Distributional Regression Forests Approach to Regional Frequency Analysis With Partial Duration Series

Kiran, KG and Srinivas, VV (2021) Distributional Regression Forests Approach to Regional Frequency Analysis With Partial Duration Series. In: Water Resources Research, 57 (10).

PDF wat_res_res_57-10_2021.pdf - Published Version Restricted to Registered users only Download (4MB) | Request a copy

Abstract

Regional flood frequency analysis (RFFA) is widely used to quantify flood risk at ungauged and sparsely gauged locations. There are minimal attempts to use partial duration series (PDS) for RFFA, though the use of PDS instead of widely used annual maximum series (AMS) can offer some advantages. This article contributes two novel random/regression forests (RFs)-based methodologies, namely generalized pareto distribution (GPD)-based distributional RFs (DRFs) and multivariate RFs (MVRFs), for RFFA with PDS. The RFs facilitate modeling interactions between predictors and their complex relationships with the predictands without explicitly specifying them. The DRFs and MVRFs comprise an ensemble of corresponding regression trees, each constructed by recursive binary partitioning of the feature space into meaningful segments. The proposed DRFs account for the sampling uncertainty of PDS in the partitioning and parameter estimation. In DRFs (MVRFs), quantile estimates for an ungauged site are obtained using maximum likelihood estimates (expected values) of GPD parameters corresponding to the segments to which the site belongs. The potential of DRFs and MVRFs relative to two recently proposed techniques (univariate RFs-based quantile regression, generalized additive model based on GPD) is demonstrated through Monte-Carlo simulation experiments and a study on 1,031 watersheds in the United States. The key features influencing scale and shape parameters of GPD fitted to PDS of the watersheds are identified as drainage area and 24-hr rainfall intensity corresponding to 2-year return period, respectively. Those identified for shape parameter differ from key features known based on analysis with AMS and generalized extreme value distribution. © 2021. American Geophysical Union. All Rights Reserved.

Item Type:	Journal Article
Publication:	Water Resources Research
Publisher:	John Wiley and Sons Inc
Additional Information:	The copyright for this article belongs to John Wiley and Sons Inc
Keywords:	Additives; Binary trees; Flood control; Floods; Intelligent systems; Maximum likelihood estimation; Monte Carlo methods; Parameter estimation; Pareto principle; Regression analysis; Risk assessment; Watersheds, Annual maximum series; Distributional random forest; Generalized additive model; Generalized Pareto Distributions; Key feature; Multivariate random forest; Partial duration series; Regional flood frequency analysis; Regional frequency analysis; Regression forests, Decision trees
Department/Centre:	Division of Interdisciplinary Sciences > Interdisciplinary Centre for Water Research Division of Mechanical Sciences > Civil Engineering
Date Deposited:	25 Nov 2021 04:45
Last Modified:	25 Nov 2021 04:45
URI:	http://eprints.iisc.ac.in/id/eprint/70498

Actions (login required)

View Item