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Kazi Rifat Ahmed

Researcher at University of Zurich

Publications -  10
Citations -  450

Kazi Rifat Ahmed is an academic researcher from University of Zurich. The author has contributed to research in topics: Evapotranspiration & Water quality. The author has an hindex of 5, co-authored 10 publications receiving 332 citations. Previous affiliations of Kazi Rifat Ahmed include Khulna University.

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Flood risk of natural and embanked landscapes on the Ganges–Brahmaputra tidal delta plain

TL;DR: Controlled embankment breaches could reduce flood risk for the Ganges-Brahmaputra tidal delta plain as sea level rises as mentioned in this paper, which could reduce the risk of flooding.
Patent

Transportation management system and method for shipment planning optimization

TL;DR: In this paper, a system and method for planning transportation shipments for delivery and pickup of goods is proposed, which is capable of considering all possible locations through which goods can be moved by shipments.
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Analysis of landcover change in southwest Bengal delta due to floods by NDVI, NDWI and K-means cluster with landsat multi-spectral surface reflectance satellite data

TL;DR: In this article, the authors studied significant changes and alteration in landcovers due to floods events in May 2009 and found that NDVI and NDWI are prominent to identify vegetation and water covers considering their individual constrain, along with the validation by K-means clustering unsupervised and supervised land classifications.
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Possible factors for increasing water salinity in an embanked coastal island in the southwest Bengal Delta of Bangladesh

TL;DR: The study exposed four responding factors for increasing groundwater salinity in this region, which are - regional surface geological settings, hydrological settings, hydraulic head gradient, and human activities.
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A simple and robust wetland classification approach by using optical indices, unsupervised and supervised machine learning algorithms

TL;DR: This study introduced a simple, scalable, and robust wetland classification by applying unsupervised (K-means cluster – KMC) and supervised (Support vector machine classification – SVMc) ML algorithms.