Prediction Of Hydrological Models’ Uncertainty By A Committee Of Machine Learning-Models

Nagendra Kayastha, Dmitri Solomatine, D. Lal Shrestha

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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In the MLUE method (reported in Shrestha et al. [1, 2]) we run a hydrological model M for multiple realizations of parameters vectors (Monte Carlo simulations), and use this data to build a machine learning model V to predict uncertainty (quantiles) of the model M output. In this paper, for model V, we employ three machine learning techniques, namely, artificial neural networks, model tree, locally weighted regression which leads to several models results. We propose to use the simple averaging method (SA) and the weighted model averaging method (WMA) to form a committee of these models. These approaches are applied to estimate uncertainty of streamflows simulation in Bagmati catchment in Nepal. Tests on the different data sets show that WMA performs a bit better than SA.
Original languageEnglish
Title of host publicationProceedings of the HIC 2014 - 11th international conference on hydroinformatics
EditorsM Piasecki
Publication statusPublished - 2014
Event11th International Conference on Hydroinformatics - New York, United States
Duration: 17 Aug 201421 Aug 2014
Conference number: 11

Publication series



Conference11th International Conference on Hydroinformatics
Abbreviated titleHIC 2014
CountryUnited States
CityNew York
Internet address


  • uncertainty analysis
  • hydrological model
  • machine learning
  • MLUE
  • model averaging


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