Modeling of annual maximum flows with geographic data components and artificial neural networks

The flow rate at which the instantaneous maximum flow is recorded throughout the year is called the Annual Maximum Flow (AMF). These flow rates often cause disasters such as floods. Snow melts and extreme precipitation associated with temperature fluctuations are the two most important factors that occurred flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of a flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. This study was used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012. Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. In these packages, the AMF data measured for the 5 stations mentioned above between 1965 and 2012 were divided into 4 and used by creating 25% (test) and 75% (training) packages. Root Means Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.

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Eser Adı
(dc.title)
Modeling of annual maximum flows with geographic data components and artificial neural networks
Yayın Türü
(dc.type)
Makale
Yazar/lar
(dc.contributor.author)
ÇUBUKÇU, Aslı
Yazar/lar
(dc.contributor.author)
DEMİR, Vahdettin
Yazar/lar
(dc.contributor.author)
SEVİMLİ, Mehmet Faik
Atıf Dizini
(dc.source.database)
Wos
Atıf Dizini
(dc.source.database)
Scopus
Konu Başlıkları
(dc.subject)
Modeling
Konu Başlıkları
(dc.subject)
Flood
Konu Başlıkları
(dc.subject)
Artificial Neural Networks
Konu Başlıkları
(dc.subject)
Annual Maximum Flow
Konu Başlıkları
(dc.subject)
Geographical Information Systems
Yayıncı
(dc.publisher)
International Journal of Engineering and Geosciences
Yayın Tarihi
(dc.date.issued)
2023
Kayıt Giriş Tarihi
(dc.date.accessioned)
2023-03-09T08:35:33Z
Açık Erişim tarihi
(dc.date.available)
2023-03-09T08:35:33Z
Atıf için Künye
(dc.identifier.citation)
Çubukçu, E. A., Demir, V., & Sevimli, M. F. (2023). Modeling of annual maximum flows with geographic data components and artificial neural networks. International Journal of Engineering and Geosciences, 8(2), 200-211
ISSN
(dc.identifier.issn)
2548-0960
Özet
(dc.description.abstract)
The flow rate at which the instantaneous maximum flow is recorded throughout the year is called the Annual Maximum Flow (AMF). These flow rates often cause disasters such as floods. Snow melts and extreme precipitation associated with temperature fluctuations are the two most important factors that occurred flooding. The deluge that follows kills people and destroys property in communities and agricultural lands. As a result, it's critical to predict the flow that causes flooding and take appropriate precautions to limit the damage. The prediction of the probability of a flood event in advance is very important for the safety of life and property of large masses and agricultural lands. Early warning systems, disaster management plans and minimizing these losses are among the important goals of the country's administration. This study was used in five Current Observation Stations (COS) located in Yeşilırmak Basin in Turkey. By using 8 input data including geographical location, altitude and area information of these stations, AMF data were tried to be estimated for each COS. A total of 240 input data was used in the study. The data period covers the years 1964-2012. Unfortunately, AMF values cannot be monitored for all 5 stations used after 2012. Therefore, the data period was stopped in 2012. In this study, Multilayer Artificial Neural Networks (MANN), Generalized Artificial Neural Networks (GANN), Radial Based Artificial Neural Networks (RBANN) and Multiple Linear Regulation (MLR) methods were used. Input data sets were made into 4 packets and these packages were used respectively in both training and testing stages. In these packages, the AMF data measured for the 5 stations mentioned above between 1965 and 2012 were divided into 4 and used by creating 25% (test) and 75% (training) packages. Root Means Square Error (RMSE), Mean Absolute Error (MAE) and correlation coefficient (R) were used as the comparison criteria. The results are as follow; MANN (RMSE = 119.118, MAE = 93.213, R = 0.808), and RBANN (RMSE = 111.559, MAE = 81.114, R = 0.900). These results show that AMF can be predicted with artificial intelligence techniques and can be used as an alternative method.
Yayın Dili
(dc.language.iso)
en
Haklar
(dc.rights)
Attribution 3.0 United States
Tek Biçim Adres
(dc.identifier.uri)
http://hdl.handle.net/20.500.12498/5942
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