Browsing by Author "Ashwani Raju"

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A geostatistical approach to compare metal accumulation pattern by lichens in plain and mountainous regions of northern and central India
(Springer Science and Business Media Deutschland GmbH, 2022) Rajesh Bajpai; Vertika Shukla; Ashwani Raju; Chandra Prakash Singh; Dalip Kumar Upreti
Based on the physicochemical characteristics, metals emitted from the source (both natural and anthropogenic) contributes towards spatial continuity at a regional scale. Apart from the intrinsic properties of metals, meteorological conditions and topography of the region are also known to contribute towards spatial continuity. In the present study, a comparative spatial assessment of 12 metals in lichen Phaeophyscia hispidula collected from mountains and plains of northern and north-central India was carried out with the help of the indicator kriging method. The total metal concentration varies between 25.4–429 µgg−1 and 22.8–507 µgg−1 dry weight in plains and mountains, respectively. The ‘Indicator Kriging’, a cokriging non-parametric approach has been applied to predict the total metal load (TML) probability from a regional lichen database derived from the different metals in the mountain and plain regions. Cr, Cd, Cu and Pb had higher concentrations having higher coverage area, while metals like Cd and Hg had the highest localized distribution indicating point sources. The probability values of TML are further related with topography, population density and land cover attributes to specific factors responsible for metal accumulation in the study area. Observations indicated that apart from local sources, topography, population density and land cover, also plays an essential role in the spatial behaviour of the metals, which has been verified by the bioaccumulation pattern of metals in lichen samples from the mountainous region. Among which three mountainous states of Northern India, Uttarakhand has a higher concentration of metals which may be attributed to the topography and local anthropogenic sources. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
A Novel Deep Learning-based Landsat 7 ETM+ Multi-Spectral to Hyperspectral Reconstruction Model: Application for Water Bodies in an Indian Region
(Institute of Electrical and Electronics Engineers Inc., 2024) Saraah Imran; Subhojit Mandal; Ajanta Goswami; Mainak Thakur; Ashwani Raju
Hyperspectral (HS) remote sensing has the capacity to provide finer spectral information and better identification of objects while multispectral (MS) data are more readily available but with fewer bands. In absence of HS data, spectral reconstruction from MS to HS data can be considered in order to enhance the applicability of HS data. In this study, a deep learning-based Multi-Head Attention enabled Multi-Layer Perceptron (MHA-MLP) model is developed to reconstruct a scene of EO-1 Hyperion (HS) from a Landsat 7 ETM+ (MS) image. The reconstruction is also done using Multi-Layer Perceptron (MLP) and Transformer models. The reconstructed data from the three models is studied for water body locations in Betul, Madhya Pradesh, India. The results are analysed by comparative study of the spectra and calculation of standard statistical metrics. The reconstructed spectra from the MHA-MLP are found to follow the original HS spectra more closely than the other models and show the best values in the statistical metrics. Hence, The MHA-MLP model is found to be the best HS reconstructor model when compared to MLP and Transformer models. The reconstructed spectra are capable of capturing the 670 nm notch important for the study of chlorophyll concentration. This model can be used for water quality assessment applications and can also be extended for other applications. © 2024 IEEE.
A synergetic approach for quantification and analysis of coal fires in Jharia Coalfield, India
(Elsevier Ltd, 2023) Ashwani Raju; Anjali Singh; Surendra Kumar Chandniha
Temporal monitoring and understanding of the dynamics of coal fires in the Jharia Coalfield (JCF) are required to reduce its effect on sustainable industrial growth, environment & human safety. This research explores temporal dataset of Landsat 8 OLI Thermal Infrared Sensor (TIRS) from 2015 to 2019 to detect, map and quantify coal fire affected areas in JCF at the colliery level. The results indicated that the East Barora, Sijua, Katras, Kusunda, Kustore, Pootkee Balihari, Bastacolla, Jharia, and Lodna are intensely fire-affected collieries with a significant increase in risk area from 4.57 km2 in 2015 to 11.43 km2 in 2019. The central part of the area is highly affected. The extent of coal fire shows temporal fluctuation between 2015 and 2019, but overall exhibit a significant increase from 2.76 km2 to 7.52 km2. Sijua, Katras, Kusunda, Lodna, and Kustor occupying the central and southeastern parts of the JCF, respectively, constitute nearly ∼85% of the total fire. However, in comparison to the information inferred from the field-based knowledge, the results derived from satellite-based observations are slightly underestimated due to the reason that the coal fire-derived thermal anomalies are the function of depth, intensity and proportion of coal fire in a coarse resolution TIR pixel, structural attributes, interventions from the mining operation and regional land use planning. Further, the risk areas map out using the TIR-based approach have been integrated with the prevailing structural attributes and Landsat 8 OLI-derived surface thermal anomalies, which enabled an understanding of the dynamics of coal fire propagation in JCF. © 2023 Elsevier Ltd
Comprehensive assessment of subsidence in Eastern Gangatic plain region with relation to groundwater storage change and seasonal derivatives
(Institute of Electrical and Electronics Engineers Inc., 2024) Praveen Kumar Kannojiya; Ashwani Raju
Subsidence is a critical and emerging geo-hazard prominent in metropolitan hubs and coastal areas due to exponential unscientific extraction of groundwater and climate change. The Eastern Gangatic plains (EGP) has dynamic geomorphology stating with alluvial deposits from large streams and host prolific aquifer system. An exponential increase in population over the years in the region had created a metropolitan hub leading to over drafting of groundwater subsequently inducing subsidence in the urban region. With advancement in satellite microwave remote sensing in subsidence monitoring at global and regional scale has been quite efficacious. To understand the dynamics of the groundwater change with that of subsidence along with the seasonal influence, Satellite data analysis is carried out using Sentinal 1-A and GRACE in order to monitor the rate and evolutionary pattern of emerging land subsidence. This study explores the multi-temporal analysis of 192 Sentinel-1A SAR scenes acquired between February 2017 and August 2023 and GRACE data from 2003 to 2023. The SAR dataset were processed using Persistent Scatterer Interferometry (PSI), an advanced time series synthetic aperture radar technique (InSAR) to identify the potential subsidence hotspots. The potential area under extreme subsidence are classified into 13 blocks. The results shows the range of rate of subsidence to be -2.9 to 5.1 mm/yr. The mean cumulative subsidence is ~3 cm for the blocks which is in line with the declining groundwater storage trend. Results depicts that the unconfined aquifer is under significant stress and is experiencing a progressive loss of storage capacity over time, according to the discovered displacement trends, which also considerably match the city's ongoing decline in groundwater levels. © 2024 IEEE.
Correction to: Hydrogeochemical characterization of groundwater and their associated potential health risks (Environmental Science and Pollution Research, (2022), 30, 6, (14993-15008), 10.1007/s11356-022-23222-2)
(Springer Science and Business Media Deutschland GmbH, 2023) Anjali Singh; Ashwani Raju; Surendra Kumar Chandniha; Lipi Singh; Inderjeet Tyagi; Rama Rao Karri; Ajay Kumar
The correct affiliations for 5th,6th and 7th Author is presented in this paper. © 2022 Springer-Verlag GmbH Germany, part of Springer Nature.
Declining groundwater and its impacts along Ganga riverfronts using combined Sentinel-1, GRACE, water levels, and rainfall data
(Elsevier B.V., 2024) Ashwani Raju; Ramesh P. Singh; Praveen Kumar Kannojiya; Abhinav Patel; Saurabh Singh; Mitali Sinha
The Indo-Gangetic Plains (IGP) in northern India are vast alluvial tracts with huge shallow aquifers, densely populated and agriculturally productive regions. In the last few decades, IGP has been facing water scarcity driven by erratic monsoon dynamics, anthropogenic activity, and hydroclimatic variability. In urban centers, continuous groundwater withdrawal leads to high stress, affecting surface deformation and a threat to buildings and infrastructures. An attempt has been made to explore the possible linkage and coupling between groundwater level, hydroclimatic variables, and subsidence in the Central Ganga Plains (CGP), in Varanasi metropolis using the combined multisensory multitemporal data, Sentinel-1 (2017–2023), GRACE (2003–2023), groundwater levels (1998–2023), and precipitation (2002–2023). Long-term hydrological response in the CGP shows continuous depletion (14.6 ± 5.6 mm/yr) in response to precipitation variability. Results show spatiotemporal variations between GWS, and precipitation estimate with nonlinear trend response due to associated inter-annual/inter-seasonal climate variability and anthropogenic water withdrawal, specifically during the observed drought years. The significant storage response in the urban center compared to a regional extent suggests the potential impact of exponentially increasing urbanization and building hydrological stress in the cities. The implications of reducing storage capacity show measured land subsidence (∼2–8 mm/yr) patterns developed along the meandering stretch of the Ganga riverfronts in Varanasi. The groundwater level data from the piezometric supports the hydroclimatic variables and subsidence coupling. Considering the vital link between water storage, food security, and socioeconomic growth, the results of this study require systematic inclusion in water management strategies as climate change seriously impacts water resources in the future. © 2024
Detecting slow-moving landslides in parts of Darjeeling–Sikkim Himalaya, NE India: quantitative constraints from PSInSAR and its relation to the structural discontinuities
(Springer Science and Business Media Deutschland GmbH, 2022) Saurabh Singh; Ashwani Raju; Sayandeep Banerjee
The difficulty of monitoring slow-moving landslides is attributed to its highly dynamic spatial and temporal character, especially in a tectonic regime like the Himalayas, where the control of structural discontinuities to determine the risk at a local and regional scale is essential. Although many methods have been used for landslide monitoring in the past, the identification, measurement and categorization of landslide-induced relative surface displacements, its relevant relationship with relative slope and structural features, and forecasting remain elusive. The present work explores a holistic approach towards studying slow-moving landslides in and around the urban locales (Darjeeling, Kalimpong and Gangtok) of Darjeeling–Sikkim Himalaya. In order to establish an effective relationship, 109 successive C-band Sentinel-1A dataset acquired from February 2017 to October 2020 has been analysed using PSInSAR to identify the potential landslide risk zone in the selected blocks. Results indicate nearly 10.63% (~ 42km2) of the total Sikkim monitoring area (~ 395km2) is subjected to a 20-40 mm/yr of mean annual displacement rate and thus considered to be under the expression of potential risk. The contextual relationship between slope instabilities and regional thrusts/faults is also established. The derived time series displacement estimates are further integrated with corresponding slope estimates derived from the ALOS PALSAR DEM to determine the nature of Line of Sight (LOS) displacement followed by its classification into different categories and are further used in forecasting using ARIMA (1,1,1; 2,1,1) model. Results indicated that the area may experience cumulative displacement of 200-240 mm with a predicted 2.5–threefold increase between 2020 and 2023. © 2022, Springer-Verlag GmbH Germany, part of Springer Nature.
Geomorphological and mineralogical analysis of the lunar Robertson crater
(Elsevier Ltd, 2025) Ashwani Raju; Saraah Imran; Jiwantika Kumari; Ankit Kumar; Ramesh P. Singh
This study provides a comprehensive overview of the lunar Robertson crater of Copernican period located on the far side of the moon using multi-sensor satellite observations from combined Chandrayaan-I M3, LROC WAC Global Geomorphology and SELENE DTM mosaics. The analysis shows development of dynamic features, distribution of minerals, and topographic features during the crater formation. The crater preserves a complex geological evolution based on the mineralogical heterogeneity and distinct geomorphological features (such as accumulated melt flow at the crater floor, topographic undulations etc.) observed in a radial symmetry, which suggest formation through high energy impact processes. The detailed investigation of melt pool topography at the crater floor, highlights the formation and subsequent modifications of the transient cavity developed during simple to complex crater transition after the impact. The mineral species identified using the RELAB spectral library through the ‘spectral hourglass’ workflow show a distinct distribution, with Mg-spinel and olivine-rich lithologies concentrated in the central peak, while pyroxenes dominate the crater floor and surrounding rock rings. This pattern shows a complex mineral distribution, likely excavated from different depths as a result of the impact event. The dynamics of crater formation show a diameter respectively of 4.36 km and 5.73 km, assumed for chondrite and iron projectiles. Besides, CSFD measurements represent an absolute age of about 82 ± 4 Ma based on the 121 isochron fits to the differential data of post-impact craters that suggests recent resurfacing consistent with melt flow during the terminal stages of impact dynamics. © 2025 COSPAR
Health risk assessment from exposure to dissolved trace element concentration in drinking groundwater resources of Central Ganga Alluvial Plain: a case study of Lucknow region
(Taylor and Francis Ltd., 2022) Praveen Kumar Kannojiya; Ashwani Raju; Anjali Singh; Nupur Srivastava; Sandeep Singh; Munendra Singh
This study explores ‘Indicator Kriging’ approach for assessment of health risk from exposure to trace elements concentration (Formula presented.) in drinking water resources of the Central Ganga Alluvial Plain (CGAP), northern India. The estimates for (Formula presented.) were generated using analysis of groundwater samples (n = 100) collected from the Lucknow monitoring area to map the predicted area of health risk. The predicted probability maps have reclassified into a unified scale to generate Trace Element Risk Index (TERI), which has further integrated with human population count data to generate Health Risk Index of Lucknow. The results indicate that the risk is potentially alarming in urban areas as relatively high (Formula presented.) there are referring to the local (point) sources of contamination. Approximately 23.15% human population residing in about 69.77% of the total area is at moderate-to-high health risk probability. The findings of this study could help planning substantial remediation measures on long-term basis. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Hydrogeochemical characterization of groundwater and their associated potential health risks
(Springer Science and Business Media Deutschland GmbH, 2023) Anjali Singh; Ashwani Raju; Surendra Kumar Chandniha; Lipi Singh; Inderjeet Tyagi; Rama Rao Karri; Ajay Kumar
The present study assessed the human health risk exposure from the consumption of poor quality groundwater in the Lucknow area, a part of Central Ganga alluvial plain in India. Around 27 (n = 27) groundwater samples were collected from the study area. The analytical results of the samples (n = 27) collected indicate silicate and carbonate weathering is the dominant process along with cation exchange, sulfide oxidation, and reverse ion exchange. The type of groundwater is Ca2–Na–HCO3− type having all cations and anions within permissible WHO limits except for iron (Fe2+) and nitrate (NO3−). The high concentrations of Fe2 and NO3− in samples indicate the possibility of a non-geogenic point source for the same in an urban-influenced environment. The ionic concentration of dissolved constituents is used in weighted overlay analysis to generate the water quality index (WQI). WQI indicates that most urban areas (~ 98.52%) have fallen in the good to excellent category except few situated in the highly populated parts of Lucknow. The ionic concentrations of Fe2+ and NO3− have been further used to estimate human health risk by integrating regional urban population density data in Lucknow. The risk map shows alarming risks in the west-central part, where nearly ~ 35% of the total area is at moderate to high health risk. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Integrated Assessment of the Hydrogeochemical and Human Risks of Fluoride and Nitrate in Groundwater Using the RS-GIS Tool: Case Study of the Marginal Ganga Alluvial Plain, India
(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Dev Sen Gupta; Ashwani Raju; Abhinav Patel; Surendra Kumar Chandniha; Vaishnavi Sahu; Ankit Kumar; Amit Kumar; Rupesh Kumar; Samyah Salem Refadah
Groundwater contamination with sub-lethal dissolved contaminants poses significant health risks globally, especially in rural India, where access to safe drinking water remains a critical challenge. This study explores the hydrogeochemical characterization and associated health risks of groundwater from shallow aquifers in the Marginal Ganga Alluvial Plain (MGAP) of northern India. The groundwater chemistry is dominated by Ca-Mg-CO3 and Ca-Mg-Cl types, where there is dominance of silicate weathering and the ion-exchange processes are responsible for this solute composition in the groundwater. All the ionic species are within the permissible limits of the World Health Organization, except fluoride (F−) and nitrate (NO3−). Geochemical analysis using bivariate relationships and saturation plots attributes the occurrence of F− to geogenic sources, primarily the chemical weathering of granite-granodiorite, while NO3− contaminants are linked to anthropogenic inputs, such as nitrogen-rich fertilizers, in the absence of a large-scale urban environment. Multivariate statistical analyses, including hierarchical cluster analysis and factor analysis, confirm the predominance of geogenic controls, with NO3−-enriched samples derived from anthropogenic factors. The spatial distribution and probability predictions of F− and NO3− were generated using a non-parametric co-kriging technique approach, aiding in the delineation of contamination hotspots. The integration of the USEPA human health risk assessment methodology with the urbanization index has revealed critical findings, identifying approximately 23% of the study area as being at high risk. This comprehensive approach, which synergizes geospatial analysis and statistical methods, proves to be highly effective in delineating priority zones for health intervention. The results highlight the pressing need for targeted mitigation measures and the implementation of sustainable groundwater management practices at regional, national, and global levels. © 2024 by the authors.
Machine learning approach for detection of land subsidence induced by underground coal fire using multi-sensor satellite data
(Taylor and Francis Ltd., 2024) Ashwani Raju; Mansi Sinha; Saurabh Singh; Praveen Kumar Kannojiya; Mitali Sinha; Ramesh P. Singh
High-rank coal reserves in Jharia Coalfield (JCF, India), are invariably associated with underground coal fires and land subsidence. This study explores multi-sensor time series satellite data (Landsat 8 OLI and Sentinel-1) through machine learning (ML) to determine the regional ground deformation accompanying coal fires and their contextual relationship. The results show that the highest degree of subsidence is closely associated with the active mine benches with overburden dumps. The relationship between the coal fire and land subsidence parameters is considered as a binary classification problem, explored by calculating the probability of subsidence with a desirable categorical outcome through different ML models. The accuracy of the models is validated using performance metrics that shows that the Random Forest (RF) metrics predict the probability of deformation locations in response to the volume reduction of the burning coal fire and vertical compression due to Overburden Dump (OBD) near active mine benches. The estimated displacement trends have been used to forecast the Autoregressive Integrated Moving Average (ARIMA) method, estimated using Line-of-Sight (LOS) displacement values vary around the best fit within the 95% confidence limits. The trend shows ∼15–25% increase in subsidence compared to the cumulative subsidence. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Machine learning approach for detection of land subsidence induced by underground coal fire using multi-sensor satellite data
(Taylor and Francis Ltd., 2025) Ashwani Raju; Mansi Sinha; Saurabh Kumar Singh; Praveen Kumar Kannojiya; Mitali Sinha; Ramesh P. Singh
High-rank coal reserves in Jharia Coalfield (JCF, India), are invariably associated with underground coal fires and land subsidence. This study explores multi-sensor time series satellite data (Landsat 8 OLI and Sentinel-1) through machine learning (ML) to determine the regional ground deformation accompanying coal fires and their contextual relationship. The results show that the highest degree of subsidence is closely associated with the active mine benches with overburden dumps. The relationship between the coal fire and land subsidence parameters is considered as a binary classification problem, explored by calculating the probability of subsidence with a desirable categorical outcome through different ML models. The accuracy of the models is validated using performance metrics that shows that the Random Forest (RF) metrics predict the probability of deformation locations in response to the volume reduction of the burning coal fire and vertical compression due to Overburden Dump (OBD) near active mine benches. The estimated displacement trends have been used to forecast the Autoregressive Integrated Moving Average (ARIMA) method, estimated using Line-of-Sight (LOS) displacement values vary around the best fit within the 95% confidence limits. The trend shows ∼15–25% increase in subsidence compared to the cumulative subsidence. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Mapping human health risk by geostatistical method: a case study of mercury in drinking groundwater resource of the central ganga alluvial plain, northern India
(Springer International Publishing, 2019) Ashwani Raju; Anjali Singh; Nupur Srivastava; Sandeep Singh; Dharmendra Kumar Jigyasu; Munendra Singh
Human health is “at risk” from exposure to sub-lethal elemental occurrences at a local and or regional scale. This is of global concern as good-quality drinking water is a basic need for our wellbeing. In the present study, the “probability kriging,” a geostatistical method that has been used to predict the risk magnitude of the areas where the probability of dissolved mercury concentration (dHg) is higher than the World Health Organization (WHO) permissible limit. The method was applied to geochemical data of dHg concentration in 100 drinking groundwater samples of Lucknow monitoring area (1222 km2) located within the Ganga Alluvial Plain, India. Threefold (high to extreme risk) and twofold (moderate risk) higher dHg concentration values than the WHO permissible limit were observed in all of the groundwater samples. The generated prediction map using the probability kriging method shows that the probability of exceedance of dHg is the highest in the northwestern part of the Lucknow monitoring area due to anthropogenic interferences. The hotspots with high to very high probability are potentially alarming in the urban sector where 32.4% of the total population is residing in 6.8% of the total area. Interpolation of local estimates results in an easily readable and communicable human health risk map. It may help to consider substantial remediation measures for managing drinking water resources of the Ganga Alluvial Plain, which is among the anthropogenic mercury emission–dominated regions of the world. © 2019, Springer Nature Switzerland AG.
Multi-temporal analysis of groundwater depletion-induced land subsidence in Central Ganga Alluvial plain, Northern India
(Taylor and Francis Ltd., 2022) Ashwani Raju; Ritika Nanda; Anjali Singh; Kapil Malik
This study explores the Persistent Scatterer Interferometry technique to identify the developing pattern of groundwater depletion-induced land subsidence in Lucknow, northern India. The results show the development of two significant subsidence zones, one each in the north and south of Lucknow. The situation is potentially alarming as ∼9.3% (∼27.6 km2) of the total area is under the expression of a weak degree of subsidence (∼10–40 mm/yr). The detected displacement trends substantially correspond to the constant dwindling of groundwater levels in the city, further indicating that the first unconfined aquifer is under high stress and faces a continuous storage capacity loss with time. The time-series displacement trends have been later forecasted using the Autoregressive Integrated Moving Average method, indicating the city might face subsidence at the rate of ∼25–40 mm/yr. The findings of this study are significant to take precise mitigation measures in the areas facing continuous groundwater depletion on a long-term basis. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Predicting Change in Groundwater Storage Associated to Hydroclimatic Variability in the Indo-Gangetic Plains, India
(Institute of Electrical and Electronics Engineers Inc., 2024) Ashwani Raju; Ramesh P. Singh; Mitali Sinha
Despite numerous studies on dwindling groundwater resources in northern India, the linkage between Groundwater Storage (GWS) and hydroclimatic variability in the Indo-Gangetic Plain (IGP) has not been widely studied. In this regard, an attempt has been made to assimilate the satellite-based observations of hydroclimatic variables (predictors) (Precipitation- P, Evapotranspiration- ET, Soil Moisture-SM, and Runoff- RO) to predict GWS using multi-regression machine learning models (Random Forest; R2=0.81). Predicted estimates of the GWS show that northern and NW parts of the IGP are highly affected, and large residual estimates in the eastern parts are due to the uncertainty in the observed P and ET. Despite the performance of the models, spatial biases have been recognized between the observed and predicted estimates of the GWS, suggesting no significant variations among the predictors (hydroclimatic variables) in the major parts of the study area. © 2024 IEEE.
SBAS-InSAR analysis of regional ground deformation accompanying coal fires in Jharia Coalfield, India
(Taylor and Francis Ltd., 2023) Ashwani Raju; Kamran Mehdi
The present study explores a holistic approach toward better assimilation of the contextual relationship between coal fire-induced land subsidence in the Jharia Coalfield (JCF), India. For the process, 31 consecutive Sentinel-1A and Landsat 8 scenes of 2018 were processed to estimate mean Line-of-Sight displacement and Land Surface Temperature (LST) in JCF, respectively. The results indicated that the displacement rate in JCF significantly varies at active mine benches and overburden dump, and high degree of displacement owing to the additive compression inducted along with the volume reduction at the subsurface. The estimated displacement accounts were then spatially correlated with the thermally anomalous pixels to determine the categories of subsidence. Further, the contextual relationship between the displacements estimates (dependent variable) with a set of explanatory variables, i.e. pixel integrated LST was tested using Binary Logistic Regression. The performance of the model was cross-validated using statistical parameters derived from the confusion matrix. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.