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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

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Related Experiment Video

Updated: Jun 22, 2026

Experimental Investigation of Secondary Flow Structures Downstream of a Model Type IV Stent Failure in a 180° Curved Artery Test Section

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Application of ANN and ANFIS models for reconstructing missing flow data.

Mohammad T Dastorani¹, Alireza Moghadamnia, Jamshid Piri

¹Faculty of Natural Resources, Yazd University, Yazd, Iran. mdastorani@yazduni.ac.ir

Environmental Monitoring and Assessment

|June 23, 2009

Summary

This study addresses missing river flow data in hydrological yearbooks using artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). ANFIS demonstrated superior performance in predicting flow data, especially for arid regions.

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Last Updated: Jun 22, 2026

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Area of Science:

Hydrology
Data Science
Environmental Engineering

Background:

Hydrological data gaps are common, particularly in developing nations.
Accurate flow data is crucial for water resource management, feasibility studies, and real-time decision-making.

Purpose of the Study:

To evaluate the effectiveness of Artificial Neural Networks (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) in filling missing river flow data.
To compare these advanced methods against traditional techniques like the normal ratio and correlation methods.

Main Methods:

Utilized neighboring station data to train and test ANN and ANFIS models.
Implemented normal ratio and correlation methods as baseline comparisons.
Assessed prediction accuracy for hydrological gauging stations.

Main Results:

ANFIS showed superior performance in predicting missing flow data, particularly in arid regions with variable data.
ANN also proved to be an efficient method for data imputation compared to traditional approaches.
All four methods provided acceptable predictions in certain scenarios.

Conclusions:

Advanced methods like ANFIS and ANN offer significant improvements for hydrological data gap filling.
ANFIS is particularly effective for heterogeneous and variable flow data common in arid environments.
These techniques enhance the reliability of hydrological assessments and water resource management.