Prediction Interval Estimation Methods for Artificial Neural Network (ANN)-Based Modeling of the Hydro-Climatic Processes, a Review

Vahid Nourani, Nardin Jabbarian Paknezhad and Hitoshi Tanaka
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Vahid Nourani: Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz 51368, Iran
Nardin Jabbarian Paknezhad: Center of Excellence in Hydroinformatics and Faculty of Civil Engineering, University of Tabriz, Tabriz 51368, Iran
Hitoshi Tanaka: Department of Civil Engineering, Tohoku University, 6-6-06 Aoba, Sendai 980-8579, Japan

Sustainability, 2021, vol. 13, issue 4, 1-18

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.

Keywords: artificial neural network; uncertainty; sustainability; prediction intervals (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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