Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2023, , 634 - 642, 30.06.2023
https://doi.org/10.16984/saufenbilder.1152982

Öz

Kaynakça

  • Y. Zhou, S. Guo, F. Chang, "Explore an Evolutionary Recurrent ANFIS for Modelling Multi-Step-Ahead Flood Forecasts", Journal of Hydrology, vol. 570, pp. 343-355, 2019.
  • Z. He, X. Wen, H. Liu, J. Du, "A Comparative Study Of Artificial Neural Network, Adaptive Neuro Fuzzy İnference System And Support Vector Machine For Forecasting River Flow İn The Semiarid Mountain Region", Journal of Hydrology, vol. 509, pp. 379-386, 2014.
  • Z. Yaseen, S. Sulaiman, R. Deo, K. Chau, "An Enhanced Extreme Learning Machine Model For River Flow Forecasting: State-Of-The-Art, Practical Applications İn Water Resource Engineering Area And Future Research Direction", Journal of Hydrology, vol. 569, pp. 387-408, 2019.
  • Z. Yaseen, S. R. Naganna, Z. Sa’adi, P. Samui, M.A. Ghorbani, S. Shahid, "Hourly River Flow Forecasting: Application Of Emotional Neural Network Versus Multiple Machine Learning Paradigms", Water Resources Management, vol. 34, no. 3, pp. 1075-1091, 2020.
  • Z. Wang, N. F. Attar, K. Khalili, J. Behmanes, S. S. Band, A. Mosavi, K. K. Chau, "Monthly Streamflow Prediction Using A Hybrid Stochastic-Deterministic Approach For Parsimonious Non-Linear Time Series Modeling", Engineering Applications of Computational Fluid Mechanics, vol. 14, no. 1, pp. 1351-1372, 2020.
  • A. Sun, D. Wang, X. Xu, "Monthly Streamflow Forecasting Using Gaussian Process Regression", Journal of Hydrology, vol. 511, pp. 72-81, 2014.
  • Z. Yaseen, A. El-shafie, O. Jaafar, H. Afan, K. Sayl, "Artificial İntelligence Based Models For Stream-Flow Forecasting: 2000–2015", Journal of Hydrology, vol. 530, pp. 829-844, 2015.
  • E. Khadangi, H. R. Madvar, M. M. Ebadzadeh “Comparison Of ANFIS And RBF Models İn Daily Stream Flow Forecasting”. In 2009 2nd International Conference on Computer, control and Communication (pp. 1-6). IEEE.
  • G. Huang, Q. Zhu, C. Siew, "Extreme Learning Machine: Theory And Applications", Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
  • H. Sıqueıra, L. Boccato, R. Attux, C. Lyra, "Unorganızed Machınes For Seasonal Streamflow Serıes Forecastıng", International Journal of Neural Systems, vol. 24, no. 03, p. 1430009, 2014.
  • Z. Yaseen, O. Jaafar, R. C. Deo, O. Kisi, J. Adamowski, J. Qulity, A. El-Shafie, "Stream-Flow Forecasting Using Extreme Learning Machines: A Case Study İn A Semi-Arid Region in Iraq", Journal of Hydrology, vol. 542, pp. 603-614, 2016.
  • R. Adnan, Z. Liang, S. Trajkovic, M. Zounemat-Kermani, B. Li, O. Kisi, "Daily Streamflow Prediction Using Optimally Pruned Extreme Learning Machine", Journal of Hydrology, vol. 577, p. 123981, 2019.
  • D. E. Rumelhart, G.E. Hinton, R. J. Williams “Learning Internal Representations By Error Propagation”, Parallel Distributed Processing.
  • M. Atiquzzaman, J. Kandasamy, "Robustness of Extreme Learning Machine İn The Prediction of Hydrological Flow Series", Computers & Geosciences, vol. 120, pp. 105-114, 2018.
  • D. Kukolj, "Design of Adaptive Takagi–Sugeno–Kang Fuzzy Models", Applied Soft Computing, vol. 2, no. 2, pp. 89-103, 2002.
  • Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.
  • A. Mosavi, P. Ozturk, K. Chau, "Flood Prediction Using Machine Learning Models: Literature Review", Water, vol. 10, no. 11, p. 1536, 2018.
  • H. B. Demuth, “Fuzzy Logic Toolbox for Use With MATLAB”, User’s Guide Version 4. MA, 2000.
  • Z. Jin, G. Zhou, D. Gao, Y. Zhang, "EEG Classification Using Sparse Bayesian Extreme Learning Machine For Brain–Computer Interface", Neural Computing and Applications, vol. 32, no. 11, pp. 6601-6609, 2018.
  • E. R. David, L. M. James, “Learning Internal Representations By Error Propagation. In Parallel Distributed Processing: Explorations in The Microstructure Of Cognition: Foundations”, MITP, pp. 318-362,1987.
  • A. Khashman, "Application of An Emotional Neural Network to Facial Recognition", Neural Computing and Applications, vol. 18, no. 4, pp. 309-320, 2008.
  • A. Khashman, “Blood Cell Identification Using Emotional Neural Networks”, Journal of Information Science & Engineering, 2009, vol. 25,6.
  • V. N. Vapnik, “Statistical Learning Theory”, New York: Wiley, 1998, ISBN: 978-0-471-03003-4
  • C. J. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”. Data mining and knowledge discovery, 1998, Vol. 2(2), pp. 121-167.
  • F. Fotovatikhah, M. Herrera, S. Shamshirband, K. Chau, S. Faizollahzadeh Ardabili, M. Piran, "Survey of Computational İntelligence As Basis to Big Flood Management: Challenges, Research Directions And Future Work", Engineering Applications of Computational Fluid Mechanics, vol. 12, no. 1, pp. 411-437, 2018.
  • A. Mehr, A. Gandomi, "MSGP-LASSO: An Improved Multi-Stage Genetic Programming Model for Streamflow Prediction", Information Sciences, vol. 561, pp. 181-195, 2021.
  • U. Okkan, Z. Serbes, "Rainfall-Runoff Modeling Using Least Squares Support Vector Machines", Environmetrics, vol. 23, no. 6, pp. 549-564, 2012.
  • N. Ceryan, U. Okkan, P. Samui, S. Ceryan, "Modeling of Tensile Strength of Rocks Materials Based on Support Vector Machines Approaches", International Journal for Numerical and Analytical Methods in Geomechanics, vol. 37, no. 16, pp. 2655-2670, 2012.
  • R. Chanklan, N. Kaoungku, K. Suksut, K. Kerdprasop, N. Kerdprasop, "Runoff Prediction With a Combined Artificial Neural Network and Support Vector Regression", International Journal of Machine Learning and Computing, vol. 8, no. 1, pp. 39-43, 2018.
  • S. Shamshirband, S. Hashemi, H. Salimi, S. Samadianfard, E. Asadi, S. Shadkani, K. Kargar, A. Mosavi, N. Nabipour, k. W. Chau, "Predicting Standardized Streamflow Index For Hydrological Drought Using Machine Learning Models", Engineering Applications of Computational Fluid Mechanics, vol. 14, no. 1, pp. 339-350, 2020.
  • C. Wu, K. Chau, "Prediction of Rainfall Time Series Using Modular Soft Computing Methods", Engineering Applications of Artificial Intelligence, vol. 26, no. 3, pp. 997-1007, 2013.
  • K. P. Murphy, “Machine Learning: A Probabilistic Perspective”. MIT press, 2012.
  • R. Richardson, M. Osborne, D. Howey, "Gaussian Process Regression for Forecasting Battery State of Health", Journal of Power Sources, vol. 357, pp. 209-219, 2017.
  • Z. Zhang, Q. Zhang, V. Singh, "Univariate Streamflow Forecasting Using Commonly Used Data-Driven Models: Literature Review and Case Study", Hydrological Sciences Journal, vol. 63, no. 7, pp. 1091-1111, 2018.
  • A. Danandeh Mehr, V. Nourani, E. Kahya, B. Hrnjica, A. Sattar, Z. Yaseen, "Genetic Programming in Water Resources Engineering: A State-Of-The-Art Review", Journal of Hydrology, vol. 566, pp. 643-667, 2018.
  • R. Taormina, K. Chau, "ANN-Based İnterval Forecasting of Streamflow Discharges Using The LUBE Method and MOFIPS", Engineering Applications of Artificial Intelligence, vol. 45, pp. 429-440, 2015.
  • Y. Zhang, H. Yang, H. Cui, Q. Chen, "Comparison of The Ability of ARIMA, WNN and SVM Models for Drought Forecasting in The Sanjiang Plain, China", Natural Resources Research, vol. 29, no. 2, pp. 1447-1464, 2019.
  • Z. Chen, M. Kavvas, N. Ohara, M. Anderson, J. Yoon, "Coupled Regional Hydroclimate Model and Its Application to the Tigris-Euphrates Basin", Journal of Hydrologic Engineering, vol. 16, no. 12, pp. 1059-1070, 2011.
  • DSI:https://web.archive.org/web/200508130 23730/http://www.dsi.gov.tr/topraksu.htm ((2020, 07 16).
  • C. C. Aggarwal, “Data Mining: The Textbook”, Springer, New York, 2015.
  • G. B. Guacho, S. Abdali, N. Shah, E. E. Papalexakis, “Semi-Supervised Content-Based Detection of Misinformation Via Tensor Embeddings”, In 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), 2018, pp. 322-325. IEEE.
  • Y. Khan, S. S Chai, “Ensemble of ANN and ANFIS for Water Quality Prediction And Analysis-A Data Driven Approach”, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2017, Vol. 9(2-9), pp. 117-122.
  • S. Samantaray, P. Dash, "Decision Tree Based Discrimination Between Inrush Currents And Internal Faults İn Power Transformer", International Journal of Electrical Power & Energy Systems, vol. 33, no. 4, pp. 1043-1048, 2011.

Application of Soft Computing Techniques in River Flow Modeling

Yıl 2023, , 634 - 642, 30.06.2023
https://doi.org/10.16984/saufenbilder.1152982

Öz

Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

Kaynakça

  • Y. Zhou, S. Guo, F. Chang, "Explore an Evolutionary Recurrent ANFIS for Modelling Multi-Step-Ahead Flood Forecasts", Journal of Hydrology, vol. 570, pp. 343-355, 2019.
  • Z. He, X. Wen, H. Liu, J. Du, "A Comparative Study Of Artificial Neural Network, Adaptive Neuro Fuzzy İnference System And Support Vector Machine For Forecasting River Flow İn The Semiarid Mountain Region", Journal of Hydrology, vol. 509, pp. 379-386, 2014.
  • Z. Yaseen, S. Sulaiman, R. Deo, K. Chau, "An Enhanced Extreme Learning Machine Model For River Flow Forecasting: State-Of-The-Art, Practical Applications İn Water Resource Engineering Area And Future Research Direction", Journal of Hydrology, vol. 569, pp. 387-408, 2019.
  • Z. Yaseen, S. R. Naganna, Z. Sa’adi, P. Samui, M.A. Ghorbani, S. Shahid, "Hourly River Flow Forecasting: Application Of Emotional Neural Network Versus Multiple Machine Learning Paradigms", Water Resources Management, vol. 34, no. 3, pp. 1075-1091, 2020.
  • Z. Wang, N. F. Attar, K. Khalili, J. Behmanes, S. S. Band, A. Mosavi, K. K. Chau, "Monthly Streamflow Prediction Using A Hybrid Stochastic-Deterministic Approach For Parsimonious Non-Linear Time Series Modeling", Engineering Applications of Computational Fluid Mechanics, vol. 14, no. 1, pp. 1351-1372, 2020.
  • A. Sun, D. Wang, X. Xu, "Monthly Streamflow Forecasting Using Gaussian Process Regression", Journal of Hydrology, vol. 511, pp. 72-81, 2014.
  • Z. Yaseen, A. El-shafie, O. Jaafar, H. Afan, K. Sayl, "Artificial İntelligence Based Models For Stream-Flow Forecasting: 2000–2015", Journal of Hydrology, vol. 530, pp. 829-844, 2015.
  • E. Khadangi, H. R. Madvar, M. M. Ebadzadeh “Comparison Of ANFIS And RBF Models İn Daily Stream Flow Forecasting”. In 2009 2nd International Conference on Computer, control and Communication (pp. 1-6). IEEE.
  • G. Huang, Q. Zhu, C. Siew, "Extreme Learning Machine: Theory And Applications", Neurocomputing, vol. 70, no. 1-3, pp. 489-501, 2006.
  • H. Sıqueıra, L. Boccato, R. Attux, C. Lyra, "Unorganızed Machınes For Seasonal Streamflow Serıes Forecastıng", International Journal of Neural Systems, vol. 24, no. 03, p. 1430009, 2014.
  • Z. Yaseen, O. Jaafar, R. C. Deo, O. Kisi, J. Adamowski, J. Qulity, A. El-Shafie, "Stream-Flow Forecasting Using Extreme Learning Machines: A Case Study İn A Semi-Arid Region in Iraq", Journal of Hydrology, vol. 542, pp. 603-614, 2016.
  • R. Adnan, Z. Liang, S. Trajkovic, M. Zounemat-Kermani, B. Li, O. Kisi, "Daily Streamflow Prediction Using Optimally Pruned Extreme Learning Machine", Journal of Hydrology, vol. 577, p. 123981, 2019.
  • D. E. Rumelhart, G.E. Hinton, R. J. Williams “Learning Internal Representations By Error Propagation”, Parallel Distributed Processing.
  • M. Atiquzzaman, J. Kandasamy, "Robustness of Extreme Learning Machine İn The Prediction of Hydrological Flow Series", Computers & Geosciences, vol. 120, pp. 105-114, 2018.
  • D. Kukolj, "Design of Adaptive Takagi–Sugeno–Kang Fuzzy Models", Applied Soft Computing, vol. 2, no. 2, pp. 89-103, 2002.
  • Jang, "ANFIS: Adaptive-Network-Based Fuzzy Inference System", IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, 1993.
  • A. Mosavi, P. Ozturk, K. Chau, "Flood Prediction Using Machine Learning Models: Literature Review", Water, vol. 10, no. 11, p. 1536, 2018.
  • H. B. Demuth, “Fuzzy Logic Toolbox for Use With MATLAB”, User’s Guide Version 4. MA, 2000.
  • Z. Jin, G. Zhou, D. Gao, Y. Zhang, "EEG Classification Using Sparse Bayesian Extreme Learning Machine For Brain–Computer Interface", Neural Computing and Applications, vol. 32, no. 11, pp. 6601-6609, 2018.
  • E. R. David, L. M. James, “Learning Internal Representations By Error Propagation. In Parallel Distributed Processing: Explorations in The Microstructure Of Cognition: Foundations”, MITP, pp. 318-362,1987.
  • A. Khashman, "Application of An Emotional Neural Network to Facial Recognition", Neural Computing and Applications, vol. 18, no. 4, pp. 309-320, 2008.
  • A. Khashman, “Blood Cell Identification Using Emotional Neural Networks”, Journal of Information Science & Engineering, 2009, vol. 25,6.
  • V. N. Vapnik, “Statistical Learning Theory”, New York: Wiley, 1998, ISBN: 978-0-471-03003-4
  • C. J. Burges, “A Tutorial on Support Vector Machines for Pattern Recognition”. Data mining and knowledge discovery, 1998, Vol. 2(2), pp. 121-167.
  • F. Fotovatikhah, M. Herrera, S. Shamshirband, K. Chau, S. Faizollahzadeh Ardabili, M. Piran, "Survey of Computational İntelligence As Basis to Big Flood Management: Challenges, Research Directions And Future Work", Engineering Applications of Computational Fluid Mechanics, vol. 12, no. 1, pp. 411-437, 2018.
  • A. Mehr, A. Gandomi, "MSGP-LASSO: An Improved Multi-Stage Genetic Programming Model for Streamflow Prediction", Information Sciences, vol. 561, pp. 181-195, 2021.
  • U. Okkan, Z. Serbes, "Rainfall-Runoff Modeling Using Least Squares Support Vector Machines", Environmetrics, vol. 23, no. 6, pp. 549-564, 2012.
  • N. Ceryan, U. Okkan, P. Samui, S. Ceryan, "Modeling of Tensile Strength of Rocks Materials Based on Support Vector Machines Approaches", International Journal for Numerical and Analytical Methods in Geomechanics, vol. 37, no. 16, pp. 2655-2670, 2012.
  • R. Chanklan, N. Kaoungku, K. Suksut, K. Kerdprasop, N. Kerdprasop, "Runoff Prediction With a Combined Artificial Neural Network and Support Vector Regression", International Journal of Machine Learning and Computing, vol. 8, no. 1, pp. 39-43, 2018.
  • S. Shamshirband, S. Hashemi, H. Salimi, S. Samadianfard, E. Asadi, S. Shadkani, K. Kargar, A. Mosavi, N. Nabipour, k. W. Chau, "Predicting Standardized Streamflow Index For Hydrological Drought Using Machine Learning Models", Engineering Applications of Computational Fluid Mechanics, vol. 14, no. 1, pp. 339-350, 2020.
  • C. Wu, K. Chau, "Prediction of Rainfall Time Series Using Modular Soft Computing Methods", Engineering Applications of Artificial Intelligence, vol. 26, no. 3, pp. 997-1007, 2013.
  • K. P. Murphy, “Machine Learning: A Probabilistic Perspective”. MIT press, 2012.
  • R. Richardson, M. Osborne, D. Howey, "Gaussian Process Regression for Forecasting Battery State of Health", Journal of Power Sources, vol. 357, pp. 209-219, 2017.
  • Z. Zhang, Q. Zhang, V. Singh, "Univariate Streamflow Forecasting Using Commonly Used Data-Driven Models: Literature Review and Case Study", Hydrological Sciences Journal, vol. 63, no. 7, pp. 1091-1111, 2018.
  • A. Danandeh Mehr, V. Nourani, E. Kahya, B. Hrnjica, A. Sattar, Z. Yaseen, "Genetic Programming in Water Resources Engineering: A State-Of-The-Art Review", Journal of Hydrology, vol. 566, pp. 643-667, 2018.
  • R. Taormina, K. Chau, "ANN-Based İnterval Forecasting of Streamflow Discharges Using The LUBE Method and MOFIPS", Engineering Applications of Artificial Intelligence, vol. 45, pp. 429-440, 2015.
  • Y. Zhang, H. Yang, H. Cui, Q. Chen, "Comparison of The Ability of ARIMA, WNN and SVM Models for Drought Forecasting in The Sanjiang Plain, China", Natural Resources Research, vol. 29, no. 2, pp. 1447-1464, 2019.
  • Z. Chen, M. Kavvas, N. Ohara, M. Anderson, J. Yoon, "Coupled Regional Hydroclimate Model and Its Application to the Tigris-Euphrates Basin", Journal of Hydrologic Engineering, vol. 16, no. 12, pp. 1059-1070, 2011.
  • DSI:https://web.archive.org/web/200508130 23730/http://www.dsi.gov.tr/topraksu.htm ((2020, 07 16).
  • C. C. Aggarwal, “Data Mining: The Textbook”, Springer, New York, 2015.
  • G. B. Guacho, S. Abdali, N. Shah, E. E. Papalexakis, “Semi-Supervised Content-Based Detection of Misinformation Via Tensor Embeddings”, In 2018 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), 2018, pp. 322-325. IEEE.
  • Y. Khan, S. S Chai, “Ensemble of ANN and ANFIS for Water Quality Prediction And Analysis-A Data Driven Approach”, Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 2017, Vol. 9(2-9), pp. 117-122.
  • S. Samantaray, P. Dash, "Decision Tree Based Discrimination Between Inrush Currents And Internal Faults İn Power Transformer", International Journal of Electrical Power & Energy Systems, vol. 33, no. 4, pp. 1043-1048, 2011.
Toplam 43 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İnşaat Mühendisliği
Bölüm Araştırma Makalesi
Yazarlar

Sefa Nur Yesilyurt 0000-0001-6173-3038

Hüseyin Yildirim Dalkılıç 0000-0002-4405-9341

Pijush Samuı 0000-0003-2906-6479

Erken Görünüm Tarihi 22 Haziran 2023
Yayımlanma Tarihi 30 Haziran 2023
Gönderilme Tarihi 10 Ağustos 2022
Kabul Tarihi 22 Mart 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Yesilyurt, S. N., Dalkılıç, H. Y., & Samuı, P. (2023). Application of Soft Computing Techniques in River Flow Modeling. Sakarya University Journal of Science, 27(3), 634-642. https://doi.org/10.16984/saufenbilder.1152982
AMA Yesilyurt SN, Dalkılıç HY, Samuı P. Application of Soft Computing Techniques in River Flow Modeling. SAUJS. Haziran 2023;27(3):634-642. doi:10.16984/saufenbilder.1152982
Chicago Yesilyurt, Sefa Nur, Hüseyin Yildirim Dalkılıç, ve Pijush Samuı. “Application of Soft Computing Techniques in River Flow Modeling”. Sakarya University Journal of Science 27, sy. 3 (Haziran 2023): 634-42. https://doi.org/10.16984/saufenbilder.1152982.
EndNote Yesilyurt SN, Dalkılıç HY, Samuı P (01 Haziran 2023) Application of Soft Computing Techniques in River Flow Modeling. Sakarya University Journal of Science 27 3 634–642.
IEEE S. N. Yesilyurt, H. Y. Dalkılıç, ve P. Samuı, “Application of Soft Computing Techniques in River Flow Modeling”, SAUJS, c. 27, sy. 3, ss. 634–642, 2023, doi: 10.16984/saufenbilder.1152982.
ISNAD Yesilyurt, Sefa Nur vd. “Application of Soft Computing Techniques in River Flow Modeling”. Sakarya University Journal of Science 27/3 (Haziran 2023), 634-642. https://doi.org/10.16984/saufenbilder.1152982.
JAMA Yesilyurt SN, Dalkılıç HY, Samuı P. Application of Soft Computing Techniques in River Flow Modeling. SAUJS. 2023;27:634–642.
MLA Yesilyurt, Sefa Nur vd. “Application of Soft Computing Techniques in River Flow Modeling”. Sakarya University Journal of Science, c. 27, sy. 3, 2023, ss. 634-42, doi:10.16984/saufenbilder.1152982.
Vancouver Yesilyurt SN, Dalkılıç HY, Samuı P. Application of Soft Computing Techniques in River Flow Modeling. SAUJS. 2023;27(3):634-42.

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