River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques

Fatih Üneş,Martina Zelenakova,Yunus Ziya Kaya,Mustafa Çalışıcı,Bestami Taşar,František Vranay,Mustafa Demirci

doi:10.3390/w12092427

Abstract

Accurate determination of river flows and variations is used for the efficient use of water resources, the planning of construction of water structures, and preventing flood disasters. However, accurate flow prediction is related to a good understanding of the hydrological and meteorological characteristics of the river basin. In this study, flow in the river was estimated using Multi Linear Regression (MLR), Artificial Neural Network (ANN), M5 Decision Tree (M5T), Adaptive Neuro-Fuzzy Inference System (ANFIS), Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) models. The Stilwater River in the Sterling region of the USA was selected as the study area and the data obtained from this region were used. Daily rainfall, river flow, and water temperature data were used as input data in all models. In the paper, the performance of the methods is evaluated based on the statistical approach. The results obtained from the generated models were compared with the recorded values. The correlation coefficient (R), Mean Square Error (MSE), and Mean Absolute Error (MAE) statistics are computed separately for each model. According to the comparison criteria, as a final result, it is considered that Mamdani-Fuzzy Logic (M-FL) and Simple Membership Functions and Fuzzy Rules Generation Technique (SMRGT) model have better performance in river flow estimation than the other models.

Highlights

Accurate prediction of the relationship between rainfall-runoff on a drainage basin, prediction of river flows, and changes are used for the efficient use of water resources, planning of the construction of water structures and prevention of flood disasters
The flow in to thethe river was estimated by using artificial intelligence techniques and Multi Linear Regression methods
When we look at the Mean Square Error (MSE) and Mean Absolute Error (MAE) criteria, and they have have similar similar low low error error rates

Summary

Introduction

Accurate prediction of the relationship between rainfall-runoff on a drainage basin, prediction of river flows, and changes are used for the efficient use of water resources, planning of the construction of water structures and prevention of flood disasters. The correct flow forecasts, hydrological and meteorological characteristics of the river basin are related to better understanding. This estimate can be made for a short period of time, such as a single stormy period, or to cover long periods such as monthly or yearly. Changes in local and regional characteristics make it difficult to determine the relationship between precipitation and flow. In the field of hydrology and water resources, artificial neural networks (ANN), which are one of the black box modeling methods, have been used as a suitable alternative for modeling the precipitation flow relationship. Hsu et al [1] used

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Conclusion