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

研究成果: Review article査読

4 被引用数 (Scopus)

抄録

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.

本文言語English
論文番号1633
ページ(範囲)1-18
ページ数18
ジャーナルSustainability (Switzerland)
13
4
DOI
出版ステータスPublished - 2021 2 2

ASJC Scopus subject areas

  • 地理、計画および開発
  • 再生可能エネルギー、持続可能性、環境
  • 環境科学(その他)
  • エネルギー工学および電力技術
  • 管理、モニタリング、政策と法律

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