Correction to: Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling

Landslides - A Correction to this paper has been published: https://doi.org/10.1007/s10346-021-01646-0.     

Effect of time and space partitioning strategies of samples on regional landslide susceptibility modelling

Landslides - Assessment of the spatial probability of future landslide occurrences for disaster risk reduction is done through landslide susceptibility modelling. In this study, we investigated the...     

Geospatial landslide inventory of India—an insight into occurrence and exposure on a national scale

Landslides - India ranks first in the world in terms of fatal landslides. Large vulnerable area (0.42 million km2), high population density and monsoon rainfall make India’s landslide...     

Extenuating the parameters using HEC-HMS hydrological model for ungauged catchment in the central Omo-Gibe Basin of Ethiopia

Characteristics of ungauged catchments can be studied from the hydrological model parameters of gauged catchments. In this research, discharge prediction was carried out in ungauged catchments using HEC-HMS in the central Omo-Gibe basin. Linear regression, spatial proximity, area ratio, and sub-basin mean were amalgamated for regionalization. The regional model parameters of the gauged catchment and physical characteristics of ungauged catchments were collated together to develop the equations to predict discharge from ungauged catchments. From the sensitivity analysis, crop coefficient (CC), storage coefficient (R), constant rate (CR), and time of concentration (TC) are found to be more sensitive than others. The model efficiency was evaluated using Nash–Sutcliffe Efficiency (N_SE) which was greater than 0.75, varying between ?10% and +10% and the coefficient of determination (R²) was approximated to be 0.8 during the calibration and validation period. The model parameters in ungauged catchments were determined using the regional model (linear regression), sub-basin mean, area ratio, and spatial proximity methods, and the discharge was simulated using the HEC-HMS model. Linear regression was used in the prediction where p-value ≤ 0.1, determination coefficient (R²) = 0.91 for crop coefficient (CC) and 0.99 for maximum deficit (MD). Constant rate (CR), maximum storage (MS), initial storage (IS), storage coefficient (R), and time of concentration (TC) were obtained. The result is that an average of 30 m³/s and 15 m³/s as the maximum monthly simulated flow for ungauged sub-catchments, i.e. Denchiya and Mansa of the main river basin .     

Analysis on the effect of salinity in underwater wireless optical communication

Abstract

This article deals with the effect of salinity variation on underwater wireless optical communication (UWOC). Effect of different concentration of salt on underwater optical communication has been carried out experimentally in terms of received power at different link lengths. Based on the analysis of experimental data, a mathematical model has been proposed to describe the saline water channel. A simulation study is performed for different data rates and link lengths. It is seen that with increased salinity the attenuation is higher and the UWOC system performance degrades with higher data rate and increased link length.     

Unified numerical study of shallow foundation on structured soft clay under unconsolidated and consolidated-undrained loadings

Abstract

Based on a new elasto-plastic constitutive model, this paper presents a soil–water coupled numerical prediction of the bearing capacity for shallow foundation constructed on Ballina soft clay for unconsolidated undrained (UU) and consolidated undrained (CU) conditions. This elasto-plastic constitutive Shanghai model has an advantage of describing the mechanical behaviour of over-consolidated and structured soil under different loading and drainage conditions, by using one set of material parameter. In this paper, the Shanghai model used for both UU and CU conditions has the same initial parameters obtained from laboratory test results. The loading conditions and consolidation stages vary based on construction details. The predicted bearing pressure-settlement responses for UU and CU, approves the field observation. The phenomenon of gaining the bearing capacity due to consolidation is captured and explained by the use of soil–water coupled numerical analysis with a new elasto-plastic model. The stress strain behaviour, stress paths and the decay of the structure of elements at different depths presented in this study, reveal the mechanism for the difference between UU and CU conditions to understand the foundation behaviour. Effect of the initial degree of soil structure on the bearing capacity is also addressed. Overall, this approach provides the integrated solution for the shallow foundation design problems under short and long-term loadings.     

Assessment of Thermal Maturity,Source Rock Potential and Paleodepositional Environment of the Paleogene Lignites in Barsingsar,Bikaner–Nagaur Basin,Western Rajasthan,India

Natural Resources Research - Investigations on the Paleogene lignites were carried out through geochemical, mineralogical and petrographic analyses to assess their paleodepositional environment,...     

A Study on the Surface Wettability of Clastic Rocks with Potential Application for CO2 Storage Sites

Natural Resources Research - There have been many studies carried out in the past decades attempting to develop strategies for a safe injection of CO2 into storage sites without leakage and...     

The impact of northern Indian Ocean rivers on the Bay of Bengal using NEMO global ocean model

The effect of river runoff over the northern Indian Ocean(NIO) especially over the Bay of Bengal(Bo B) has been studied using global Nucleus for European Modelling of the Ocean(NEMO). Two sensitivity experiments, with and without river runoff are conducted and the influence of river runoff on the Indian Ocean hydrography,stratification and circulation features are studied. It is found that due to river runoff surface salinity over the northern Bo B decreases by more than 5 and the East India Coastal Current strengthens by 2 cm/s during post monsoon season. The fresh river water reaches up to 15°N in the Bo B and is the main cause for low salinity there.Sea surface temperature in the northwestern Bo B increases by more than 0.2℃ due to the river runoff in summer monsoon while surface cooling upto 0.2℃ is seen in north-west part of Bo B in winter season. The seasonal mixed layer depth in the region is found to be dependent on river runoff. The effect of vertical shear and Brunt Vaisala frequency on stratification is also examined. The ocean water becomes highly stratified up to 3 035 m due to the river runoff. It is found that the energy required for mixing is high in the northern and coastal Bo B.     

Application of soft computing techniques for shallow foundation reliability in geotechnical engineering

This research focuses on the application of three soft computing techniques including Minimax Probability Machine Regression(MPMR),Particle Swarm Optimization based Artificial Neural Network(ANN-PSO)and Particle Swarm Optimization based Adaptive Network Fuzzy Inference System(ANFIS-PSO)to study the shallow foundation reliability based on settlement criteria.Soil is a heterogeneous medium and the involvement of its attributes for geotechnical behaviour in soil-foundation system makes the prediction of settlement of shallow a complex engineering problem.This study explores the feasibility of soft computing techniques against the deterministic approach.The settlement of shallow foundation depends on the parametersγ(unit weight),e0(void ratio)and CC(compression index).These soil parameters are taken as input variables while the settlement of shallow foundation as output.To assess the performance of models,different performance indices i.e.RMSE,VAF,R^2,Bias Factor,MAPE,LMI,U(95),RSR,NS,RPD,etc.were used.From the analysis of results,it was found that MPMR model outperformed PSO-ANFIS and PSO-ANN.Therefore,MPMR can be used as a reliable soft computing technique for non-linear problems for settlement of shallow foundations on soils.