Landslide Hazard Zonation along the MH SH-73 at Kelghar Ghat, Satara, Maharashtra

Road transportation is the most common victim of landslide in the world. The present study investigates the landslide hazard zonation along the MH SH-73 at Kelghar ghat between Medha and Mahabaleshwar hill station of Maharashtra. Remote Sensing and GIS were used for the landslide hazard zonation of this section. The ghat section was buffered 100 m on both side to define the extent of study area based on the field investigation. The study incorporated predefined important landslide causative factors, viz. slope, rainfall, relief, lithology, soil depth, soil erosion, soil texture, land use / land cover, drainage distance, drainage density, lineament distance, lineament density, aspect, temperature, landslide inventory and in this approach fifteen thematic layers were prepared in GIS platform. The weight and score were assigned to each thematic layer based on heuristic approach on their relative importance in causing landslide. Multi-criteria model in ArcGIS 10.5 software were used for the mapping landslide hazard zones and it were classified into six zones: very high (1.3 %), high (1.7 %), moderate (3.4 %), low (7.6 %) very low (8.4 %) and no risk zone (77.7 %). The final result of this research can help for proper mitigation and adaptation measures for engineers, planners and administrators for this ghat section. Keywords: GIS, Landslide Hazard Zonation, Remote Sensing, Susceptibility, Sahyadri, Kelghar.

Landslide Hazard Zonation and Evaluation around Debre Werk Town, North West Ethiopia

The present research was conducted in the town of Debre Werk, East Gojjam, North West Ethiopia, with the ultimate aim of conducting a Landslide Hazard Zonation and Evaluation. To reach this aim, the Slope Stability Susceptibility Evaluation Parameter (SSEP) rating system was adopted to zone and evaluate the landslide status of the area. This rating system was done by considering the parameters of intrinsic and external triggering factors that cause landslides. Systematic and detailed fieldwork had been undertaken as a justification. Secondary data, on the other hand, was required to define the general conditions of the area and to gain a thorough understanding of the field of study. Ratings for intrinsic parameters in the SSEP system include slope morphometry, relative relief, slope content, geological structures/discontinuities, land use land cover, groundwater, and external parameters include erosion, seismicity, and manmade activities. Individual facet-wise ratings for intrinsic causative factors and external triggering factors ratings are summarized to evaluate the landslide hazard zonation of an environment. The sum of all causative parameter ratings will give evaluated landslide hazards (ELH). Therefore, the research was carried out by dividing the study area into 70 facets. Then 85 landslide incidents in the study area were investigated. From 85 landslides, 39 districts showed past landslides, 23 showed active landslides and the remaining 23 districts showed signs of landslides. The delineated 70 facets were categorized into 3 landslide hazard zones. There are about 73.3km2 (27.2%) of the study area within the low hazard zone, 140.8km2 (52.1%) within the moderate hazard zone, and the remaining 55.9km2 (20.7%) within the high hazard zone. Based on the findings of SSEP, it can be deduced that the present research area is highly susceptible to landslide and requires special attention during rainy seasons. Finally, the validity of the prepared LHZ map was checked by overlaying the inventory map over the produced LHZ map. The overlap map shows that 17 districts showing active landslides, 2 districts showing signs of landslides, and 5 districts showing past landslide activities fall into high hazard zones. Likewise, 5 districts showing active landslides, 3 districts showing signs of landslides, and 28 districts showing past landslides fall into moderate hazard zones. The remaining 1 district showing active landslides, 18 districts showing signs of landslides, and 6 districts showing past landslide activities fall into moderate hazard zones.

Landslide Hazard Zonation and Evaluation Around Debre Markos Town, NW Ethiopia—A GIS-Based Statistical Approach

The main goal of this research was to perform a landslide hazard zonation and evaluation around Debre Markos town, North West Ethiopia, found about 300 km from the capital city Addis Ababa. To achieve the aim, a GIS-based probabilistic statistical technique was used to rate the governing factors, followed by geoprocessing in the GIS setting to produce the landslide hazard zonation map. In this research, eight internal causative and external triggering factors were selected: slope material (lithology and soil mass), elevation, aspect, slope, land use land cover, curvature, distance to fault, and distance to drainage. Data were collected from field mapping, secondary maps, and digital elevation models. Systematic and detailed fieldwork had been done for image interpretation and inventory mapping. Accordingly, the past landslides map of the research area was prepared. All influencing factors were statistically analyzed to determine their relationship to previous landslides. The results revealed that 17.15% (40.60 km2), 25.53% (60.45 km2), 28.04% (66.39 km2), 18.93% (44.83 km2), and 10.36% (24.54 km2) of the research area falls under no hazard, low hazard, moderate hazard, high hazard, and very high hazard respectively. The validation of the landslide hazard zonation map reveals that 1%, 2%, 3%, and 94% of past landslides fall in no hazard zone, low hazard, moderate hazard zone, and high hazard or very high hazard zones respectively. The validation of the landslide hazard zonation map thus, it has been adequately demonstrated that the adopted approach has produced acceptable results. The defined hazard zones can practically be utilized for land management and infrastructure construction in the study area.

Landslide hazard zonation using logistic regression technique: the case of Shafe and Baso catchments, Gamo highland, Ethiopia

Landslide hazard zonation plays an important role in safe and viable infrastructure development, urbanization, land use, and environmental planning. The Shafe and Baso catchments are found in the Gamo highland which has been highly degraded by erosion and landslides thereby affecting the lives of the local people. In recent decades, recurrent landslide incidences were frequently occurring in this Highland region of Ethiopia in almost every rainy season. This demands landslide hazard zonation in the study area in order to alleviate the problems associated with these landslides. The main objectives of this study are to identify the spatiotemporal landslide distribution of the area; evaluate the landslide influencing factors and prepare the landslide hazard map. In the present study, lithology, groundwater conditions, distance to faults, morphometric factors (slope, aspect and curvature), and land use/land cover were considered as landslide predisposing/influencing factors while precipitation was a triggering factor. All these factor maps and landslide inventory maps were integrated using ArcGIS 10.4 environment. For data analysis, the principle of logistic regression was applied in a statistical package for social sciences (SPSS). The result from this statistical analysis showed that the landslide influencing factors like distance to fault, distance to stream, groundwater zones, lithological units and aspect have revealed the highest contribution to landslide occurrence as they showed greater than a unit odds ratio. The resulting landslide hazard map was divided into five classes: very low (13.48%), low (28.67%), moderate (31.62%), high (18%), and very high (8.2%) hazard zones which was then validated using the goodness of fit techniques and receiver operating characteristic curve (ROC) with an accuracy of 85.4. The high and very high landslide hazard zones should be avoided from further infrastructure and settlement planning unless proper and cost-effective landslide mitigation measures are implemented.

The Comparison Between Certainty Factor Method and Index of Entropy Method For Landslide Hazard Assessment: South Western Chamba District, Himachal Pradesh, India

The present study reflects the contributions of geo-environmental factors that were analyzed for the development of landslide hazard zonation map using certainty factor method and index of entropy method. Heavy rainfall, unscientific excavation of slopes during road construction, expansion of infrastructure, and unplanned growth in urban population were the major factors for unstable slopes in the Lesser Himalayan region. Historical database, interpretation of satellite and Google earth images were used to identification of 248 landslides. The data collected using remote sensing images have been verified by conducting ground truth surveys undertaken from January 2018 to October 2020 in preparing the landslide inventory of the study area. Inventory thus generated was divided into 70% training and 30% validation datasets. Relationships between slope failure and its causative factors (relief, slope, aspect, curvature, lithology, soil, weathering, land use, lineament density, rainfall, and density of drainage networks) were analyzed by using certainty factor (CF) and index of entropy (IOE) methods. The analysis of all causative factors and assigning relative weightage values by using the index of entropy and certainty factor models leads to the generation of Landslide hazard zonation maps of the region. Finally, the landslide prediction accuracy of hazard zonation maps was calculated by drawing Successive Rate Curve (SRC) curves for both training and validation datasets. The outcomes of this study will be useful to government agencies, planners, decision-makers, researchers, and general land-use planners for sustainable development of the study area.