Research Article
BibTex RIS Cite

Artificial Intelligence (AI) Studies in Water Resources

Year 2018, Volume: 3 Issue: 2, 187 - 195, 17.05.2018
https://doi.org/10.28978/nesciences.424674

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.

References

  • 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 Toprak ve Su Kaynakları Kongresi, Cilt I, Sayfa: 98-105, 22-26 Kasım, Ankara, Türkiye.
  • Brady, J.E. & Holum, J.R. (1988). Fundamentals of Chemistry, ISBN: 978-0471844730, 3rd Edition, 1055pp.
  • Chang, F.J., Chung, C.H., Chen, P.A., Liu, C.W., Coynel, A., Vachaud, G. (2014). Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis. Science of the Total Environment, 494-495, 202-210.
  • Chapra, S.C. (2008). Surface Water-Quality Modelling, 844p. Waveland Pr Inc, ISBN: 978-1577666059.
  • Chau, K.W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin, 52, 726-733. Cherkassy, V., Krasnopolsky, V., Solomatine, D., Valdes, J. (2006). Computational intelligence in earth sciences and environmental applications: Issue and challenges. Neural Networks, 19, 113-121.
  • Chithra, N.R. & Thampi, S.G. (2016). Downscaling future projections of monthly precipitation in a catchment with varying physiography. ISH Journal of Hydraulic Engineering. http://dx.doi.org/10.1080/09715010.2016.1264895.
  • Cibin, R., Athira, P., Sudheer, K.P. Chaubey, I. (2014). Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty. Hydrological Processes, 28(4), 2033-2045.
  • Cigizoglu, H.K. (2003). Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences Journal, 48(3), 363-379.
  • Darras, T., Estupina, V.B., Kong-A-Siou, L., Vayssade, B., Johannet, A., Pistre S. (2015). Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France). Hydrology and Earth System Sciences, 19, 4397-4410.
  • Demirci, M. & Baltaci, A. (2013). Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing Applications, 23, 145-151.
  • Demirci, M., Üneş, F., Saydemir, S. (2015). Suspended sediment estimation using an artificial intelligence approach. In: Sediment matters. Eds. P. Heininger, J. Cullmann. Springer International Publishing p. 83-95.
  • Droppo, I.G., Krishnappan, B.G. (2016). Modeling of hydrophobic cohesive sediment transport in the Ells River Alberta, Canada. Journal of Soils and Sediments, 16(12), 2753-2765.
  • Goyal, M.K., Bharti, B., Quilty, J., Adamowskic, J., Pandey, A. (2014). Modeling of daily pan evaporation in subtropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications, 41(11), 5267-5276.
  • Güçlü, Y.S., Subyani, A.M. & Şen, Z. (2017). Regional fuzzy chain model for evapotranspiration estimation. Journal of Hydrology, 544, 233-241.
  • Güner, H.A.A. & Yumuk, H.A. (2014). Application of a fuzzy inference system for the prediction of longshore sediment transport. Applied Ocean Research, 48, 162-175.
  • Hao, A. & Singh, V.P. (2016). Review of dependence modeling in hydrology and water resources. Progress in Physical Geography, 40(4), 549-578.
  • Harmancioglu, N.B., Fıstıkoglu, O., Ozkul, S.D., Signh, V.P. & Alpaslan, M.N. (1999). Water Quality Monitoring Network Design. Dohrecht, the Netherlands.
  • Haykin, S. (1998). Neural Networks- A Comprehensive Foundation (2nd. ed.), Prentice-Hall, Upper Saddle River, NJ. Hipni, A., El-shafie, A., Najah, A., Karim, O. A., Hussain, A. & Mukhlisin, M. (2013). Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system. Water Resources Management, 27 (10), 3803-3823.
  • Huang, W., Xu, B. & Hilton, A.C. (2004). Forecasting flows in Apalachicola River using neural networks. Hydrologic Process, 18(13), 2545-2564.
  • Karimi, S., Kisi, O., Kim, S., Nazemi, A.H. & Shiri, J. (2016). Modelling daily reference evapotranspiration in humid locations of South Korea using local and cross-station data management scenarios. International Journal of Climatology.
  • Khan U.T. & Valeo, C. (2015). Dissolved oxygen prediction using a possibility-theory based fuzzy neural network. Hydrology and Earth System Sciences Discuss, 12, 12311–12376.
  • Kisi, O. & Ay, M. (2013). Modeling dissolved oxygen concentration using soft computing techniques. 6th International Perpective on Water Resources & the Environment. January 07-09. İzmir, Turkey.
  • Kuo, J.T., Hsieh, M.H., Lung, W.S. & She N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200, 171-177.
  • Li, B., Yang, G., Wan, R., Dai, X. & Zhang, Y. (2016). Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China. Hydrology Research, in press.
  • Lin, G.F. & Chen, L.H. (2004). A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology, 289, 1-8. Londhe, S., Dixit, P., Shah, S. & Narkhede, S. (2015). Infilling of missing daily rainfall records using artificial neural network, ISH Journal of Hydraulic Engineering, 21(3), 255-264.
  • Maier, H. & Dandy, G. (2000). Neural networks for the prediction and forecasting of water sources variables: a review of a modeling issues and applications. Environmental Modeling&Software, 15, 101-124.
  • Maier, H.R. & Dandy, G.C. (1996). The use of artificial neural network for the prediction of water quality parameters. Water Resources, 32(4), 1013-1022.
  • Maier, H.R., Jain, A., Dandy, G.C. & Sudheer, K.P. (2010). Methods used for development of neural Networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25, 891-909.
  • Marquardt, D.W. (1963). An algorithm for least squares estimation of non-linear parameters, Journal of Society Industrial and Applied Mathematics, 11, 431-441.
  • Marsili-Libelli, S. (2004). Fuzzy prediction of the algal blooms in the Orbetello lagoon. Environmental Modelling and Software, 19 (9), 799-808.
  • Mi, X., Sivakumar, M. & Hagare, D. (2004). A general review of applications of artificial neural network to water industry. In M. Mowlaei, A. Rose&J. Lamborn (Eds.), Environmental Sustainability through Multidisciplinary Integration (pp.234-243). Australia: Environmental Engineering Research Event.
  • Moatar, F., Fessant, F. & Poirel, A. (1999). pH modelling by neural Networks. Application of control and validation data series in the Middle Loire River. Ecological Modelling, 120, 141-156.
  • Nourani V., Baghana, A.H., Adamowski, J. & Kisi, O. (2014). Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514, 358-377.
  • Nourani, V., Baghanam, A.H. & Gebremichael, M. (2012). Investigating the Ability of Artificial Neural Network (ANN) Models to Estimate Missing Rain-gauge Data. Journal of Environmental Informatics, 19(1), 38-50.
  • Oke, S.A. (2008). A Literature Review on Artificial Intelligence. International Journal of Information and Management Sciences, 19(4), 535-570.
  • Scardi, M. (2001). Advances in neural network modelling of phytoplankton primary production. Ecological Modelling, 146, 33-45. Şen, Z., Harmancıoğlu, N., Şorman, Ü. & Bulu, A. (2002). Hidrolojide Veri, İşlem, Yorumlama ve Tasarım Seminer Notları, DSİ Küçük Çamlıca Tesisleri, Su Vakfı Yayınları, 193pp., Editor: Zekai Şen.
  • Takagi, T. & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116-32.
  • Talebi, A., Mahjoobi, J., Dastorani, M.T. & Moosavi, V. (2016). Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran).
  • Talei, A., Chua, L.H.C., Quek, C., Jansson, P.-E. (2013). Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. Journal of Hydrology, 488, 17-32.
  • Tayfur, G. (2011). Soft Computing in Water Resources Engineering: Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms, WIT Press.
  • Tayfur, G. (2017). Modern Optimization Methods in Water Resources Planning, Engineering and Management. Water Resource Management.
  • Üneş, F., Demirci, M., Kişi, Ö. (2015). Prediction of millers ferry dam reservoir level in USA using artificial neural network. Periodica Polytechnica Civil Engineering, 59(3), 309-318.
  • Ward, R.C. (2007). Water quality monitoring: Where’s the beef?”. Water Resource Bulletin, 32(4), 673-680.
  • Waseem, M., Ajmal, M., & Kim, T.W. (2015). Ensemble hydrological prediction of streamflow percentile at ungauged basins in Pakistan. Journal of Hydrology, 525, 130-137.
  • Wilson, H. & Recknagel, F. (2001). Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes. Ecological Modelling, 147, 69-84.
  • Yan, H., Zou, Z. & Wang, H. (2010). Adaptive neuro fuzzy inference system for classification of water quality status. Journal Environmental Sciences, 22(12), 1891-1896.
  • Yaseen, Z.M., El-shafie, A., Jaafar, O., Afan, H.A. & Sayl, K.N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000-2015 (Review Paper). Journal of Hydrology, 530, 829-844.
  • Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8(3), 338-353.
Year 2018, Volume: 3 Issue: 2, 187 - 195, 17.05.2018
https://doi.org/10.28978/nesciences.424674

Abstract

References

  • 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 Toprak ve Su Kaynakları Kongresi, Cilt I, Sayfa: 98-105, 22-26 Kasım, Ankara, Türkiye.
  • Brady, J.E. & Holum, J.R. (1988). Fundamentals of Chemistry, ISBN: 978-0471844730, 3rd Edition, 1055pp.
  • Chang, F.J., Chung, C.H., Chen, P.A., Liu, C.W., Coynel, A., Vachaud, G. (2014). Assessment of arsenic concentration in stream water using neuro fuzzy networks with factor analysis. Science of the Total Environment, 494-495, 202-210.
  • Chapra, S.C. (2008). Surface Water-Quality Modelling, 844p. Waveland Pr Inc, ISBN: 978-1577666059.
  • Chau, K.W. (2006). A review on integration of artificial intelligence into water quality modelling. Marine Pollution Bulletin, 52, 726-733. Cherkassy, V., Krasnopolsky, V., Solomatine, D., Valdes, J. (2006). Computational intelligence in earth sciences and environmental applications: Issue and challenges. Neural Networks, 19, 113-121.
  • Chithra, N.R. & Thampi, S.G. (2016). Downscaling future projections of monthly precipitation in a catchment with varying physiography. ISH Journal of Hydraulic Engineering. http://dx.doi.org/10.1080/09715010.2016.1264895.
  • Cibin, R., Athira, P., Sudheer, K.P. Chaubey, I. (2014). Application of distributed hydrological models for predictions in ungauged basins: a method to quantify predictive uncertainty. Hydrological Processes, 28(4), 2033-2045.
  • Cigizoglu, H.K. (2003). Estimation, forecasting and extrapolation of river flows by artificial neural networks. Hydrological Sciences Journal, 48(3), 363-379.
  • Darras, T., Estupina, V.B., Kong-A-Siou, L., Vayssade, B., Johannet, A., Pistre S. (2015). Identification of spatial and temporal contributions of rainfalls to flash floods using neural network modelling: case study on the Lez basin (southern France). Hydrology and Earth System Sciences, 19, 4397-4410.
  • Demirci, M. & Baltaci, A. (2013). Prediction of suspended sediment in river using fuzzy logic and multilinear regression approaches. Neural Computing Applications, 23, 145-151.
  • Demirci, M., Üneş, F., Saydemir, S. (2015). Suspended sediment estimation using an artificial intelligence approach. In: Sediment matters. Eds. P. Heininger, J. Cullmann. Springer International Publishing p. 83-95.
  • Droppo, I.G., Krishnappan, B.G. (2016). Modeling of hydrophobic cohesive sediment transport in the Ells River Alberta, Canada. Journal of Soils and Sediments, 16(12), 2753-2765.
  • Goyal, M.K., Bharti, B., Quilty, J., Adamowskic, J., Pandey, A. (2014). Modeling of daily pan evaporation in subtropical climates using ANN, LS-SVR, Fuzzy Logic, and ANFIS. Expert Systems with Applications, 41(11), 5267-5276.
  • Güçlü, Y.S., Subyani, A.M. & Şen, Z. (2017). Regional fuzzy chain model for evapotranspiration estimation. Journal of Hydrology, 544, 233-241.
  • Güner, H.A.A. & Yumuk, H.A. (2014). Application of a fuzzy inference system for the prediction of longshore sediment transport. Applied Ocean Research, 48, 162-175.
  • Hao, A. & Singh, V.P. (2016). Review of dependence modeling in hydrology and water resources. Progress in Physical Geography, 40(4), 549-578.
  • Harmancioglu, N.B., Fıstıkoglu, O., Ozkul, S.D., Signh, V.P. & Alpaslan, M.N. (1999). Water Quality Monitoring Network Design. Dohrecht, the Netherlands.
  • Haykin, S. (1998). Neural Networks- A Comprehensive Foundation (2nd. ed.), Prentice-Hall, Upper Saddle River, NJ. Hipni, A., El-shafie, A., Najah, A., Karim, O. A., Hussain, A. & Mukhlisin, M. (2013). Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system. Water Resources Management, 27 (10), 3803-3823.
  • Huang, W., Xu, B. & Hilton, A.C. (2004). Forecasting flows in Apalachicola River using neural networks. Hydrologic Process, 18(13), 2545-2564.
  • Karimi, S., Kisi, O., Kim, S., Nazemi, A.H. & Shiri, J. (2016). Modelling daily reference evapotranspiration in humid locations of South Korea using local and cross-station data management scenarios. International Journal of Climatology.
  • Khan U.T. & Valeo, C. (2015). Dissolved oxygen prediction using a possibility-theory based fuzzy neural network. Hydrology and Earth System Sciences Discuss, 12, 12311–12376.
  • Kisi, O. & Ay, M. (2013). Modeling dissolved oxygen concentration using soft computing techniques. 6th International Perpective on Water Resources & the Environment. January 07-09. İzmir, Turkey.
  • Kuo, J.T., Hsieh, M.H., Lung, W.S. & She N. (2007). Using artificial neural network for reservoir eutrophication prediction. Ecological Modelling, 200, 171-177.
  • Li, B., Yang, G., Wan, R., Dai, X. & Zhang, Y. (2016). Comparison of random forests and other statistical methods for the prediction of lake water level: a case study of the Poyang Lake in China. Hydrology Research, in press.
  • Lin, G.F. & Chen, L.H. (2004). A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology, 289, 1-8. Londhe, S., Dixit, P., Shah, S. & Narkhede, S. (2015). Infilling of missing daily rainfall records using artificial neural network, ISH Journal of Hydraulic Engineering, 21(3), 255-264.
  • Maier, H. & Dandy, G. (2000). Neural networks for the prediction and forecasting of water sources variables: a review of a modeling issues and applications. Environmental Modeling&Software, 15, 101-124.
  • Maier, H.R. & Dandy, G.C. (1996). The use of artificial neural network for the prediction of water quality parameters. Water Resources, 32(4), 1013-1022.
  • Maier, H.R., Jain, A., Dandy, G.C. & Sudheer, K.P. (2010). Methods used for development of neural Networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling & Software, 25, 891-909.
  • Marquardt, D.W. (1963). An algorithm for least squares estimation of non-linear parameters, Journal of Society Industrial and Applied Mathematics, 11, 431-441.
  • Marsili-Libelli, S. (2004). Fuzzy prediction of the algal blooms in the Orbetello lagoon. Environmental Modelling and Software, 19 (9), 799-808.
  • Mi, X., Sivakumar, M. & Hagare, D. (2004). A general review of applications of artificial neural network to water industry. In M. Mowlaei, A. Rose&J. Lamborn (Eds.), Environmental Sustainability through Multidisciplinary Integration (pp.234-243). Australia: Environmental Engineering Research Event.
  • Moatar, F., Fessant, F. & Poirel, A. (1999). pH modelling by neural Networks. Application of control and validation data series in the Middle Loire River. Ecological Modelling, 120, 141-156.
  • Nourani V., Baghana, A.H., Adamowski, J. & Kisi, O. (2014). Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review. Journal of Hydrology, 514, 358-377.
  • Nourani, V., Baghanam, A.H. & Gebremichael, M. (2012). Investigating the Ability of Artificial Neural Network (ANN) Models to Estimate Missing Rain-gauge Data. Journal of Environmental Informatics, 19(1), 38-50.
  • Oke, S.A. (2008). A Literature Review on Artificial Intelligence. International Journal of Information and Management Sciences, 19(4), 535-570.
  • Scardi, M. (2001). Advances in neural network modelling of phytoplankton primary production. Ecological Modelling, 146, 33-45. Şen, Z., Harmancıoğlu, N., Şorman, Ü. & Bulu, A. (2002). Hidrolojide Veri, İşlem, Yorumlama ve Tasarım Seminer Notları, DSİ Küçük Çamlıca Tesisleri, Su Vakfı Yayınları, 193pp., Editor: Zekai Şen.
  • Takagi, T. & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, 15, 116-32.
  • Talebi, A., Mahjoobi, J., Dastorani, M.T. & Moosavi, V. (2016). Estimation of suspended sediment load using regression trees and model trees approaches (Case study: Hyderabad drainage basin in Iran).
  • Talei, A., Chua, L.H.C., Quek, C., Jansson, P.-E. (2013). Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning. Journal of Hydrology, 488, 17-32.
  • Tayfur, G. (2011). Soft Computing in Water Resources Engineering: Artificial Neural Networks, Fuzzy Logic and Genetic Algorithms, WIT Press.
  • Tayfur, G. (2017). Modern Optimization Methods in Water Resources Planning, Engineering and Management. Water Resource Management.
  • Üneş, F., Demirci, M., Kişi, Ö. (2015). Prediction of millers ferry dam reservoir level in USA using artificial neural network. Periodica Polytechnica Civil Engineering, 59(3), 309-318.
  • Ward, R.C. (2007). Water quality monitoring: Where’s the beef?”. Water Resource Bulletin, 32(4), 673-680.
  • Waseem, M., Ajmal, M., & Kim, T.W. (2015). Ensemble hydrological prediction of streamflow percentile at ungauged basins in Pakistan. Journal of Hydrology, 525, 130-137.
  • Wilson, H. & Recknagel, F. (2001). Towards a generic artificial neural network model for dynamic predictions of algal abundance in freshwater lakes. Ecological Modelling, 147, 69-84.
  • Yan, H., Zou, Z. & Wang, H. (2010). Adaptive neuro fuzzy inference system for classification of water quality status. Journal Environmental Sciences, 22(12), 1891-1896.
  • Yaseen, Z.M., El-shafie, A., Jaafar, O., Afan, H.A. & Sayl, K.N. (2015). Artificial intelligence based models for stream-flow forecasting: 2000-2015 (Review Paper). Journal of Hydrology, 530, 829-844.
  • Zadeh, L.A. (1965). Fuzzy Sets. Information and Control, 8(3), 338-353.
There are 58 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section 3
Authors

Murat Ay

Serhat Özyıldırım This is me

Publication Date May 17, 2018
Submission Date December 4, 2017
Published in Issue Year 2018 Volume: 3 Issue: 2

Cite

APA Ay, M., & Özyıldırım, S. (2018). Artificial Intelligence (AI) Studies in Water Resources. Natural and Engineering Sciences, 3(2), 187-195. https://doi.org/10.28978/nesciences.424674

                                                                                               We welcome all your submissions

                                                                                                             Warm regards,
                                                                                                      


All published work is licensed under a Creative Commons Attribution 4.0 International License Link . Creative Commons License
                                                                                         NESciences.com © 2015