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YAPAY SİNİR AĞLARI İLE DİBİS BARAJI’NIN SEVİYE TAHMİNİ

Year 2018, Volume: 6 Issue: 4, 564 - 569, 11.12.2018

Abstract

Suya olan talebin giderek artması su kaynaklarının optimum bir
şekilde kullanılmasını gerekli hale getirmiştir. Bu çalışmada Irak Kerkük’ün
kuzeybatısında bulunan Dibis barajının su seviyesi tahmin edilmeye
çalışılmıştır. Çalışmada Dibis barajına giren akım değerleri, barajdan çıkan
akım değerleri, yağış değerleri ve başlangıç su seviyesi modellerin tahmininde
çeşitli kombinasyonlar şeklinde kullanılmıştır. Bu çalışmada dört farklı model
kullanılmıştır. Modeller yağışlı/yağışsız, başlangıç su seviyeli/başlangıç su
seviyesiz olarak oluşturulmuştur. Çalışmada başlangıç su seviyesinin en önemli
parametre olduğu sonucuna ulaşılmıştır. Diğer parametrelerden yağışında önemli
olduğu ancak doğruluk payını yaklaşık olarak % 1 değiştirdiği tespit
edilmiştir. Başlangıç su seviyesine ek olarak bir önceki günün su seviyesinin
modellere konulmasının da sadece % 2’lik bir katkısının olduğu çalışmada elde
edilmiş bir başka sonuçtur.
  

References

  • Abu Salam, Z. 2017. Yapay Sinir Ağları ile Dibis Barajı'nın Seviye Tahmini. Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 50s, Isparta.
  • Alshihri, M. M., Azmy, A. M., El-Bisy, M. S. 2009. Neural Networks for Predicting Compressive Strength of Structural Light Weight Concrete. Construction and Building Materials, 23(2009), 2214–2219.
  • Altunkaynak, A. 2007. Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Network. Water Resour Manage, 21(2007), 399-408.
  • Chandwani, V., Agrawal, V., Nagar, R. 2015. Modeling Slump of Ready Mix Concrete Using Genetic Algorithms Assisted Training of Artificial Neural Networks. Expert Systems with Applications, 42(2015), 885–893.
  • Chen, C., Yu, W., Liang, C., Chun, W. 2016, Developing a Conjunctive Use Optimization Model for Allocating Surface and Subsurface Water in an Off-Stream Artificial Lake System. WATER, 8(2016), 1-14.
  • Doğan, E., Kocamaz, U., Utkucu, M., Yıldırım, E., 2016. Modelling Daily water level fluctuations of Lake Van (Eastern Turkey) using Artificial Neural Networks. Fundam. Appl. Limnol., 187(3), 177–189.
  • Elmas, Ç. 2003. Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama). Seçkin Yayınevi, Ankara, 192s.
  • Faraj, B. 2016. Dibis Barajı'nın yönetimi yıllık rapor. Kerkük.
  • Lohani, A., Krishan, G. 2015. Application of Artificial Neural Network for Groundwater Level Simulation in Amritsar and Gurdaspur Districts of Punjab, India. Earth Science and Climatic Change, 6(2015), 1-5.
  • Mpallas, L., Tzimopoulos, C., Evangelides, C. 2011, Comparison between Neural Networks and Adaptive Neuro-fuzzy Inference System in Modeling Lake Kerkini Water Level Fluctuation Lake Management using Artificial Intelligence. Journal of Environmental Science and Technology, 4(2011), 366-376.
  • Rajasekaran, S., Pai, G. 2003. Neural networks, fuzzy logic and genetic algorithms. synthesis and applications. Prentice-Hall of India Private Limited, New Delh, 456s.
  • Saplıoğlu, K., Çimen, M. 2010. Yapay Sinir Ağlarını Kullanarak Günlük Yağış Miktarının Tahmini. Journal of Engineering Science and Design, 1 (2011), 14-21.
  • Shafaei, M., Kisi, O., 2016. Lake Level Forecasting Using Wavelet – Svr, Wavelet – Anfis and Wavelet – Arma Conjunction Models. Water Resour Manage, 30, 79-97.
  • Tanty, R., Desmukh, T., 2015. Application of Artificial Neural Network in Hydrology- A Review. International Journal of Engineering Research and Technology, 4(06), 184-188.
  • Vaheddoost, B., Aksoy, H., Abghari, H., 2016. Prediction of Water Level using Monthly Lagged Data in Lake Urmia, Iran. Water Resources Management, 30(13), 4951–4967.

PREDICTION OF WATER LEVEL IN DIBIS DAM USING ARTIFICIAL NEURAL NETWORK

Year 2018, Volume: 6 Issue: 4, 564 - 569, 11.12.2018

Abstract










Gradual increasing in water demanding has made the use
of water resources in the optimum manner as a necessity. In this study, it is
attempted to prediction the water level of dibis dam located at the north-west
of kirkuk in iraq. Inflow, outflow values of dibis dam, rainfall values and the
water level at the beginning were used in the models' prediction in different
combinations. Four different models have been used in this study. The models
were made with/without rainfall and with/without initial water level. It was
found that the initial water level is the most important parameter and also
clarified the significance of the rainfall as one of the other parameters which
is changing the accuracy only by 1%. Another finding shows that the inclusion
of the water level of the previous day in the models contributed only by 2%, in
addition to the initial water level.
    

References

  • Abu Salam, Z. 2017. Yapay Sinir Ağları ile Dibis Barajı'nın Seviye Tahmini. Süleyman Demirel Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi, 50s, Isparta.
  • Alshihri, M. M., Azmy, A. M., El-Bisy, M. S. 2009. Neural Networks for Predicting Compressive Strength of Structural Light Weight Concrete. Construction and Building Materials, 23(2009), 2214–2219.
  • Altunkaynak, A. 2007. Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Network. Water Resour Manage, 21(2007), 399-408.
  • Chandwani, V., Agrawal, V., Nagar, R. 2015. Modeling Slump of Ready Mix Concrete Using Genetic Algorithms Assisted Training of Artificial Neural Networks. Expert Systems with Applications, 42(2015), 885–893.
  • Chen, C., Yu, W., Liang, C., Chun, W. 2016, Developing a Conjunctive Use Optimization Model for Allocating Surface and Subsurface Water in an Off-Stream Artificial Lake System. WATER, 8(2016), 1-14.
  • Doğan, E., Kocamaz, U., Utkucu, M., Yıldırım, E., 2016. Modelling Daily water level fluctuations of Lake Van (Eastern Turkey) using Artificial Neural Networks. Fundam. Appl. Limnol., 187(3), 177–189.
  • Elmas, Ç. 2003. Yapay Sinir Ağları (Kuram, Mimari, Eğitim, Uygulama). Seçkin Yayınevi, Ankara, 192s.
  • Faraj, B. 2016. Dibis Barajı'nın yönetimi yıllık rapor. Kerkük.
  • Lohani, A., Krishan, G. 2015. Application of Artificial Neural Network for Groundwater Level Simulation in Amritsar and Gurdaspur Districts of Punjab, India. Earth Science and Climatic Change, 6(2015), 1-5.
  • Mpallas, L., Tzimopoulos, C., Evangelides, C. 2011, Comparison between Neural Networks and Adaptive Neuro-fuzzy Inference System in Modeling Lake Kerkini Water Level Fluctuation Lake Management using Artificial Intelligence. Journal of Environmental Science and Technology, 4(2011), 366-376.
  • Rajasekaran, S., Pai, G. 2003. Neural networks, fuzzy logic and genetic algorithms. synthesis and applications. Prentice-Hall of India Private Limited, New Delh, 456s.
  • Saplıoğlu, K., Çimen, M. 2010. Yapay Sinir Ağlarını Kullanarak Günlük Yağış Miktarının Tahmini. Journal of Engineering Science and Design, 1 (2011), 14-21.
  • Shafaei, M., Kisi, O., 2016. Lake Level Forecasting Using Wavelet – Svr, Wavelet – Anfis and Wavelet – Arma Conjunction Models. Water Resour Manage, 30, 79-97.
  • Tanty, R., Desmukh, T., 2015. Application of Artificial Neural Network in Hydrology- A Review. International Journal of Engineering Research and Technology, 4(06), 184-188.
  • Vaheddoost, B., Aksoy, H., Abghari, H., 2016. Prediction of Water Level using Monthly Lagged Data in Lake Urmia, Iran. Water Resources Management, 30(13), 4951–4967.
There are 15 citations in total.

Details

Primary Language Turkish
Subjects Civil Engineering
Journal Section Research Articles
Authors

Zaki Kareem Ahmed Abu Salam 0000-0002-4544-0136

Mustafa Erol Keskin 0000-0003-3749-5169

Publication Date December 11, 2018
Submission Date May 15, 2018
Acceptance Date September 27, 2018
Published in Issue Year 2018 Volume: 6 Issue: 4

Cite

APA Abu Salam, Z. K. A., & Keskin, M. E. (2018). YAPAY SİNİR AĞLARI İLE DİBİS BARAJI’NIN SEVİYE TAHMİNİ. Mühendislik Bilimleri Ve Tasarım Dergisi, 6(4), 564-569.