Prediction interval estimation methods for artificial neural network (Ann)‐based modeling of the hydro‐climatic processes, a review

Vahid Nourani, Nardin Jabbarian Paknezhad, Hitoshi Tanaka

Research output: Contribution to journalReview articlepeer-review

Abstract

Despite the wide applications of artificial neural networks (ANNs) in modeling hydro-climatic processes, quantification of the ANNs’ performance is a significant matter. Sustainable management of water resources requires information about the amount of uncertainty involved in the modeling results, which is a guide for proper decision making. Therefore, in recent years, uncertainty analysis of ANN modeling has attracted noticeable attention. Prediction intervals (PIs) are one of the prevalent tools for uncertainty quantification. This review paper has focused on the different techniques of PI development in the field of hydrology and climatology modeling. The implementation of each method was discussed, and their pros and cons were investigated. In addition, some suggestions are provided for future studies. This review paper was prepared via PRISMA (preferred reporting items for systematic reviews and meta‐analyses) methodology.

Original languageEnglish
Article number1633
Pages (from-to)1-18
Number of pages18
JournalSustainability (Switzerland)
Volume13
Issue number4
DOIs
Publication statusPublished - 2021 Feb 2

Keywords

  • Artificial neural network
  • Prediction intervals
  • Sustainability
  • Uncertainty

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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