ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis

Kiran, KG and Srinivas, VV (2021) Fuzzy Ensemble Clustering Approach to Address Regionalization Uncertainties in Flood Frequency Analysis. In: Water Resources Research, 57 (3).

[img] PDF
wat_res_res_57-03_2021.pdf - Published Version
Restricted to Registered users only

Download (2MB) | Request a copy
[img] Microsoft Word
2020wr028412-sup-0001-supporting information si-s01.docx - Published Supplemental Material
Restricted to Registered users only

Download (687kB) | Request a copy
Official URL: https://doi.org/10.1029/2020WR028412

Abstract

Various regionalization approaches are in use to delineate watersheds in an area into homogeneous groups/regions for regional frequency analysis of hydrologic extremes (floods, droughts). Each approach differs in the underlying assumptions and strategy for regionalization and yields regions that differ in composition. There is ambiguity in the choice of approaches, as none is established to be universally superior in yielding true regions that are unknown. This article proposes a novel fuzzy ensemble clustering (FEC) approach to deal with uncertainty in the composition of regions obtained using different regionalization approaches. It forms fuzzy meta-regions by integrating information on similarities in watershed groupings found in an ensemble of regions derived using several regionalization approaches. The FEC approach's potential to form effective regions (than those found in the ensemble) and their utility in regional flood frequency analysis to predict flood quantiles at ungauged sites is demonstrated through Monte Carlo simulation experiments and a case study on peninsular India. An ensemble of regions for use with FEC is formed using the region of influence approach, clustering approach based on Gaussian mixture model (GMM), and a hybrid approach which combines canonical correlation analysis with GMM based clustering. The FEC is shown to be effective in grouping similar watersheds even when the ensemble comprises regions having some improperly/wrongly grouped watersheds. © 2021. American Geophysical Union. All Rights Reserved.

Item Type: Journal Article
Publication: Water Resources Research
Publisher: Blackwell Publishing Ltd
Additional Information: The copyright for this article belongs to Blackwell Publishing Ltd
Keywords: Flood control; Gaussian distribution; Monte Carlo methods; Uncertainty analysis; Watersheds, Canonical correlation analysis; Flood frequency analysis; Gaussian Mixture Model; Integrating information; Region-of-influence approaches; Regional flood frequency analysis; Regional frequency analysis; Regionalization approaches, Floods, ensemble forecasting; extreme event; flood frequency; flooding; frequency analysis; fuzzy mathematics; Gaussian method; meta-analysis; regionalization; watershed, India
Department/Centre: Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research
Division of Mechanical Sciences > Civil Engineering
Date Deposited: 16 Jul 2021 11:02
Last Modified: 16 Jul 2021 11:02
URI: http://eprints.iisc.ac.in/id/eprint/68724

Actions (login required)

View Item View Item