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Rabin Chakrabortty

Researcher at University of Burdwan

Publications -  92
Citations -  2531

Rabin Chakrabortty is an academic researcher from University of Burdwan. The author has contributed to research in topics: Environmental science & Geology. The author has an hindex of 18, co-authored 58 publications receiving 826 citations.

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Water-induced erosion potentiality and vulnerability assessment in Kangsabati river basin, eastern India

TL;DR: In this paper, an ensemble of EBF and spatial logistic regression (SLR) was used to estimate the erosion potentiality with the help of ArcGIS and Soil and water assessment tool (SWAT).
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Weather indicators and improving air quality in association with COVID-19 pandemic in India.

TL;DR: In this paper, the authors investigated the connection between COVID-19 pandemic-imposed lockdown and its relation to the present air quality in India; besides, relationship between climate variables and daily new affected cases of Coronavirus and mortality in India during the this period has also been examined.
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Evaluation of various boosting ensemble algorithms for predicting flood hazard susceptibility areas

TL;DR: To predict the areas affected by flood hazard in the Talar watershed, Mazandaran province, Iran, using Adaptive Boosting, Boosted Generalized Linear Models, and the novel ensemble framework of deep decision trees include the Deep Boosting (DB) model, the results showed that all used models have a good efficiency in predicting flood hazard.
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Impact of firecrackers burning and policy-practice gap on air quality in Delhi during Indian's great mythological event of Diwali festival

TL;DR: In this paper, the authors emphasized on Diwali firecrackers emissions and associated increasing atmospheric pollutants level and focused on pollution related various policies and policy-practice gaps to mitigate them accordingly for both local and international practice.
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Application of data-mining technique and hydro-chemical data for evaluating vulnerability of groundwater in Indo-Gangetic Plain.

TL;DR: In this paper , an integrated approach for delineating groundwater vulnerability using hydrochemical analysis and data-mining methods, i.e., Decision Tree (DT) and K-Nearest Neighbor (KNN) via k-fold cross-validation (CV) technique, was developed.