مدل‌سازی هیدرولیکی منابع آب با استفاده از تکنیک‌های یادگیری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 سازمان برنامه و بودجه، معاونت فنی

2 دانشجوی دکترای تخصصی مهندسی برق قدرت، گروه مهندسی برق، دانشگاه لرستان، خرم آباد، ایران

3 دانشیار گروه مهندسی آب، واحد کرمانشاه، دانشگاه آزاد اسلامی، کرمانشاه،ایران

چکیده

تحلیل کمی و کیفی منابع آب امروزه به یکی از موضوعات مهم در تحقیقات منابع آب تبدیل شده است. در این تحقیق از داده­کاوی، تکنیک­های هوش مصنوعی و ریاضی برای شبیه­سازی رفتار آب و تخمین تغییرات پارامتریک آن استفاده شده است. نام مدل­های بکار گرفته شده عبارتند از: مدل ماشین یادگیری نیرومند خودتطبیق SAELM، حداقل مربعات ماشین بردار پشتیبان LSSVM، مدل شبکه­های عصبی نروفازی ANFIS و مدل آماری رگرسیون خطی چندگانه MLR که برای تخمین پارامترهای هیدروژئولوژیکی استفاده شده است. همچنین برای ارزیابی عملکرد مدل­ها، در قالب 5 رویکرد دقت مدل­ها بررسی گردید. نتایج تحقیق نشان داد که براساس نمودارهای شبیه­سازی و همبستگی مدل SAELM برترین مدل بود. براساس شاخص­های ارزیابی دقت، مدل SAELM با شاخص­های RMSE و MAPE و R به ترتیب برابر با 1545/0، 0070/0 و 9979/0 دارای بالاترین دقت در تخمین پارامترهای هیدروژئولوژیکی بود. بر اساس تحلیل عدم قطعیت ویلسون (Wilson Score method) عملکرد مدل برتر (SAELM) دست پایین (Underestimated) برآورد گردید. همچنین براساس نمودارهای نسبت اختلاف خطا، دقیق­ترین نتایج مربوط به مدل SAELM بود. در پایان با استفاده از نمودارهای توزیع خطا کمترین میزان خطا به مدل SAELM اختصاص یافت.

کلیدواژه‌ها


عنوان مقاله [English]

Hydraulic Modeling of the Water Resources using Learning Techniques

نویسندگان [English]

  • Mojtaba Poursaeid 1
  • Amirhossain Poursaeid 2
  • saeid shabanlou 3
1 Deputy of Technical and Engineering, Plan and Budget Organization, Khorramabad, Iran
2 Ph.D student, Department of Electrical Engineering, Faculty of Tecnnical and Engineering, Lorestan University, KHorramabad, Iran
3 Department of Water Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran.
چکیده [English]

Quantitative and qualitative analysis of water resources has become one of the most widely used topics in water resources research today. In this research, data mining, artificial intelligence, mathematical techniques have been used to simulate water behavior and estimate its parameters changes. The models used to estimate hydrogeological parameters are Self-adaptive Extreme learning machine (SAELM), Least square support vector machine (LSSVM), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multiple linear regression (MLR) models. Also, to evaluate the performance of these models, the accuracy of the models was assessed in the form of 5 approaches. The results showed that the SAELM model was the best model based on the simulation and correlation diagrams. Based on accuracy evaluation indices, the SAELM model with RMSE, MAPE and, R indices equal to 0.1545, 0.0070, and 0.9979, respectively, had the highest accuracy in hydrogeological parameters prediction. Based on Uncertainty Analysis by the Wilson Score method, the performance of the top model (SAELM) was estimated to be underestimated. Also, based on the error ratio diagrams, the most accurate results were related to the SAELM model. Finally, the SAELM model was assigned the lowest error rate using the error distribution diagrams.

کلیدواژه‌ها [English]

  • Self Adaptive Extreme Learning Machine
  • Least Square Support Vector Machine
  • Adaptive neuro fuzzy inference system
  • Multiple Linear Regression
  • Uncertainty analysis
Adhikary, S., & Gupta, A. (2011). MODELING GROUNDWATER FLOW AND SALINITY INTRUSION BY ADVECTIVE TRANSPORT IN THE REGIONAL UNCONFINED AQUIFER OF SOUTHWEST BANGLADESH.
Arora, S., & Keshari, A. K. (2021). ANFIS-ARIMA modelling for scheming re-aeration of hydrologically altered rivers. Journal of Hydrology, 601, 126635. https://doi.org/10.1016/J.JHYDROL.2021.126635.
Azimi, S., & Azhdary Moghaddam, M. (2020). Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index. Water Resources Management 2020 34:4, 34(4), 1369–1405. https://doi.org/10.1007/S11269-020-02507-6.
Banerjee, P., Singh, V. S., Chatttopadhyay, K., Chandra, P. C., & Singh, B. (2011). Artificial neural network model as a potential alternative for groundwater salinity forecasting. Journal of Hydrology, 398(3–4), 212–220. https://doi.org/10.1016/J.JHYDROL.2010.12.016
Çamdevýren, H., Demýr, N., Kanik, A., & Keskýn, S. (2005). Use of principal component scores in multiple linear regression models for prediction of Chlorophyll-a in reservoirs. Ecological Modelling, 181(4), 581–589. https://doi.org/10.1016/J.ECOLMODEL.2004.06.043
Campbell, C. (2002). Kernel methods: A survey of current techniques. Neurocomputing, 48(1–4), 63–84. https://doi.org/10.1016/S0925-2312(01)00643-9
Che Nordin, N. F., Mohd, N. S., Koting, S., Ismail, Z., Sherif, M., & El-Shafie, A. (2021). Groundwater quality forecasting modelling using artificial intelligence: A review. Groundwater for Sustainable Development, 14, 100643. https://doi.org/10.1016/J.GSD.2021.100643
Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., Liu, F., Zuo, M., Zou, X., Wang, J., Zhang, Y., Chen, D., Chen, X., Deng, Y., & Ren, H. (2020). Comparative analysis of surface water quality prediction performance and identification of key water parameters using different machine learning models based on big data. Water Research, 171, 115454. https://doi.org/10.1016/J.WATRES.2019.115454
Cristianini, N., & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. https://doi.org/10.1017/CBO9780511801389
Cuest Cordoba, G. A., Tuhovčák, L., & Tauš, M. (2014). Using Artificial Neural Network Models to Assess Water Quality in Water Distribution Networks. Procedia Engineering, 70, 399–408. https://doi.org/10.1016/J.PROENG.2014.02.045
El-Shafie, A., Taha, M. R., & Noureldin, A. (2006). A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resources Management 2006 21:3, 21(3), 533–556. https://doi.org/10.1007/S11269-006-9027-1
Elkiran, G., Nourani, V., & Abba, S. I. (2019). Multi-step ahead modelling of river water quality parameters using ensemble artificial intelligence-based approach. Journal of Hydrology, 577, 123962. https://doi.org/10.1016/J.JHYDROL.2019.123962.
Guneshwor, L., Eldho, T. I., & Vinod Kumar, A. (2018). Identification of Groundwater Contamination Sources Using Meshfree RPCM Simulation and Particle Swarm Optimization. Water Resources Management 2018 32:4, 32(4), 1517–1538. https://doi.org/10.1007/S11269-017-1885-1
Huang, G. Bin, Zhou, H., Ding, X., & Zhang, R. (2012). Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(2), 513–529. https://doi.org/10.1109/TSMCB.2011.2168604
Huang, G. Bin, Zhu, Q. Y., & Siew, C. K. (2004). Extreme learning machine: A new learning scheme of feedforward neural networks. IEEE International Conference on Neural Networks - Conference Proceedings, 2, 985–990. https://doi.org/10.1109/IJCNN.2004.1380068
Jamei, M., Ahmadianfar, I., Chu, X., & Yaseen, Z. M. (2020). Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: New approach. Journal of Hydrology, 589, 125335. https://doi.org/10.1016/J.JHYDROL.2020.125335
Kadkhodazadeh, M., & Farzin, S. (2021). A Novel LSSVM Model Integrated with GBO Algorithm to Assessment of Water Quality Parameters. https://doi.org/10.21203/RS.3.RS-465707/V1
Liang, N. Y., Huang, G. Bin, Saratchandran, P., & Sundararajan, N. (2006). A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Transactions on Neural Networks, 17(6), 1411–1423. https://doi.org/10.1109/TNN.2006.880583
Majumder, P., & Eldho, T. I. (2020). Artificial Neural Network and Grey Wolf Optimizer Based Surrogate Simulation-Optimization Model for Groundwater Remediation. Water Resources Management 2020 34:2, 34(2), 763–783. https://doi.org/10.1007/S11269-019-02472-9
Mustapha, A., & Abdu, A. (2012). Application of Principal Component Analysis & Multiple Regression Models in Surface Water Quality Assessment. Journal of Environment and Earth Science, 2(2), 16–23. https://www.iiste.org/Journals/index.php/JEES/article/view/1516
Poursaeid, M., Mastouri, R., Shabanlou, S., & Najarchi, M. (2020). Estimation of total dissolved solids, electrical conductivity, salinity and groundwater levels using novel learning machines. Environmental Earth Sciences 2020 79:19, 79(19), 1–25. https://doi.org/10.1007/S12665-020-09190-1
Poursaeid, M., Mastouri, R., Shabanlou, S., & Najarchi, M. (2021). Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks. Water and Environment Journal, 35(1), 67–83. https://doi.org/10.1111/WEJ.12595
Rajaee, T., Khani, S., & Ravansalar, M. (2020). Artificial intelligence-based single and hybrid models for prediction of water quality in rivers: A review. Chemometrics and Intelligent Laboratory Systems, 200, 103978. https://doi.org/10.1016/J.CHEMOLAB.2020.103978
Sapankevych, N., & Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2), 24–38. https://doi.org/10.1109/MCI.2009.932254
Schölkopf, B., & Smola, A. J. (2002). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond Adaptive computation and machine learning. 626.
Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B., & Vandewalle, J. (2002). Least Squares Support Vector Machines. https://doi.org/10.1142/5089
Vaheddoost, B., & Aksoy, H. (2018). Interaction of groundwater with Lake Urmia in Iran. Hydrological Processes, 32(21), 3283–3295. https://doi.org/10.1002/hyp.13263
Valyon, J. Horvath, G. (2007). (PDF) Extended Least Squares LS-SVM. World Academy of Science, Engineering and Technology, 36. https://www.researchgate.net/publication/242532586_Extended_Least_Squares_LS-SVM
Wang, P., Yao, J., Wang, G., Hao, F., Shrestha, S., Xue, B., Xie, G., & Peng, Y. (2019). Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants. Science of The Total Environment, 693, 133440. https://doi.org/10.1016/J.SCITOTENV.2019.07.246
Zhang, Yanyang, Gao, X., Smith, K., Inial, G., Liu, S., Conil, L. B., & Pan, B. (2019). Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network. Water Research, 164, 114888. https://doi.org/10.1016/J.WATRES.2019.114888
Zhang, Yishan, Wu, L., Deng, L., & Ouyang, B. (2021). Retrieval of Water Quality Parameters from Hyperspectral Images Using a Hybrid Feedback Deep Factorization Machine Model. Water Research, 117618. https://doi.org/10.1016/J.WATRES.2021.117618