River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques
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 charact...
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doaj-ef92ab81a14f43fcbd0beeea35c442432020-11-25T03:57:25ZengMDPI AGWater2073-44412020-08-01122427242710.3390/w12092427River Flow Estimation Using Artificial Intelligence and Fuzzy TechniquesFatih Üneş0Mustafa Demirci1Martina Zelenakova2Mustafa Çalışıcı3Bestami Taşar4František Vranay5Yunus Ziya Kaya6Department of Civil Engineering, Iskenderun Technical University, İskenderun 31200, TurkeyDepartment of Civil Engineering, Iskenderun Technical University, İskenderun 31200, TurkeyDepartment of Environmental Engineering, Technical University of Kosice, Vysokoškolská 4, 040 01 Košice, SlovakiaDepartment of Civil Engineering, Iskenderun Technical University, İskenderun 31200, TurkeyDepartment of Civil Engineering, Iskenderun Technical University, İskenderun 31200, TurkeyDepartment of Building Construction, Technical University of Kosice, 04200 Kosice, SlovakiaDepartment of Civil Engineering, Osmaniye Korkut Ata University, Osmaniye 80100, TurkeyAccurate 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.https://www.mdpi.com/2073-4441/12/9/2427artificial neural networkriver flowfuzzy logicM5 decision treepredictionSMRGT |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Fatih Üneş Mustafa Demirci Martina Zelenakova Mustafa Çalışıcı Bestami Taşar František Vranay Yunus Ziya Kaya |
spellingShingle |
Fatih Üneş Mustafa Demirci Martina Zelenakova Mustafa Çalışıcı Bestami Taşar František Vranay Yunus Ziya Kaya River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques Water artificial neural network river flow fuzzy logic M5 decision tree prediction SMRGT |
author_facet |
Fatih Üneş Mustafa Demirci Martina Zelenakova Mustafa Çalışıcı Bestami Taşar František Vranay Yunus Ziya Kaya |
author_sort |
Fatih Üneş |
title |
River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques |
title_short |
River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques |
title_full |
River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques |
title_fullStr |
River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques |
title_full_unstemmed |
River Flow Estimation Using Artificial Intelligence and Fuzzy Techniques |
title_sort |
river flow estimation using artificial intelligence and fuzzy techniques |
publisher |
MDPI AG |
series |
Water |
issn |
2073-4441 |
publishDate |
2020-08-01 |
description |
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. |
topic |
artificial neural network river flow fuzzy logic M5 decision tree prediction SMRGT |
url |
https://www.mdpi.com/2073-4441/12/9/2427 |
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