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

Authors : Esra Aslı ÇUBUKÇU, Vahdettin DEMİR, Mehmet Faik SEVİMLİ

Pages : 200-211

Doi:10.26833/ijeg.1125412

View : 7 | Download : 9

Publication Date : 2023-07-05

Article Type : Research Paper

Abstract :The flow rate at which the instantaneous maximum flow is recorded throughout the year is called the Annual Maximum Flow insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(MANN);, Generalized Artificial Neural Networks insert ignore into journalissuearticles values(GANN);, Radial Based Artificial Neural Networks insert ignore into journalissuearticles values(RBANN); and Multiple Linear Regulation insert ignore into journalissuearticles values(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% insert ignore into journalissuearticles values(test); and 75% insert ignore into journalissuearticles values(training); packages. Root Means Square Error insert ignore into journalissuearticles values(RMSE);, Mean Absolute Error insert ignore into journalissuearticles values(MAE); and correlation coefficient insert ignore into journalissuearticles values(R); were used as the comparison criteria. The results are as follow; MANN insert ignore into journalissuearticles values(RMSE = 119.118, MAE = 93.213, R = 0.808);, and RBANN insert ignore into journalissuearticles values(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.  
Keywords : Modeling, Flood, Artificial Neural Networks, Annual Maximum Flow, Geographical Information Systems