Remote sensing and GIS-based landslide susceptibility mapping using frequency ratio and analytical hierarchy methods in Rize province (NE Turkey)

The northeast part of Turkey is prone to landslides because of the climatic conditions, as well as geologic and geomorphologic characteristics of the region. Especially, frequent landslides in the Rize province often result in significant damage to people and property. Therefore, in order to mitigate the damage from landslides and help the planners in selecting suitable locations for implementing development projects, especially in large areas, it is necessary to scientifically assess susceptible areas. In this study, the frequency ratio method and the analytical hierarchy process (AHP) were used to produce susceptibility maps. Especially, AHP gives best results because of allowing better structuring of various components, including both objective and subjective aspects and comparing them by a logical and thorough method, which involves a matrix-based pairwise comparison of the contribution of different factors for landslide. For this purpose, lithology, slope angle, slope aspect, land cover, distance to stream, drainage density, and distance to road were considered as landslide causal factors for the study area. The processing of multi-geodata sets was carried out in a raster GIS environment. Lithology was derived from the geological database and additional field studies; slope angle, slope aspect, distance to stream, distance to road and drainage density were invented from digital elevation models; land cover was produced from remote sensing imagery. In the end of study, the results of the analysis were verified using actual landslide location data. The validation results showed satisfactory agreement between the susceptibility map and the existing data on landslide locations.     

Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand

A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved.     

Identification of potential groundwater recharge zones in Vaigai upper basin,Tamil Nadu,using GIS-based analytical hierarchical process (AHP) technique

Groundwater recharge is an important process for the management of both surface and subsurface water resources. The present study utilizes the application of analytical hierarchical process (AHP) on geospatial analysis for the exploration of potential zones for artificial groundwater recharge along Vaigai upper basin in the Theni district, Tamil Nadu, India. The morphology of earth surface features such as geology, geomorphology, soil types, land use and land cover, drainage, lineament, and aquifers influence the groundwater recharge in either direct or indirect way. These thematic layers are extracted from Landsat ETM+ image, topographical map, and other collateral data sources. In this study, the multilayers were weighed accordingly to the magnitude of groundwater recharge potential. The AHP technique is a pair-wise matrix analytical method was used to calculate the geometric mean and normalized weight of individual parameters. Further, the normalized weighted layers are mathematically overlaid for preparation of groundwater recharge potential zone map. The results revealed that 21.8 km² of the total area are identified as high potential for groundwater recharge. The gentle slope areas in middle-east and central part have been moderately potential for groundwater recharge. Hilly terrains in south are considered as unsuitable zone for groundwater recharge processes.     

Municipal solid waste landfill siting by using GIS and analytical hierarchy process (AHP): a case study in Qom city,Iran

Qom is the eighth most populated city in center of Iran, and its population growth rate is among the highest in this country. Th presence of a Great Salt Lake, petroleum potential and tourism attractions in this city sheds light on the importance of how solid waste landfill locations should be disposed, located and managed as an environmental issue. Considering the key parameters in landfill site selection, in this study a series of location analysis have been conducted to locate optimum regions for municipal solid waste disposal, using analytical hierarchy process (AHP) and geographical information system (GIS). The main factors in selecting the suitable location for waste disposal include geomorphology–hydrography, environmental–social factors and design criteria, each of which are subdivided into several categories. Criteria are selected according to the regional condition; therefore, important factors such as distance from sea and forested areas were not considered. In the next step, digital layers are weighted and classified according to the available standards and expert judgment. Then, analytical multi-criteria decision-making algorithms as AHP and weighted linear combination are applied upon existing layers in GIS. The results show that by implementing the AHP method in this region only 7% of the study area has a very good and appropriate condition for landfill location and the field observation confirms them. Finally, considering the environmental effects of landfill, appropriate locations are suggested.