Key morphological changes and their linkages with stream power and land-use changes in the Upper Tapi River basin,India

Knowledge on spatio-temporal variations in planform, hydraulic geometry, and bed-level variations of alluvial streams is required for planning and development of hydraulic structures and bank protection works. In the current study, a Geographic Information System (GIS) has been used to analyze topographical maps, multi-temporal remotely sensed imagery, and hydrologic and hydraulic data to extract the morphological parameters of the Upper Tapi River, India. The river has been found to have consistent migration towards the northern direction, with erosion/deposition on right/left banks. The river has not experienced any major meander except in the lower reaches of the Upper Tapi Gorge and minor braiding conditions at the location where the river emerges from mountainous topography to the plain region. The analyzed river cross sections were found to be depth dominated, and contain large flows within the channel banks. The cross-sections exhibited moderate channel bed adjustments in 1994, 2006, and 2007 wherein excessive sediment flux and stream power were capable of causing morphological changes in the river. High intensity rainfall in the subcatchment resulted in high sediment flux into the river during 1994, which was reported to cause significant aggradation at the downgauging station. The analysis of sediment flux into the river in conjunction with decadal land use land cover, revealed that sediment yield from the catchment was reduced during 2000–2010 due to an increase in water bodies in the form of minor hydraulic structures. The entry of comparatively less sediment laden water into the river, resulted in moderate bed degradation especially in 2006 and 2007 as observed at the downstream station. The methodology applied in the current study is generic in nature and can be applied to other rivers to identify their morphological issues.     

Landslide Susceptibility Mapping Using Remotely Sensed Data through Conditional Probability Analysis Using Seed Cell and Point Sampling Techniques

Rapid urbanization, intense infra-structure development and increased tourism related activities have resulted in the change of landscape of the Kodaikkanal town and its surrounding, a popular hill town in Tamilnadu, South India. As an after effect, the numbers of landslides and rock-falls have increased steadily in the past decade. Landslide susceptibility analysis is carried out for this area using conditional probability analysis. The geo-spatial database for mapping landslide susceptibility consists of the factors - Relief, Slope, Aspect, Curvature, Weathering, Land use, Topographic Wetness Index and Proximity to road. Two sampling strategies ?C point and seed-cell are compared for landslide susceptibility mapping. The Landslide Susceptibility map developed using conditional probability method is verified using R index for both sampling strategies. The study shows that both the sampling strategies perform with good accuracy, seed cell technique excels slightly over point sampling. 86.11% of the landslides fall in the high and critical susceptible zones. The results show that conditional probability technique provides a simple tool for susceptibility analysis. The method can be used at regional scale and is a valuable input for planning purpose.     

Retrieval of soil physicochemical properties towards assessing salt-affected soils using Hyperspectral Data

The relationship between soil salinity parameters and their influence on soil spectral characteristics were analyzed using both satellite data (Hyperion) and reflectance data of soil samples collected from parts of Ahmedabad district of Gujarat, India. The soil spectral reflectance curves were assessed using absorption feature parameters by DISPEC software to identify suitable spectral band for salinity characterization. The Hyperion data of the study area were processed and classified into different classes by spectral angle mapper algorithm using spectral library generated from soil spectra. The results showed that among all the observed soil parameters Electrical Conductivity, Exchangeable Sodium Percentage, Cation Exchange Capacity and Mg⁺⁺ predictions can be made accurately based on partial least square regression models developed from selected wavelengths. Out of the total study area moderately saline-sodic, severely saline-sodic, severely saline and slightly saline soils occupy 23.5, 12.6, 10.9 and 0.04%, respectively.     

Mapping debris flow susceptibility using analytical network process in Kodaikkanal Hills,Tamil Nadu (India)

Rapid debris flows, a mixture of unconsolidated sediments and water travelling at speeds > 10 m/s are the most destructive water related mass movements that affect hill and mountain regions. The predisposing factors setting the stage for the event are the availability of materials, type of materials, stream power, slope gradient, aspect and curvature, lithology, land use and land cover, lineament density, and drainage. Rainfall is the most common triggering factor that causes debris flow in the Palar subwatershed and seismicity is not considered as it is a stable continental region and moderate seismic zone. Also, there are no records of major seismic activities in the past. In this study, one of the less explored heuristic methods known as the analytical network process (ANP) is used to map the spatial propensity of debris flow. This method is based on top-down decision model and is a multi-criteria, decision-making tool that translates subjective assessment of relative importance to weights or scores and is implemented in the Palar subwatershed which is part of the Western Ghats in southern India. The results suggest that the factors influencing debris flow susceptibility in this region are the availability of material on the slope, peak flow, gradient of the slope, land use and land cover, and proximity to streams. Among all, peak discharge is identified as the chief factor causing debris flow. The use of micro-scale watersheds demonstrated in this study to develop the susceptibility map can be very effective for local level planning and land management.     

Rock Slope Stability Assessment Using Geomechanical Classification and its Application for Specific Slopes along Kodaikkanal-Palani Hill Road,Western Ghats,India

Linear infrastructure networks like roads play a vital role in the socio-economic development of hill towns centered on tourism. Stability of the slopes along the hill roads are therefore a major concern and slope failures lead to disruption of traffic and loss of property/life or both. This study analyses the stability of cut-slopes along the Kodaikkanal – Palani hill road in the Western Ghats, India using rock mass classification systems like rock mass rating (RMR), slope mass rating (SMR) and continuous slope mass rating (CSMR). These geomechanical classifications provide a preliminary assessment of rock quality based on rock strength, discontinuity properties, hydrogeological condition of the slopes and slope stability based on the inherent rock strength parameters, discontinuity orientation and method of excavation. The results showed that both rock quality and discontinuity orientation contribute to type of failure in rock slopes with RMR > 40. SMR results are conservative while CSMR classification is matches more closely to the failures obtained from the field survey. CSMR classification represents continuous slope stability conditions and hence are more suitable for development of spatial database. Cutting of roads, thereby, steepening slopes has a definite influence on the stability of slopes.     

Methods for matching English language addresses

Addresses occupy a niche location within the landscape of textual data, due to the positional importance carried by every word, and the geographic scope it refers to. The task of matching addresses happens every day and is present in various fields such as mail redirection, entity resolution, etc. Our work defines, and formalizes a framework to generate matching and mismatching pairs of addresses in the English language, and use it to evaluate various methods to automatically perform address matching. These methods vary widely from distance-based approaches to deep learning models. By studying the Precision, Recall, and Accuracy metrics of these approaches, we obtain an understanding of the best suited method for this setting of the address matching task.