Integrating geospatial techniques and field survey to assess the changing nature of meander movements and meander geometry of Raidak-I River in the Himalayan foothills, West Bengal

Channel migration and resultant meander movements are the two important fluvial processes found in the riparian environment of a river basin. The present research explores the changing nature of the meander movements and meander geometry of the Raidak-I River in the Himalayan foothill region using geospatial tools. The study incorporated Landsat data (satellite imageries) for the years 1972, 1980, 1988, 2004, 2012 and 2021 and the whole study has been segmented into five periods i.e., 1972–1980, 1980–1988, 1988–2004, 2004–2012 and 2012–2021 to examine which type of meander movement dominates in the Raidak-I River within a particular time frame and how the nature of the meander movements is being changed over time. Bank lines of different periods have been superimposed with the help of the overlay analysis method in ArcGIS software (Version 10.8) to obtain the results. Furthermore, Arc-Extension tools have also been used to measure the meander geometry. Twelve active river bends have been identified to study meander geometry of sinuosity indices, meander length, meander width, meander-ratio, channel width and radius of curvature from 1972 to 2021. Initially, lateral movements predominated but, in the late-stage, rotational movement became much more prominent, which indicates dynamicity of the river channel in recent time. The cross-sectional study revealed that a convex bank has frequently been replaced with a concave bank and vice versa. The study finds human intervention – especially the construction of embankments – is the main reason behind such meander dynamics. The method we have used here is very simple, and thus can be considered for any part of the world and is very beneficial for identifying suitable sites for embankment construction, river restoration and channel management.Channel migration and resultant meander movements are the two important fluvial processes found in the riparian environment of a river basin. The present research explores the changing nature of the meander movements and meander geometry of the Raidak-I River in the Himalayan foothill region using geospatial tools. The study incorporated Landsat data (satellite imageries) for the years 1972, 1980, 1988, 2004, 2012 and 2021 and the whole study has been segmented into five periods i.e., 1972–1980, 1980–1988, 1988–2004, 2004–2012 and 2012–2021 to examine which type of meander movement dominates in the Raidak-I River within a particular time frame and how the nature of the meander movements is being changed over time. Bank lines of different periods have been superimposed with the help of the overlay analysis method in ArcGIS software (Version 10.8) to obtain the results. Furthermore, Arc-Extension tools have also been used to measure the meander geometry. Twelve active river bends have been identified to study meander geometry of sinuosity indices, meander length, meander width, meander-ratio, channel width and radius of curvature from 1972 to 2021. Initially, lateral movements predominated but, in the late-stage, rotational movement became much more prominent, which indicates dynamicity of the river channel in recent time. The cross-sectional study revealed that a convex bank has frequently been replaced with a concave bank and vice versa. The study finds human intervention – especially the construction of embankments – is the main reason behind such meander dynamics. The method we have used here is very simple, and thus can be considered for any part of the world and is very beneficial for identifying suitable sites for embankment construction, river restoration and channel management

Assessing Cohesion, Friction Angle and Slope Instability in the Shivkhola Watershed of Darjiling Himalaya

Abstract The study o

Pattern Synthesis in Time-Modulated Arrays Using Heuristic Approach

Time-modulation principle evolves as an emerging technology for easy realization of the desired array patterns with the help of an additional degree of freedom, namely, “time.” To the antenna community, the topic, time-modulated antenna array (TMAA) or 4D antenna arrays, has got much attention during the last two decades. However, population-based, stochastic, heuristic evolutionary algorithm plays as an important protagonist to meet the essential requirements on synthesizing the desired array patterns. This chapter is basically devoted to understand the theory of different time-modulation principles and the application of optimization techniques in solving different antenna array synthesis problems. As a first step, the theory of time-modulation principles and the behaviors of the sideband radiation (SBR) that appeared due to time modulation have been studied. Then, different important aspects associated with TMAA synthesis problems have been discussed. These include conflicting parameters, the need of evolutionary algorithms, multiple objectives and their optimization, cost function formation, and selection of weighting factors. After that, a novel approach to design a time modulator for synthesizing TMAAs is presented. Finally, discussing the working principle of an efficient heuristic approach, namely, artificial bee colony (ABC) algorithm, the effectiveness of the time modulator and potentiality of the algorithm are presented through representative numerical examples

An Empirical Modeling and Evaluation Approach for the Safe use of Industrial Electric Detonators in the Hazards of Radio Frequency Radiation

27-33The major causes of radio frequency radiation hazards are the transmitting antennas of radio, TV, radar, cell phones, wireless data acquisition systems and global positioning systems in the new age of communication technology using various modulation schemes such as amplitude modulation (AM), frequency modulation (FM) etc. The transmitting antennas of these communication devices generate electromagnetic fields (EMFs). Under such conditions, electric detonator wires work as receiving antenna and pickup sufficient energy from electromagnetic fields to initiate an accidental explosion. There have been several instances of accidental firing of detonators by radio frequency pickup. In this study an attempt has been made to minimize such explosions and to provide a basis for the assessment and simulation of the radio frequency radiation hazard parameters associated with industrial electric detonators. This research examines the radiated powers of various frequency bands to determine the safe distance from transmitting antenna. Two empirical relationships for the estimation of minimum safe distance (MSD) have been suggested based on mathematical simulation. Using these relations desired MSDs have been calculated for the relevant frequency bands. The values obtained have been compared with the experimental values available that demonstrated strong agreement between them. The average percentage deviations of calculated MSDs from suggested relations are found between 0.096% and 10.718%, with regression coefficient 0.970 ≤ R ≤ 1. This reflects the soundness of the proposed empirical relations. The blasting engineers, detonator designers and researchers may use these relations as a handy tool to prevent undesired explosions by maintaining minimum safe distance in radio frequency prone hazardous areas

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps (LSMs) using computer-based advanced machine learning techniques and compare the performance of the models. To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model (CNN) and three popular machine learning techniques, i.e., random forest model (RF), artificial neural network model (ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and non-landslide points. A ratio of 70:30 was considered for the selection of both training and validation points. Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve and statistical methods like root mean square error (RMSE) and mean absolute error (MAE). In both datasets (training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision

Modes of hill-slope failure under overburden loads: insights from physical and numerical models

Using sand model experiments this paper investigates failure mechanism of bed materials along hill slopes due to overburden loading. The analysis takes into account three factors: 1) surface slope (α), 2) loading pattern and 3) mechanical anisotropy of bed materials. In the experiments, external loading was simulated by moving down a rigid block extended either up to the brink of the slope (Type 1 loading) or beyond it (Type 2 loading). With progressive loading, the process of slope instability in sand models involves deformation localization in two modes: compaction (Mode 1) and shear failure (Mode 2). In isotropic models with Type 1 loading, Mode 1 deformation occurs in an elliptical region below the rigid block, which is flanked by a pair of shear bands of Mode 2. With increasing α (α > 45°), Mode 2 localization propagates rapidly towards the sloping surface leading to collapse on slopes. The failure zones also propagate in the vertical direction, and its depth of penetration appears to be inversely related to α. With Type 2 loading, Mode 2 localization becomes dominant, forming multiple shear bands, and Mode 1 deformation decreases with increasing α. In case of anisotropic bed materials, the orientation of the anisotropy plane relative to that of the surface slope determines the mode of deformation localization. With Type 2 loading, Mode 1 is the dominant process of deformation when the anisotropy plane dips same as the surface slope (α = 45°), whereas it is coupled with Mode 2 for the anisotropy planes dipping in the opposite directions. We complement the experimental findings with results from finite element models, and demonstrate the patterns of Mode 1 and Mode 2 localization considering the I1 (= σ1 + σ2 + σ3) stress invariant and Drucker-Prager yield criterion for elasto-plastic materials respectively