Late Cenozoic Himalayan foreland basin: Sedimentologic attributes

Kumar, Ram

doi:10.18814/epiiugs/2020/020026

Cited by 3 publications

(1 citation statement)

References 57 publications

(89 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The state of Bihar in India faces annual flooding incurring a loss of life, property, and agriculture (livestock included), in the tune of approximately ₹146,301.71 million (CWC, 2018). Previous studies have suggested that the Ganga River Basin (GRB) in the Himalayan Foreland Basin (HFB) is currently under active tectonic regime (Kumar, 2020). It is experiencing subsidence due to subsurface structural activities accentuating floods occurring due to various reasons (Shukla et al, 2012;Gupta et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

et al. 2021

View full text Add to dashboard Cite

This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

show abstract

Section: Introductionmentioning

confidence: 99%

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

et al. 2021

View full text Add to dashboard Cite

show abstract

Tectonics of the Himalaya

Jain

Banerjee

Kale

2020

Society of Earth Scientists Series

View full text Add to dashboard Cite

Carbonate clumped isotope thermometry of fault rocks and its possibilities: tectonic implications from calcites within Himalayan Frontal Fold-Thrust Belt

Sarkar

Ando

Kano

et al. 2021

Prog Earth Planet Sci

View full text Add to dashboard Cite

Disentangling the temperature and depth of formation of fault rocks is critical for understanding their rheology, exhumation, and the evolution of fault zones. Estimation of fault rock temperatures mostly relies on conventional geothermometers of metamorphic minerals and element partitioning analysis, which are largely inapplicable in shallow crustal fault rocks. Here, we demonstrate the applicability of the carbonate clumped isotope thermometer in low-grade carbonate-bearing fault rocks from the Himalayan frontal wedge (northwest India). Coalescing carbonate clumped isotope thermometry and calcite e-twin morphology allows us to constrain the temperature and depth of formation of the two main thrusts of the Himalayan frontal wedge, the Nahan thrust (170 ± 10 °C; 6–7 km depth), and the Main Boundary thrust (262 ± 30 °C; 10–11 km depth). The integration of the adopted analytical techniques can promote the application of calcite-based clumped isotope thermometry to the fault zone processes and refinement of shallow crustal fault zone models.

show abstract

Late Cenozoic Himalayan foreland basin: Sedimentologic attributes

Cited by 3 publications

References 57 publications

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

Tectonics of the Himalaya

Carbonate clumped isotope thermometry of fault rocks and its possibilities: tectonic implications from calcites within Himalayan Frontal Fold-Thrust Belt

Contact Info

Product

Resources

About