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Kutub Uddin Eibek

Researcher at Begum Rokeya University

Publications -  6
Citations -  399

Kutub Uddin Eibek is an academic researcher from Begum Rokeya University. The author has contributed to research in topics: Ensemble forecasting & Flash flood. The author has an hindex of 5, co-authored 5 publications receiving 133 citations.

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Flood susceptibility modelling using advanced ensemble machine learning models

TL;DR: The methodology and solution-oriented results presented in this paper will assist the regional as well as local authorities and the policy-makers for mitigating the risks related to floods and also help in developing appropriate mitigation measures to avoid potential damages.
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Modeling fragmentation probability of land-use and land-cover using the bagging, random forest and random subspace in the Teesta River Basin, Bangladesh

TL;DR: In this article, the fragmentation probability of the Teesta River Basin (TRB) in Bangladesh was investigated using remote sensing data for assessing land-use and land-cover (LULC) changes, and the results showed that water bodies and barren land were substantially decreased by 6.21% and 14.59% respectively while the built-up areas increased by 1.45% from 2010 to 2019.
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Predicting spatiotemporal changes of channel morphology in the reach of Teesta River, Bangladesh using GIS and ARIMA modeling

TL;DR: In this paper, the authors investigate the spatiotemporal changes of channel morphology and predict mid-line channel shifting in the reach of the Teesta River, Bangladesh during 1972-2031, using multi-temporal Landsat images data with GIS, spatial autocorrelation index, and an autoregressive integrated moving average (ARIMA) model.
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A comprehensive statistical assessment of drought indices to monitor drought status in Bangladesh

TL;DR: In this paper, the authors used daily temperature and precipitation data from the Bangladesh Meteorological Department (BMD) to calculate Standardized Precipitation Index (SPI) and Standardized Preciation Evapotranspiration Index(SPEI), and performed a statistical assessment, for instance, Pearson correlation coefficient, crosscorrelation, cross-wavelet transform, and root mean square error, to identify the strengths of SPI and SPEI.
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Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh

TL;DR: In this paper, the authors explored the spatiotemporal dynamics of wetlands, prediction of wetland risk assessment, and showed that wetland areas at present are declining less than one-third of those in 1988 due to the construction of the dam at Farakka, which is situated at the upstream of the Padma River.