Comparison of results of BIS and GSI guidelines on macrolevel landslide hazard zonation — A case study along highway from Bhalukpong to Bomdila,West Kameng district,Arunachal Pradesh

This paper compares the findings of macrolevel landslide hazard zonation carried out along the highway from Bhalukpong to Bomdila, West Kameng district, Arunachal Pradesh following GSI and BIS guidelines. The map resulted from the GSI guideline shows that 69.31% of the faceted area falls under the Low Hazard Zone (LHZ) while 17.69%, 7.31%, 5.03% and 0.65% of the area are in Moderate Hazard Zone (MHZ), High Hazard Zone (HHZ), Very Low Hazard Zone (VLHZ) and Very High Hazard Zone (VHHZ) respectively. Correlation between the landslide incidences and different hazard zones reveals that maximum failure percentage is in VHHZ and it is followed by HHZ, MHZ and LHZ. The second map resulting from BIS guideline reveals that 45.77% of the faceted area falls under MHZ while 41.39%, 11.52% and 1.29% of the area are in HHZ, LHZ and VHHZ respectively. Not a single facet falls in VLHZ.With regard to failure percentage VHHZ experiences 50%, while that of HHZ, MMH and LHZ is roughly 11.5% each. In the study area, the landslide hazard zonation map resulting from GSI guideline broadly conforms to field condition. It may be due to the fact that the study area is along the road corridor where slope cutting and landslides are very common and GSI guideline considers both the slope cutting and landslide parameters, while it is not so in the case of BIS guidelines. However, a final conclusion can be drawn after carrying out such studies in different geological settings.     

The application of GIS-based bivariate statistical methods for landslide hazards assessment in the upper Tons river valley,Western Himalaya,India

Landslides are one of the most frequent and common natural hazards in many parts of Himalaya. To reduce the potential risk, the landslide susceptibility maps are one of the first and most important steps in the landslide hazard mitigation. Earth observation satellite and geographical information system-based techniques have been used to derive and analyse various geo-environmental parameters significant to landslide hazards. In this study, a bivariate statistics method was used for spatial modelling of landslide susceptibility zones. For this purpose, thematic layers including landslide inventory, geology, slope angle, slope aspect, geomorphology, slope morphology, drainage density, lineament and land use/land cover were used. A large number of landslide occurrences have been observed in the upper Tons river valley area of Western Himalaya. The result has been used to spatially classify the study area into zones of very high, high, moderate, low and very low landslide susceptibility zones. About 72% of active landslides have been observed to occur in very high and high hazard zones. The result of the analysis was verified using the landslide location data. The validation result shows significant agreement between the susceptibility map and landslide location. The result can be used to reduce landslide hazards by proper planning.     

Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh,India

Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.     

基于GIS的滑坡灾害危险性区划研究——以重庆市万州区为例

为了弥补滑坡灾害危险性区划研究中影响因子和等级划分的不确定性,结合前人研究成果,依据斜坡几何形态、岩性、地质构造、河流侵蚀、土地利用类型、人类工程活动、降水条件等影响因子与研究区实际已发生的滑坡灾害数之间的关系,编制重庆市万州区滑坡灾害危险性评价标准,并基于GIS技术和信息量模型法,计算滑坡评价因子的信息量,就万州区滑坡危险性进行区划,最后基于乡镇行政区对该区滑坡危险性区划进行细化。结果表明：建设用地、坡高为90～200 m的地形、1 024～1 060 mm的年降雨量以及侏罗系中统上沙溪庙组岩层等因素对万州区滑坡发生影响较大;根据滑坡灾害危险性评价标准,万州区滑坡灾害被划分为高、中、低、极低等4个危险区;应用信息量模型法得到的万州区滑坡危险性区划与实际情况比较吻合;高危险区和中危险区面积分别为564.4 km2和848.6 km2,分别占万州区总面积的16.3%和24.5%,主要分布于长江干流及支流两岸的居民相对集中区以及公路干线地段;高危险和中危险乡镇主要分布在万州区经济较为发达的长江干流两岸,尤其是左岸的黄柏乡、太龙镇、天城镇、李河镇等以及万州主城区。     

Integration of statistical and heuristic approaches for landslide risk analysis: a case of volcanic mountains in West Java Province,Indonesia

Owing to fragile geo-morphology, extreme climatic conditions, and densely populated settlements and rapid development activities, West Java Province is the most landslide hazardous area in Indonesia. So, a landslide risk map for this province bears a great importance such as for land-use planning. It is however widely accepted that landslide risk analysis is often difficult because of the difficulties involved in landslide hazard assessment and estimation of consequences of future landslide events. For instance, lack of multi-temporal inventory map or records of triggering events is often a major problem in landslide hazard mapping. In this study, we propose a simple technique for converting a landslide susceptibility map into a landslide hazard map, which we have employed for landslide risk analysis in one ideally hazardous part of volcanic mountains in West Java Province. The susceptibility analysis was carried out through correlation between past landslides and eight spatial parameters related to instability, i.e. slope, aspect, relative relief, distance to river, geological units, soil type, land use and distance to road. The obtained susceptibility map was validated using cross-time technique, and was collaborated with the frequency-area statistics to respond to ‘when landslide will occur’ and ‘how large it will be’. As for the judgment of the consequences of future landslides, expert opinion was used considering available literature and characteristic of the study area. We have only considered economic loss in terms of physical damage of buildings, roads and agricultural lands for the landslide risk analysis. From this study, we understand the following: (1) the hazard map obtained from conversion of the susceptibility map gives spatial probability and the area of an expected landslide will be greater than 500m² in the next 2 years, (2) the landslide risk map shows that 24% of the total area is in high risk; 30% in moderate risk; 45% in low risk and no risk covers only 1% of the total area, and (3) the loss will be high in agricultural lands, while it will be low in the road structures and buildings.     

Landslide hazard mapping along national highway-154A in Himachal Pradesh,India using information value and frequency ratio

For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.     

Earthquake hazard in Northeast India — A seismic microzonation approach with typical case studies from Sikkim Himalaya and Guwahati city

A comprehensive analytical as well as numerical treatment of seismological, geological, geomorphological and geotechnical concepts has been implemented through microzonation projects in the northeast Indian provinces of Sikkim Himalaya and Guwahati city, representing cases of contrasting geological backgrounds — a hilly terrain and a predominantly alluvial basin respectively. The estimated maximum earthquakes in the underlying seismic source zones, demarcated in the broad northeast Indian region, implicates scenario earthquakes of M _W 8.3 and 8.7 to the respective study regions for deterministic seismic hazard assessments. The microzonation approach as undertaken in the present analyses involves multi-criteria seismic hazard evaluation through thematic integration of contributing factors. The geomorphological themes for Sikkim Himalaya include surface geology, soil cover, slope, rock outcrop and landslide integrated to achieve geological hazard distribution. Seismological themes, namely surface consistent peak ground acceleration and predominant frequency were, thereafter, overlaid on and added with the geological hazard distribution to obtain the seismic hazard microzonation map of the Sikkim Himalaya. On the other hand, the microzonation study of Guwahati city accounts for eight themes — geological and geomorphological, basement or bedrock, landuse, landslide, factor of safety for soil stability, shear wave velocity, predominant frequency, and surface consistent peak ground acceleration. The five broad qualitative hazard classifications — ‘low’, ‘moderate’, ‘high’, ‘moderate high’ and ‘very high’ could be applied in both the cases, albeit with different implications to peak ground acceleration variations. These developed hazard maps offer better representation of the local specific seismic hazard variation in the terrain.     

Earthquake hazard zonation of Sikkim Himalaya using a GIS platform

An earthquake hazard zonation map of Sikkim Himalaya is prepared using eight thematic layers namely Geology (GE), Soil Site Class (SO), Slope (SL), Landslide (LS), Rock Outcrop (RO), Frequency–Wavenumber (F–K) simulated Peak Ground Acceleration (PGA), Predominant Frequency (PF), and Site Response (SR) at predominant frequencies using Geographic Information System (GIS). This necessitates a large scale seismicity analysis for seismic source zone classification and estimation of maximum earthquake magnitude or maximum credible earthquake to be used as a scenario earthquake for a deterministic or quasi-probabilistic seismic scenario generation. The International Seismological Center (ISC) and Global Centroid Moment Tensor (GCMT) catalogues have been used in the present analysis. Combining b-value, fractal correlation dimension (Dc) of the epicenters and the underlying tectonic framework, four seismic source zones are classified in the northeast Indian region. Maximum Earthquake of M _W 8.3 is estimated for the Eastern Himalayan Zone (EHZ) and is used to generate the seismic scenario of the region. The Geohazard map is obtained through the integration of the geological and geomorphological themes namely GE, SO, SL, LS, and RO following a pair-wise comparison in an Analytical Hierarchy Process (AHP). Detail analysis of SR at all the recording stations by receiver function technique is performed using 80 significant events recorded by the Sikkim Strong Motion Array (SSMA). The ground motion synthesis is performed using F–K integration and the corresponding PGA has been estimated using random vibration theory (RVT). Testing for earthquakes of magnitude greater than M _W 5, a few cases presented here, establishes the efficacy and robustness of the F–K simulation algorithm. The geohazard coverage is overlaid and sequentially integrated with PGA, PF, and SR vector layers, in order to evolve the ultimate earthquake hazard microzonation coverage of the territory. Earthquake Hazard Index (EHI) quantitatively classifies the terrain into six hazard levels, while five classes could be identified following the Bureau of Indian Standards (BIS) PGA nomenclature for the seismic zonation of India. EHI is found to vary between 0.15 to 0.83 quantitatively classifying the terrain into six hazard levels as “Low” corresponding to BIS Zone II, “Moderate” corresponding to BIS Zone III, “Moderately High” belonging to BIS Zone IV, “High” corresponding to BIS Zone V(A), “Very High” and “Severe” with new BIS zones to Zone V(B) and V(C) respectively.     

Macro-zonation of landslide susceptibility in Garamaura-Swarghat-Gambhar section of national highway 21, Bilaspur District, Himachal Pradesh (India)

Landslides the most common geo-hazard in hilly terrain are short lived phenomena but cause extraordinary landscape changes and destruction of life and property. The frequency and intensity of landslides occurrences along NH-21 during the rainy season not only disrupts traffic movement but also misbalance the agro-economic and developmental activities of the region frittering away thousand crores of rupees from the exchequer. An assessment of landslide susceptibility is, therefore, a prerequisite for sustainable development of the region. The present study deals with the preparation of macro-zonation maps of landslide susceptibility in an area of about 100 sq km on 1:50,000 scale across Garamaura-Swarghat section of National Highway-21. The map has been prepared by superimposing the terrain evaluation maps in a particular zone such as lithological map, structural map, slope morphometry map, relative relief map, land use and land cover map and hydrological condition map using landslide susceptibility evaluation factor rating scheme and calculating the total estimated susceptibility as per the guidelines of IS: 14496 (Part-2) 1998). Numerical weightages are assigned to the prime causative factors of slope instability such as lithology, structure, slope morphometery, relative relief, land use and groundwater conditions as per the scheme approved by Bureau of Indian Standard for the purpose of landslide susceptibility zonation. The area depicts zones of different instability. The identified susceptibility zones compared with landslide intensity in the area show some congruence with the weightages of the inputs. The incongruence in intensity and frequency of landslide occurrences and the inferred susceptibility zones of BIS scheme allow other geotechnical considerations and causative factors to be incorporated for the landslide susceptibility zonation.     

Application of logistic regression model for slope instability prediction in Cuyahoga River Watershed,Ohio, USA

High incidences of slope movement are observed throughout Cuyahoga River watershed in northeast Ohio, USA. The major type of slope failure involves rotational movement in steep stream walls where erosion of the banks creates over-steepened slopes. The occurrence of landslides in the area depends on a complex interaction of natural as well as human induced factors, including: rock and soil strength, slope geometry, permeability, precipitation, presence of old landslides, proximity to streams and flood-prone areas, land use patterns, excavation of lower slopes and/or increasing the load on upper slopes, alteration of surface and subsurface drainage. These factors were used to evaluate the landslide-induced hazard in Cuyahoga River watershed using logistic regression analysis, and a landslide susceptibility map was produced in ArcGIS. The map classified land into four categories of landslide susceptibility: low, moderate, high, and very high. The susceptibility map was validated using known landslide locations within the watershed area. The landslide susceptibility map produced by the logistic regression model can be efficiently used to monitor potential landslide-related problems, and, in turn, can help to reduce hazards associated with landslides.     

Landslide susceptibility mapping using LiDAR and DMC data: a case study in the Three Gorges area, China

The objective of this study is to map landslide susceptibility in Zigui segment of the Yangtze Three Gorges area that is known as one of the most landslide-prone areas in China by using data from light detection and ranging (LiDAR) and digital mapping camera (DMC). The likelihood ratio (LR) and logistic regression model (LRM) were used in this study. The work is divided into three phases. The first phase consists of data processing and analysis. In this phase, LiDAR and DMC data and geological maps were processed, and the landslide-controlling factors were derived such as landslide density, digital elevation model (DEM), slope angle, aspect, lithology, land use and distance from drainage. Among these, the landslide inventories, land use and drainage were constructed with both LiDAR and DMC data; DEM, slope angle and aspect were constructed with LiDAR data; lithology was taken from the 1:250,000 scale geological maps. The second phase is the logistic regression analysis. In this phase, the LR was applied to find the correlation between the landslide locations and the landslide-controlling factors, whereas the LRM was used to predict the occurrence of landslides based on six factors. To calculate the coefficients of LRM, 13,290,553 pixels was used, 29.5 % of the total pixels. The logical regression coefficients of landslide-controlling factors were obtained by logical regression analysis with SPSS 17.0 software. The accuracy of the LRM was 88.8 % on the whole. The third phase is landslide susceptibility mapping and verification. The mapping result was verified using the landslide location data, and 64.4 % landslide pixels distributed in “extremely high” zone and “high” zone; in addition, verification was performed using a success rate curve. The verification result show clearly that landslide susceptibility zones were in close agreement with actual landslide areas in the field. It is also shown that the factors that were applied in this study are appropriate; lithology, elevation and distance from drainage are primary factors for the landslide susceptibility mapping in the area, while slope angle, aspect and land use are secondary.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

逻辑回归与支持向量机模型在滑坡敏感性评价中的应用

白龙江流域是我国滑坡泥石流灾害四大高发区之一,进行该区域滑坡敏感性评价,能够为决策者在灾害管理和设施建设规划方面提供帮助,对区域防灾减灾具有重要指导意义。本研究采用边坡单元为基本研究单元,在野外调查及前人研究基础上,选择控制该区域滑坡发育的19个要素作为影响因子; 经过主成分分析和独立性检验得到该区域对滑坡形成贡献最大的6个因子:高程、坡度、坡向、岩性、断裂距离和人口密度; 分别使用二元逻辑回归模型（LR）和支持向量机模型（SVM）对该区域进行滑坡敏感性评价; 最后,采用ROC曲线对模型精度进行验证。研究结果表明,两模型各能将38.76%、14.48%、9.40%、11.28%、26.07%和13.49%、21.61%、8.17%、26.70%、30.04%的边坡单元分别预测为极高危险区、高危险区、中度危险区、低危险区和极低危险区; 精度验证结果表明两种模型均能有效地进行该区域滑坡敏感性评价,并且支持向量机模型具有更好的分类能力、预测精度和稳定性。     

Landslide susceptibility analysis using probabilistic likelihood ratio model—a geospatial-based study

The crucial and difficult task in landslide susceptibility analysis is estimating the probability of occurrence of future landslides in a study area under a specific set of geomorphic and topographic conditions. This task is addressed with a data-driven probabilistic model using likelihood ratio or frequency ratio and is applied to assess the occurrence of landslides in the Tevankarai Ar sub-watershed, Kodaikkanal, South India. The landslides in the study area are triggered by heavy rainfall. Landslide-related factors—relief, slope, aspect, plan curvature, profile curvature, land use, soil, and topographic wetness index proximity to roads and proximity to lineaments—are considered for the study. A geospatial database of the related landslide factors is constructed using Arcmap in GIS environment. Landslide inventory of the area is produced by detailed field investigation and analysis of the topographical maps. The results are validated using temporal data of known landslide locations. The area under the curve shows that the accuracy of the model is 85.83%. In the reclassified final landslide susceptibility map, 14.48% of the area is critical in nature, falling under the very high hazard zone, and 67.86% of the total validation dataset landslides fall in this zone. This landslide susceptibility map is a vital tool for town planning, land use, and land cover planning and to reduce risks caused by landslides.     

Landslide susceptibility zonation of the Chamoli region, Garhwal Himalayas, using logistic regression model

A remote sensing and Geographic Information System-based study has been carried out for landslide susceptibility zonation in the Chamoli region, part of Garhwal Himalayas. Logistic regression has been applied to correlate the presence of landslides with independent physical factors including slope, aspect, relative relief, land use/cover, lithology, lineament, and drainage density. Coefficients of the categories of each factor have been obtained and used to assess the landslide probability value to ultimately categorize the area into various landslide susceptibility zones; very low, low, moderate, high, and very high. The results show that 71.13% of observed landslides fall in 21.96% of predicted very high and high susceptibility zone, which in fact should be the case. Furthermore, lineament first buffer category (0–500 m) and the east and south aspects are the most influential in causing landslides in the region.     

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area,China

Landslide susceptibility mapping is an indispensable prerequisite for landslide prevention and reduction. At present, research into landslide susceptibility mapping has begun to combine machine learning with remote sensing and geographic information system (GIS) techniques. The random forest model is a new integrated classification method, but its application to landslide susceptibility mapping remains limited. Landslides represent a serious threat to the lives and property of people living in the Zigui–Badong area in the Three Gorges region of China, as well as to the operation of the Three Gorges Reservoir. However, the geological structure of this region is complex, involving steep mountains and deep valleys. The purpose of the current study is to produce a landslide susceptibility map of the Zigui–Badong area using a random forest model, multisource data, GIS, and remote sensing data. In total, 300 pre-existing landslide locations were obtained from a landslide inventory map. These landslides were identified using visual interpretation of high-resolution remote sensing images, topographic and geologic data, and extensive field surveys. The occurrence of landslides is closely related to a series of environmental parameters. Topographic, geologic, Landsat-8 image, raining data, and seismic data were used as the primary data sources to extract the geo-environmental factors influencing landslides. Thirty-four layers of causative factors were prepared as predictor variables, which can mainly be categorized as topographic, geological, hydrological, land cover, and environmental trigger parameters. The random forest method is an ensemble classification technique that extends diversity among the classification trees by resampling the data with replacement and randomly changing the predictive variable sets during the different tree induction processes. A random forest model was adopted to calculate the quantitative relationships between the landslide-conditioning factors and the landslide inventory map and then generate a landslide susceptibility map. The analytical results were compared with known landslide locations in terms of area under the receiver operating characteristic curve. The random forest model has an area ratio of 86.10%. In contrast to the random forest (whole factors, WF), random forest (12 major factors, 12F), decision tree (WF), decision tree (12F), the final result shows that random forest (12F) has a higher prediction accuracy. Meanwhile, the random forest models have higher prediction accuracy than the decision tree model. Subsequently, the landslide susceptibility map was classified into five classes (very low, low, moderate, high, and very high). The results demonstrate that the random forest model achieved a reasonable accuracy in landslide susceptibility mapping. The landslide hazard zone information will be useful for general development planning and landslide risk management.     

Application of a RS- and GIS-based semi-quantitative approach (analytical hierarchy process – AHP) in landslide hazard risk assessment of the Shivkhola Watershed,Darjiling Himalaya

The present study deals with the application of analytical hierarchy process to prepare landslide hazard risk map of the Shivkhola Watershed applying remote sensing and geographic information system (GIS). Firstly, to integrate all the required thematic data layers and to prepare landslide susceptibility map, prioritised class rating value and prioritised factor rating value were obtained by developing couple-comparing matrix with a reasonable consistency and with the help of MATLAB software after Saaty. Three important risk factor/element maps, that is, weighted land use/land cover map, 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, a classification was incorporated on ARC GIS Platform to prepare landslide hazard risk map. 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 between landslide hazard risk area (%) and number of risk events (%) for each landslide hazard risk class. Finally, an accuracy assessment was also made 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.     

Mapping groundwater recharge potential using GIS approach in Darwha block

Groundwater is the most important source of water in meeting irrigation, drinking, and other needs in India. The assessment of the potential zone for its recharge is critical for sustainable usage, quality management, and food security. This study reports alternative mapping of the groundwater recharge potential of a selected block by including large-scale soil data. Thematic layers of soil, geomorphology, slope, land use land cover, topographical wetness index, and drainage density of Darwha block (District Yavatmal, Maharashtra, India) were generated and integrated in a geographic information system environment. The topographic maps, thematic maps, field data, and satellite image were processed, classified, and weighted using analytical hierarchical process for their contribution to groundwater recharge. The layers were integrated by weighted linear combination method in the GIS environment to generate four groundwater potential zones viz., “poor,” “poor to moderate,” “moderate to high,” and “high.” Based on the generated groundwater potential map, about 9830 ha (12%) of the study area was categorized as high potential for recharge, 25,558 ha (31%) as poor to moderate, 33,398 ha (40%) as moderate to high, and 12,565 ha (15%) as poor potential zone. The zonation corresponds well with the field data on greater well density (0.22/ha) and irrigated crop area (27%) in the high potential zone as against 0.02 wells/ha and only 6% irrigated area in the poor zone. The map is recommended for use in regulating groundwater development decisions and judicious expenditure on drilling new wells by farmers and the state authorities.     

A quantitative approach for improving the BIS (Indian) method of medium-scale landslide susceptibility

In India, the Bureau of Indian Standards (BIS) recommends a heuristic method for medium-scale (1:25,000/1:50,000) landslide susceptibility mapping. This is based on fixed ratings of geofactors, without the inclusion of landslide inventory information. In BIS method, the pre-defined ratings of geofactors are applied over diverse areas, irrespective of the terrain-specific spatial inter-dependence of geofactors and landslide types, which leads to rather moderate prediction. In this paper, we evaluate the effectiveness of the existing BIS method in Darjeeling Himalaya through a quantitative method adapting weights of evidence (WofE) modeling. The quantified spatial associations between specific geofactors for different landslide types and failure mechanisms that were generated, using this method showed improved prediction rates as compared to the BIS method of fixed ratings of geofactors. We therefore recommend adjusting the existing BIS guidelines by inclusions of weights, derived locally through quantitative spatial analysis of landslide inventories and geofactor maps.     

Study of regional monsoonal effects on landslide hazard zonation in Cameron Highlands, Malaysia

In general, landslides in Malaysia mostly occurred during northeast and southwest periods, two monsoonal systems that bring heavy rain. As the consequence, most landslide occurrences were induced by rainfall. This paper reports the effect of monsoonal-related geospatial data in landslide hazard modeling in Cameron Highlands, Malaysia, using Geographic Information System (GIS). Land surface temperature (LST) data was selected as the monsoonal rainfall footprints on the land surface. Four LST maps were derived from Landsat 7 thermal band acquired at peaks of dry and rainy seasons in 2001. The landslide factors chosen from topography map were slope, slope aspect, curvature, elevation, land use, proximity to road, and river/lake; while from geology map were lithology and proximity to lineament. Landslide characteristics were extracted by crossing between the landslide sites of Cameron Highlands and landslide factors. Using which, the weighting system was derived. Each landslide factors were divided into five subcategories. The highest weight values were assigned to those having the highest number of landslide occurrences. Weighted overlay was used as GIS operator to generate landslide hazard maps. GIS analysis was performed in two modes: (1) static mode, using all factors except LST data; (2) dynamic mode, using all factors including multi-temporal LST data. The effect of addition of LST maps was evaluated. The final landslide hazard maps were divided into five categories: very high risk, high risk, moderate, low risk, and very low risk. From verification process using landslide map, the landslide model can predict back about 13–16% very high risk sites and 70–93% of very high risk and high risk combined together. It was observed however that inclusion of LST maps does not necessarily increase the accuracy of the landslide model to predict landslide sites.