Ramkrishna Maiti

Integrating the Analytical Hierarchy Process (AHP) and the frequency ratio (FR) model in landslide susceptibility mapping of Shiv-khola watershed, Darjeeling Himalaya

To prepare a landslide susceptibility map of Shiv-khola watershed, one of the landslide prone parts of Darjeeling Himalaya, remote sensing and GIS tools were used to integrate 10 landslide triggering parameters: lithology, slope angle, slope aspect, slope curvature, drainage density, upslope contributing area (UCA), lineament, settlement density, road contributing area (RCA), and land use and land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to derive factor weights using MATLAB with reasonable consistency ratio (CR). The frequency ratio (FR) model was used to derive class frequency ratio or class weights that indicate the relative importance of individual classes for each factor. The weighted linear combination (WLC) method was used to determine the landslide susceptibility index value (LSIV) on a GIS platform, by incorporating both factor weights and class weights. The Shiv-khola watershed is classified into five landslide susceptibility zones. The overall classification accuracy is 99.22 and Kappa Statistics is 0.894.

Landslide Susceptibility Analysis of Shiv-Khola Watershed, Darjiling: A Remote Sensing & GIS Based Analytical Hierarchy Process (AHP)

In the present study, Remote Sensing Technique and GIS tools were used to prepare landslide susceptibility map of Shiv-khola watershed, one of the landslide prone part of Darjiling Himalaya, based on 9 landslide inducing parameters like lithology, slope gradient, slope aspect, slope curvature, drainage density, upslope contributing area, land use and land cover, road contributing area and settlement density applying Analytical Hierarchy Approach (AHA). In this approach, quantification of the factors was executed on priority basis by pair-wise comparison of the factors. Couple comparing matrix of the factors were being made with reasonable consistency for understanding relative dominance of the factors as well as for assigning weighted mean/prioritized factor rating value for each landslide triggering factors through arithmetic mean method using MATLAB Software. The factor maps/thematic data layers were generated with the help of SOI Topo-sheet, LIIS-III Satellite Image (IRS P6/Sensor-LISS-III, Path-107, Row-052, date-18/03/2010) by using Erdas Imagine 8.5, PCI Geomatica, Arc View and ARC GIS Software. Landslide frequency (%) for each class of all the thematic data layers was calculated to assign the class weight value/rank value. Then, weighted linear combination (WLC) model was implied to determine the landslide susceptibility coefficient value (LSCV or ‘M’) integrating factors weight and assigned class weight on GIS platform. Greater the value of M, higher is the propensity of landslide susceptibility over the space. Then Shivkhola watershed was classified into seven landslide susceptibility zones and the result was verified by ground truth assessment of existing landslide location where the classification accuracy was 92.86 and overall Kappa statistics was 0.8919.

Semi-quantitative approaches for landslide assessment and prediction

Introduction.- Geo-spatial Variability Geomorphic Parameters and Slope Instability.- Hydrologic Parameters and Slope Instability.- Surface Run-off, Soil Erosion and Slope Instability.- Geomorphic threshold Landslide.- Stability Model and Landslide Susceptibility Using Geo-technical Properties of Soil.- Application of Analytical Hierarchy Process (AHP)&Frequency Ratio (FR) in Assessing Landslide Susceptibility and Risk.- Landslide Mitigation.

Spatial analysis of soil properties using GIS based geostatistics models

Accurate assessment of the spatial variability of soil properties is key component of the agriculture ecosystem and environment modeling. The main objective of the present study is to measure the soil properties and their spatial variability. A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability. In November 2014 a total of 32 soil samples were collected in the field through random sampling in Medinipur Sadar block of Paschim Medinipur district in West Bengal (India). Soil properties of pH, electric conductivity (EC), phosphorus (P), potassium (K), and organic carbon (OC) were estimated using the standard analytical methods. A classical ordinary kriging (OK) interpolation was used for direct visualization of soil properties. The spatial distribution of EC, pH, and OC in soil are influenced by structural factors, such as climate, parent material, topography, soil properties and other natural factors. The semivariograms of the six soil properties were fit with exponential curve and root mean square error value is near about zero (0). Finally, spatial distribution and correlation between OC and other soil properties is shown by overlay of maps in GIS environment. The present study suggest that the OK interpolation can directly reveal the spatial distribution of soil properties and the sample distance in this study is sufficient for interpolation.

Vegetation Influence on Runoff and Sediment Yield in the Lateritic Region: An Experimental Study

The lateritic badland topography (Western part of West Bengal, India) is prone to severe erosion, caused by heavy rainfall events of short duration and high intensities. Five catchments were instrumented in order to study the rainfall– runoff process and soil management impact on runoff and/or sediment yield. In the five micro catchments (Rangamati, Medinipur), characterized by a homogeneity of surface geology, a data set of about 43 rainfall–runoff events covering the January 2012 to Sept, 2012 period was generated by field monitoring. Multiple regression analysis is done to define the role of rainfall volume vis-a-vis vegetation cover on sediment yield. The physical and chemical properties of soil were estimated at the initial and final stage of the gully development in the lower gully basin area. Temporal assessment of soil erosion indicated that increase of rainfall volume protracted the whole process of sediment production, and vegetation on the slope delayed that process. Results indicated that the highest spatial coverage of vegetation (73.5%) yield very low amount of soil [basin-I experimental site (Adjusted R2 = 0.56)] whereas, the lowest spatial coverage (5.9%) leads to severe soil loss [(basin-IV experimental site (Adjusted R2 = 0.33)]. Results illustrated that at the initial stage, the percent of sand was maximum in the upper catchment of each gully basin and the concentration of silt and clay is less. Gradually as vegetation starts trapping the sediment, composition of soil changes registering higher percentage of finer particles. Again, the nutrients detached from the upper catchment were arrested by check dams that induced nutrients supply and water storage, which in turn, increased the growth of vegetation. This result proved the significance of vegetation cover to curb soil erosion and it may help the planners and managers to take proper decision for the conservation of soil.

Application of Analytical Hierarchy Process (AHP) and Frequency Ratio (FR) Model in Assessing Landslide Susceptibility and Risk

To prepare landslide susceptibility map of the Shivkhola watershed, one of the landslide prone part of Darjiling Himalaya, RS and GIS tools were being used to integrate 10 landslide triggering parameters like lithology, slope angle, slope aspect, slope curvature, drainage density, lineament, upslope contributing area (UCA), road contributing area (RCA) settlement density, and land use and land cover (LULC). Analytical Hierarchy Process (AHP) was applied to quantify all the factors by estimating factors weight on MATLAB Software with reasonable consistency ratio (CR). Frequency ratio model (FR) was used to derive class frequency ratio or class weight incorporating both pixels with and without landslides and to determine the relative importance of individual classes. All the required data layers were prepared in consultation with SOI Topo-sheet (78B/5), LIIS-III Satellite Image (2010) by using Erdas Imagine 8.5, PCI Geomatica, and ARC GIS Software. The weighted linear combination (WLC) method was followed to combine factors weight and class weight and to determine the landslide susceptibility coefficient value (LSCV or ‘M’) on GIS platform. Greater the value of ‘M’, higher is the susceptibility of landslide. The Shivkhola watershed was classified into five landslide susceptibility zones by averaging window lengths of 3, 5, 7, and 9 and taking into account the landslide threshold boundaries value of 7.05, 9.29, 11.5, and 13.8. The overall classification accuracy rate is 92.22 % and overall Kappa statistics is 0.894. The elements like weighted LULC map, RCA (road contributing area) map and settlement density map were developed and their weighted linear combination was performed to prepare landslide risk exposure map. Then by integrating landslide susceptibility map and landslide risk exposure map landslide hazard risk co-efficient values were derived and a classification was incorporated on ARC GIS Platform to prepare landslide hazard risk map of the Shivkhola watershed. To evaluate the validity of the landslide hazard risk map, probability/chance of landslide hazard risk event has been estimated by means of frequency ratio (FR) between landslide hazard risk area (%) and number of risk events (%) for each landslide hazard risk class. Finally, an accuracy assessment was made through a comparative study between true GPS derived data and a set of randomly selected pixels points from the classified image corresponding to the true data from 50 locations on ERDAS Imagine (8.5) which depicts that the classification accuracy of the landslide hazard risk map was 92.89 with overall Kappa statistics of 0.8929.

Sedimentation under variable shear stress at lower reach of the Rupnarayan River, West Bengal, India

Abstract The lower reach of the Rupnarayan River has been deteriorated and incapacitated due to continuous sedimentation (26.57 million m3 shoaling in last 25 years). Attempts have been made to explain the causes and mechanisms of sedimentation in connection to the seasonal fluctuation of shear stress. River depth and water velocity was measured by echo-sounder and current meter respectively. Textural analysis of grains was done by sieving technique. Available and critical shear stress (N/m2) have been calculated following Du Boys (1879), Shield (1936) and Van Ledden (2003) formula. The lack of available energy to transport a particular grain size during low tide (in dry season) is the main reason behind the rapid sedimentation in this area. Most of the places (>75%) having negative deviation of shear stress (available shear stress lesser than critical shear stress), during low tide are characterized by deposition of sediments. The presence of mud (silt and clay) above the critical limit (15%) in some of the sediment samples generates the cohesive property, restricts sediments entrainment and invites sedimentation.

Slope Stability Model and Landslide Susceptibility Using Geo-technical Properties of Soil

The present study deals with the assessment of geo-technical parameters i.e. surface inclination (⊝), soil depth (z), cohesion (c), angle of internal friction (φ), soil saturation index (m), soil density (γs) and density of water (γw) and to construct 1D (one dimensional) Slope stability model for preparing the slope instability map under dry, semi-saturated and saturated condition of the landslide prone small hilly Shivkhola Watershed of Darjeeling Himalaya. To determine the spatial distribution of slope instability in the watershed, safety factor value for 50 different locations were being estimated and with the help of GIS tools. The probability or the chances of landslide phenomena in each class of slope instability maps were extracted by means of frequency ratio (FR) which shows that the probability/chances of landslide events could be expected as very high in the high to very high landslide susceptibility area and vice versa in all three conditions. The analysis of slope instability under three conditions also suggested that there was an aerial expansion of very high landslide susceptibility in saturated condition in comparison to dry and semi-saturated condition. This aerial expansion was the outcome of complete saturation and reduction of shearing strength of the slope materials above the failure plane surface. Finally, an accuracy assessment was made by ground truth verification of the existing landslide locations where the classification accuracy for dry, semi-saturated and saturated conditions was 93.86, 94.58 and 85.44 % respectively.

Impact Assessment of Hydrologic Attributes and Slope Instability

Quantitative geomorphology provides a systematic approach to the analysis of a complex landscape of any size. The stability of the mountain slope depends upon the prevalence of various hydrologic variables. In the present work, the excess and deficit moisture period in a year and its role in slope instability were assessed studying rainfall and evapotranspiration. Study envisages that July and August are the most consistent rainfall months of the year where the values of co-efficient of variation are very low. The distribution of drainage and its evolution has been studied to determine the drainage concentration over the slope surface and their role in slope steepening and instability. To assume the slope saturation of materials saturation, stream confluence points/junction points were studied for individual sub-watersheds. The length of drainage per unit area and upslope contributing area were analyzed spatially in connection to the landslide potentiality. The existence of moderate drainage density may invite havoc slope failure on convex slope segment. Greater the upslope contributing area, maximum is the slope saturation and slope instability in the Shivkhola watershed. Some important drainage basin parameters such as basin shape, form factor, circularity ratio, elongation ratio, compactness factor, and elipticity index of the sub-watersheds were considered to develop the priority scale on slope instability. The sub-basin I and IV are more efficient in drainage and are more erosion and landslide prone followed by sub-watershed V, II, III and VI.

Identification of the Sediment Sources Using X-Ray Diffraction (XRD) Technique

X-ray diffraction (XRD) technique is used to understand the sources of sediments through identification of mineral composition of sediments in the lower reach of the Rupnarayan River to explain the causes and mechanisms of sedimentation. A total of 21 sediment samples (13 samples from river bed and 8 samples from river banks) have been collected for knowing the sediment mineralogy. Sediment samples are washed by boiled distilled water, dried, disintegrated and scanned at an interval of 7°–45°2θ in XPERT-PRO diffractometer. Diffractograms produced by XRD study indicates that the entire lower reach shows the dominance of the minerals such as quartz, chlorite, illite, anatase, goethite, oligoclase, chloritoid, corundum, sillimanite, which have their origin in the upper and middle catchment area with small contribution from the lower catchment and river banks. Statistical experiment indicates that excluding tourmaline and anatase, all the minerals show steady trend in concentration in sediments. Principal Component Analysis (PCA) indicates that five Eigen values contribute for about 83.154% of the total variation of the distribution of minerals. The minerals discharged from the upper catchment are captured in the estuary and again redistributed towards upstream by stronger flood tide. This leads to a un-conspicuous and hapazard distribution of minerals in the area under study.