malaysia

Bioactive Phoenix dactylifera seeds incorporated chitosan/hydroxyapatite nanoconjugate for prospective bone tissue engineering applications: A bio-synergistic approach.

Mohd Ahmadullah Farooqi

Papers

Characterization and Geotechnical Properties of Penang Residual Soils with Emphasis on Landslides

Energy harvesting from pavements and roadways: A comprehensive review of technologies, materials, and challenges

Resol based chitosan/nano-hydroxyapatite nanoensemble for effective bone tissue engineering.

Characterization and Hazard Study of Two Areas of Penang Island, Malaysia

Bioactive Phoenix dactylifera seeds incorporated chitosan/hydroxyapatite nanoconjugate for prospective bone tissue engineering applications: A bio-synergistic approach.