An integrated approach to investigate saline water intrusion and to identify the salinity sources in the Central Godavari delta, Andhra Pradesh, India

The Central Godavari delta is located along the Bay of Bengal Coast, Andhra Pradesh, India, and is drained by Pikaleru, Kunavaram and Vasalatippa drains. There is no groundwater pumping for agriculture as wells as for domestic purpose due to the brackish nature of the groundwater at shallow depths. The groundwater table depths vary from 0.8 to 3.4 m and in the Ravva Onshore wells, 4.5 to 13.3 m. Electrical Resistivity Tomography (ERT) surveys were carried out at several locations in the delta to delineate the aquifer geometry and to identify saline water aquifer zones. Groundwater samples collected and analyzed for major ions for assessing the saline water intrusion and to identify the salinity origin in the delta region. The results derived from ERT indicated low resistivity values in the area, which can be attributed to the existence of thick marine clays from ground surface to 12–15 m below ground level near the coast and high resistivity values are due to the presence of coarse sand with freshwater away from the coast. The resistivity values similar to saline water <0.01 Ω m is attributed to the mixing of the saline water along surface water drains. In the Ravva Onshore Terminal low resistivity values indicated up coning of saline water and mixing of saline water from Pikaleru drain. The SO ₄ ^?2 /Cl^?and Na⁺²/Cl^?ratios did not indicate saline water intrusion and the salinity is due to marine palaeosalinity, dilution of marine clays and dissolution of evaporites.     

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...     

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.     

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.     

Evaluation of spatial probability of landslides using bivariate and multivariate approaches in the Goriganga valley,Kumaun Himalaya,India

Natural Hazards - In this study, new hybrid artificial neural network (ANN) models were used for predicting the groundwater resource index. The salp swarm algorithm (SSA), particle swarm...     

Discovery of lamproites near Vattikod Area, NW margin of the Cuddapah basin, Eastern Dharwar craton, southern India

A cluster of lamproite dykes are located 1 km west of Vattikod village at the NW margin of the Cuddapah basin, Eastern Dharwar craton, southern India, during the pursuit for locating primary diamond source rocks by adapting multifarious applications. These exotic rocks are emplaced along WNW-ESE to NW-SE trending fractures in the granitic rocks belonging to the Peninsular Gneissic Complex. Ten out of twelve lamproites occur near Vattikod village and one each is located in the vicinity of Marepalli and Gundrapalli villages respectively. These lamproites, though highly altered, contain microphenocrysts of altered olivine, clinopyroxene, phlogopite, leucite and sanidine and translucent to opaque, amoeboid shaped patches of glass set in a groundmass rich in carbonate, phlogopite, serpentine, and chlorite. This new cluster of lamproites constitutes a part of the recently discovered Ramadugu lamproite field. The Vattikod and Ramadugu lamproites, together with those from Krishna lamproite field and the Cuddapah basin, constitute, a wide spectrum of ultrapotassic magmatism emplaced in and around the Palaeo-Mesoproterozoic Cuddapah basin in southern India.     

Spatial evaluation of groundwater quality in Kazhakuttam block,Thiruvananthapuram district,Kerala

The groundwater in the study area generally shows an acidic trend as evidenced by the low pH values, particularly in lateritic aquifers. Spatial assessment indicates that the southern part of the coastal aquifers shows elevated pollution levels during post monsoon season. Most of the groundwater is of Na-Cl facies in post monsoon and which slightly changes to Na-Mg-Cl facies during pre monsoon. Inter relationship between chemical parameters were also studied to understand the groundwater facies. Salinity hazard, percentage of sodium, SAR, integrated SAR and EC and RSC, were considered for judging the usefulness of groundwater for irrigation. Landuse map was prepared and water sample locations were superimposed on it, in a GIS platform, to assess the variation in water quality in different landuse classes. The outcome of the present study indicates that the concentration of certain parameters exceeds the recommended limits and hence mitigation measures are needed to be adopted to ensure safe drinking water.     

Settlement of Shallow Foundations on Cohesionless Soils Based on SPT Value Using Multi-Objective Feature Selection

Accurate prediction of settlement for shallow footings on cohesionless soil is a complex geotechnical problem due to large uncertainties associated with soil. Prediction of the settlement of shallow footings on cohesionless soil is based on in situ tests as it is difficult to find out the properties of soil in the laboratory and standard penetration test (SPT) is the most often used in situ test. In data driven modelling, it is very difficult to choose the optimal input parameters, which will govern the model efficiency along with a better generalization. Feature subset selection involves minimization of both prediction error and the number of features, which are in general mutual conflicting objectives. In this study, a multi-objective optimization technique is used, where a non-dominated sorting genetic algorithm (NSGA II) is combined with a learning algorithm (neural network) to develop a prediction model based on SPT data based on the Pareto optimal front. Pareto optimal front gives the user freedom to choose a model in terms of accuracy and model complexity. It is also shown how NSGA II can be effectively applied to select the optimal parameters and besides minimizing the error rate. The developed model is compared with existing models in terms of different statistical criteria and found to be more efficient.     

Variations of trace gases over the Bay of Bengal during the summer monsoon

In situ measurements of near-surface ozone (\(\hbox {O}_{3})\), carbon monoxide (CO), and methane (\(\hbox {CH}_{4})\) were carried out over the Bay of Bengal (BoB) as a part of the Continental Tropical Convergence Zone (CTCZ) campaign during the summer monsoon season of 2009. \(\hbox {O}_{3}\), CO and \(\hbox {CH}_{4}\) mixing ratios varied in the ranges of 8–54 ppbv, 50–200 ppbv and 1.57–2.15 ppmv, respectively during 16 July–17 August 2009. The spatial distribution of mean tropospheric \(\hbox {O}_{3}\) from satellite retrievals is found to be similar to that in surface \(\hbox {O}_{3}\) observations, with higher levels over coastal and northern BoB as compared to central BoB. The comparison of in situ measurements with the Monitoring Atmospheric Composition & Climate (MACC) global reanalysis shows that MACC simulations reproduce the observations with small mean biases of 1.6 ppbv, –2.6 ppbv and 0.07 ppmv for \(\hbox {O}_{3}\), CO and \(\hbox {CH}_{4}\), respectively. The analysis of diurnal variation of \(\hbox {O}_{3}\) based on observations and the simulations from Weather Research and Forecasting coupled with Chemistry (WRF-Chem) at a stationary point over the BoB did not show a net photochemical build up during daytime. Satellite retrievals show limitations in capturing \(\hbox {CH}_{4}\) variations as measured by in situ sample analysis highlighting the need of more shipborne in situ measurements of trace gases over this region during monsoon.     

Accessing the capability of TRMM 3B42 V7 to simulate streamflow during extreme rain events: Case study for a Himalayan River Basin

The paper examines the quality of Tropical Rainfall Monitoring Mission (TRMM) 3B42 V7 precipitation product to simulate the streamflow using Soil Water Assessment Tool (SWAT) model for various rainfall intensities over the Himalayan region. The SWAT model has been set up for Gandak River Basin with 41 sub-basins and 420 HRUs. Five stream gauge locations are used to simulate the streamflow for a time span of 10 years (2000–2010). Daily streamflow for the simulation period is collected from Central Water Commission (CWC), India and Department of Hydrology and Meteorology (DHM), Nepal. The simulation results are found good in terms of Nash–Sutcliffe efficiency \((\hbox {NSE}) {>}0.65\), coefficient of determination \((R^{2}) {>}0.67\) and Percentage Bias \(\hbox {(PBIAS)}{<}15\%\), at each stream gauge sites. Thereafter, we have calculated the PBIAS and RMSE-observations standard deviation ratio (RSR) statistics between TRMM simulated and observed streamflow for various rainfall intensity classes, viz., light (\({<}7.5 \, \hbox {mm}/\hbox {d}\)), moderate (7.5 to 35.4 mm/d), heavy (35.5 to 124.4 mm/d) and extremely heavy (\({>}124.4 \, \hbox {mm}/\hbox {d}\)). The PBIAS and RSR show that TRMM simulated streamflow is suitable for moderate to heavy rainfall intensities. However, it does not perform well for light- and extremely-heavy rainfall intensities. The finding of the present work is useful for the problems related to water resources management, irrigation planning and hazard analysis over the Himalayan regions.