K
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.
Papers
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Journal ArticleDOI
Flood risk of natural and embanked landscapes on the Ganges–Brahmaputra tidal delta plain
Leslie Wallace Auerbach,Steven L. Goodbred,Dhiman R. Mondal,Carol A. Wilson,Kazi Rifat Ahmed,Kushal Roy,Michael S. Steckler,Christopher Small,Jonathan M. Gilligan,Brooke A. Ackerly +9 more
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
Francisco Jauffred,Kazi Rifat Ahmed,Alvatore Arminio,Harsh Desai,Johar Pervinder,Russell Mcgregor,Mark Pluta,Carl Wilson +7 more
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.
Journal ArticleDOI
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
Kazi Rifat Ahmed,Simu Akter +1 more
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.
Journal ArticleDOI
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.
Journal ArticleDOI
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.