Moreover, the study has also identified some future research possibilities and suggestions for modelling of the water variables.
📌 3 Key Takeaways
🎯
Objective & Hypothesis:Artificial intelligence has been extensively used in many areas such as computer science, robotics, engineering, medicine, translation, economics, business, and psychology.
📊
Key Findings & Data:Various studies in the literature show that the artificial intelligence in modeling approaches give close results to the real data for solution of linear, non-linear, and other systems.
💡
Conclusion & Impact:Moreover, the study has also identified some future research possibilities and suggestions for modelling of the water variables.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Artificial intelligence methods · modelling · water variables
Abstract
Artificial intelligence has been extensively used in many areas such as computer science, robotics, engineering, medicine, translation, economics, business, and psychology. Various studies in the literature show that the artificial intelligence in modeling approaches give close results to the real data for solution of linear, non-linear, and other systems. In this study, we reviewed the current state-of-the-art and progress on the modelling of artificial intelligence for water variables: rainfall-runoff, evaporation and evapotranspiration, streamflow, sediment, water quality variables, and dam or lake water level changes. Moreover, the study has also identified some future research possibilities and suggestions for modelling of the water variables.
AY, M., ÖZYILDIRIM, S. (2018). Artificial Intelligence (AI) Studies in Water Resources. Natural and Engineering Sciences, Cilt: 3 - Sayı: 2, 187–195. https://ajindex.com/articles/793236
Afan, H.A., El-shafie, A., Mohtar, W.H.M.W., Yaseen, Z.M. (2016). Past, present and prospect of an Artificial Intelligence (AI) based model for sediment transport prediction. Journal of Hydrology, 541, Part B, 902-913.
Akkoyunlu, A., Altun, H. & Cigizoglu, H.K. (2011). Depth integrated estimation of the lake dissolved oxygen (DO). Journal of Environment Engineering, 137(10), 961-967.
Alizadeh, M.J. & Kavianpour, M.R. (2015). Development of wavelet-ANN models to predict water quality parameters in Hilo Bay, Pacific Ocean. Marine Poll Bullet, 98(1-2), 171-178.
Ashrafi, M., Chua, L.H.C., Quek, C., Qin, X. (2017). A fully-online Neuro-Fuzzy model for flow forecasting in basins with limited data. Journal of Hydrology, 545, 424-435.
Ay, M. (2010). Sulama suyu kalitesinin kümelemeye dayalı bulanık sistem ile sınıflandırılması (Classification of irrigation water quality by a clustering based fuzzy system). Yayımlanmış Yüksek Lisans Tezi. Tez no: 269333. 41 sayfa. https://tez.yok.gov.tr/UlusalTezMerkezi/giris.jsp.
Ay, M. (2014). Su kalitesi parametrelerinin yapay zekâ yöntemleri ile değerlendirilmesi (Evaluation of water quality parameters by using artificial intelligence methods). Yayımlanmış Doktora Tezi. Tez no: 360613. 136 sayfa. https://tez.yok.gov.tr/UlusalTezMerkezi/giris.jsp.
Ay, M. & Kisi, O. (2012). Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado, USA. Journal of Environmental Engineering, 138(6), 654-662.
Ay, M. & Kisi, O. (2013a). Modeling dissolved oxygen concentration using neural network and ARMA techniques. 6th International Perpective on Water Resources & the Environment (IPWE-2013). January 07-09.
İzmir,
Turkey. Ay, M. & Kisi, O. (2013b). Modelling COD concentration by using different artificial intelligence methods. Journal of Selçuk University Natural and Applied Science, Special Issue 2, 477-489, Avaiable at: http://www.josunas.org/login/index.php/josunas/article/view/181/147,
JOSUNAS Online, ISSN: 2147-3781.
Ay, M. & Kisi, O. (2014). Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. Journal of Hydrology, 511, 279-289.
Ay, M. & Kisi, O. (2017). Estimation of dissolved oxygen by using neural networks and neuro fuzzy computing techniques. KSCE Journal of Civil Engineering, 21(5), 1631-1639.
Ay, M. & Kişi, Ö. (2011). Sulama suyu kalitesini etkileyen,,, ve değişkenleri ile tuz konsantrasyonunun modellenmesi. II. Ulusal Top