Open AccessBody Height Estimation In Irrigation Dams With Deep Learning Model
Author(s) -
Şükrü ĞAYA,
Gizem Şahin,
Ergin ĞAYA,
Ayfer KOYUNOĞLU,
Selami ŞAHİN,
Murat Canpolat
Publication year - 2022
Publication title -
mas journal of applied sciences :
Language(s) - English
Resource type - Journals
ISSN - 2757-5675
DOI - 10.52520/masjaps.226
Subject(s) - python (programming language) , artificial neural network , deep learning , artificial intelligence , hydrology (agriculture) , water level , computer science , machine learning , engineering , cartography , geography , geotechnical engineering , operating system
Dams are one of the most important constructions for our country. The body height of the dams is one of the important factors in the efficiency of the dams. Today, the body height of dams is calculated by engineers. The aim of our study is to calculate the dam height with the deep learning model of artificial intelligence. Modeling was coded with python software. Numpy pandas libraries were used for the analysis of dam data. Matplotlib and seaborn were employed to visualize the data. Sklearn, tensorflow and keras libraries were used for deep learning modeling. Dam data are limited to irrigation dams in Turkey. For data analysis, the altitude, height, volume, area, temperature and precipitation characteristics were taken into consideration. As a result of our study, the dam body height estimation was done by teaching the dam data to the machine through multi-layer artificial neural networks of the deep learning model. The deviation in the body height estimations was found to be higher due to the insufficient data.