ارزیابی کارایی درخت تصمیم در ترکیب با تبدیل موجک به‌منظور پیش‌بینی نوسانات سطح آب‌زیرزمینی (مطالعه موردی: دشت کرمان- باغین)

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آب، دانشکده مهندسی عمران و نقشه‌برداری، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

2 گروه مهندسی آب، دانشکده مهندسی عمران و نقشه برداری، دانشگاه تحصیلات تکمیلی صنعتی و فناوری پیشرفته، کرمان، ایران

3 عضو هیئت علمی گروه مهندسی آب، دانشکده کشاورزی، دانشگاه جیرفت، جیرفت، ایران.

چکیده

پایش سطوح آب‌های زیرزمینی و برآورد دقیق نوسانات آن‌ در دوره‌های پیش‌رو به‌خصوص در مناطق خشک و نیمه‌خشک ضرورت دارد. با توجه به قابلیت مدل‌های مبتنی بر هوش مصنوعی در مدل‌سازی پدیده‌های هیدرولوژیکی، در این پژوهش از مدل‌ درخت تصمیم M5P در ترکیب با تبدیل موجک به‌منظور پیش‌بینی نوسانات سطح آب‌زیرزمینی دشت کرمان- باغین استفاده شده است. جهت توسعه مدل ترکیبی موجک-درخت تصمیم (W-M5P)، خروجی‌های تبدیل موجک به‌عنوان ورودی بر M5P اعمال می‌شوند. برای ارزیابی کارایی مدل ترکیبی در مقایسه با مدل منفرد، از چندین معیار از جمله ضریب همبستگی (R)، شاخص توافق (Ia) و شاخص پراکندگی (SI) استفاده شد.‌ نتایج نشان داد، به‌رغم اینکه ورودی مدل ترکیبی، صرفاً داده‌های هواشناسی ایستگاه سینوپتیک بوده و از سطح آب در دوره‌های پیشین استفاده نشده، با این وجود WM5P کارایی بالایی در مدل‌سازی نوسانات سطح آب‌زیرزمینی در مقایسه با مدل منفرد ارائه نموده است. به‌گونه‌ای که مدلWM5P برای افق پیش‌بینی سه ماه با موجکCoif4 و سطح تجزیه شش، مقدار SI را از 6394/0 به 0181/0 کاهش و هم‌زمان Ia را از 6898/0 به 9998/0 افزایش داده است. براین اساس، انتخاب موجک کویفلت با مرتبه 4 و سطوح تجزیه 5 و 6 در مدل ترکیبی، کاراترین مدل در برآورد سطح آب‌زیرزمینی دشت کرمان-باغین می‌باشد.

کلیدواژه‌ها

عنوان مقاله [English]

Evaluating Decision Tree Efficiency in Combination with Wavelet Transform to Predict Groundwater Level Fluctuation

نویسندگان [English]

  • Hanieh Rostaminezhad Dolatabad 1
  • Sajad Shahabi 2
  • Mohamad Reza Madadi 3
1 Department of Water Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran
2 Department of Water Engineering, Faculty of Civil and Surveying Engineering, , Graduate University of Advanced Technology, Kerman, Iran
3 Assistant Professor, Department of Water Engineering, Faculty of Agriculture, University of Jiroft, Jiroft, Iran.
چکیده [English]

Accurate monitoring of groundwater levels and estimating their fluctuations in the future is of importance, especially in arid and semi-arid areas. regarding the high capabilities of AI-based models in the modeling of hydrologic phenomena, this research used the MP5 decision tree, in combination with the wavelet transform, to predict groundwater level fluctuations of the Kerman-Bagheyn plane. To develop the wavelet-decision tree (W-M5p) hybrid model, the wavelet transformation outputs were exported to the MP5 as inputs. Several statistical criteria, including coefficient of correlation (R), agreement index (Ia), and scattering index (SI), were used to evaluate the performance of the hybrid model compared to the single model. The results indicated that, even when the inputs of the hybrid model includes only the meteorological data from a synoptic station (the water level of previous periods were not used in the analysis), that the performance of the WM5P was superior to the single model in the prediction of groundwater fluctuations. The WM5P model with three months of forecast horizon with the Coif4 wavelet and decomposition level of 6 reduced the SI value from 0.6394 to 0.0181 and, at the same time, increased the Ia from 0.6898 to 0.9998. Consequently, the Coiflet4 with decomposition levels of 5 and 6 was the most efficient wavelet in the hybrid model for reliable estimation of the Kerman-Bagheyn plane groundwater level.

کلیدواژه‌ها [English]

  • Data preprocessing
  • Water table
  • Modeling
  • Artificial intelligence
  • Machine lrarning

مراجع

Abdolahzadeh, M. and Schmalz B. 2022. Assessment of wavelet-SVR and wavelet-GP models in predicting the groundwater level using areal precipitation and consumption data. Hydrological Sciences Journal. 67(7): 1026-1039.
Ardabili, S., Mosavi, A. and Várkonyi-Kóczy AR. 2020. Advances in machine learning modeling reviewing hybrid and ensemble methods. InInternational Conference on Global Research and Education. Springer, Cham: 215-227.
Azarpira, F. and Shahabi, S. 2021. Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables. Journal of Hydroinformatics. 23(6): 1165-1181.
Bahmani, R. and Ouarda, T. B. 2021. Groundwater level modeling with hybrid artificial intelligence techniques. Journal of Hydrology. 595: 125659.
Bahmani, R., Solgi, A. and Ouarda, T. B. 2020. Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform. Hydrological Sciences Journal. 65(8): 1430-1442.
Daubechies, I. 1990. The wavelet transform, time-frequency localization and signal analysis. IEEE transactions on information theory. 36(5):961-1005.
Grossmann, A. and Morlet, J. 1984. Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM journal on mathematical analysis. 15(4): 723-736.
Huang M. and Tian, Y. 2015. Prediction of groundwater level for sustainable water management in an arid basin using data-driven models. International Conference on Sustainable Energy and Environmental Engineering 2015 Oct. Atlantis Press: 134-137.
Javadinejad, S., Dara, R. and Jafary F. 2020. Modelling groundwater level fluctuation in an Indian coastal aquifer. Water SA. 46(4): 665-671.
Lallahem, S., Mania, J., Hani, A. and Najjar, Y. 2005. On the use of neural networks to evaluate groundwater levels in fractured media. Journal of hydrology. 307(1-4): 92-111.
Milan, S. G., Roozbahani, A. and Banihabib, M. E. 2018. Fuzzy optimization model and fuzzy inference system for conjunctive use of surface and groundwater resources. Journal of hydrology. 566: 421-434.
Kardan Moghaddam, H., Ghordoyee, Milan, S., Kayhomayoon, Z. and Arya Azar, N. 2021. The prediction of aquifer groundwater level based on spatial clustering approach using machine learning. Environmental Monitoring and Assessment. 193(4): 1-20.
Misiti, M., Misiti, Y., Oppenheim, G. and Poggi, J.-M. 1996. Wavelet toolbox. The MathWorks Inc., Natick, MA. 15: 21.
Nalarajan, N. A. and Mohandas, C. 2015. Groundwater level prediction using M5 model trees. Journal of the Institution of Engineers (India): Series A. 96(1): 57-62.
Nourani, V., Baghana, A. H., Adamowski, J. and Kisi, O. 2014. Applications of hybrid wavelet–artificial intelligence models in hydrology: a review. Journal of Hydrology. 514: 358-377.
Nourani, V., Komasi, M. and Mano, A. 2009. A multivariate ANN-wavelet approach for rainfall–runoff modeling. Water resources management. 23(14): 2877-2894.
 
Pham, Q. B., Kumar, M., Di Nunno, F., Elbeltagi, A., Granata, F., Islam, A. R. M. T., Talukdar, S., Nguyen, X. C., Ahmed, A.N. and Anh, D. T. 2022. Groundwater level prediction using machine learning algorithms in a drought-prone area. Neural Computing and Applications. 34(13): 10751-10773.
Quinlan, J. R. 1992. Learning with continuous classes. 5th Australian joint conference on artificial intelligence, World Scientific.
Rajaee, T., Ebrahimi, H. and Nourani, V. 2019. A review of the artificial intelligence methods in groundwater level modeling. Journal of hydrology. 572: 336-351.
Rakhshandehroo, G. R., Vaghefi, M. and Aghbolaghi, M. A. 2012. Forecasting groundwater level in Shiraz plain using artificial neural networks. Arabian Journal for Science and Engineering. 37(7): 1871-1883.
Rezaie-balf, M., Naganna, S. R., Ghaemi, A. and Deka, P. C. 2017. Wavelet coupled MARS and M5 Model Tree approaches for groundwater level forecasting. Journal of hydrology. 553: 356-373.
Samani, S., Vadiati, M., Nejatijahromi, Z., Etebari, B., and Kisi, O. 2022. Groundwater level response identification by hybrid wavelet–machine learning conjunction models using meteorological data. Environmental Science and Pollution Research, 1-22.
Sattari, M. T., Mirabbasi, R., Sushab, R. S. and Abraham, J. 2018. Prediction of groundwater level in Ardebil plain using support vector regression and M5 tree model. Groundwater. 56(4): 636-646.
Sharafati, A., Asadollah, S. B. H. S. and Neshat, A. 2020. A new artificial intelligence strategy for predicting the groundwater level over the Rafsanjan aquifer in Iran. Journal of Hydrology. 591: 125468.
Shariat, R., Roozbahani, A. and Ebrahimian, A. 2019. Risk analysis of urban stormwater infrastructure systems using fuzzy spatial multi-criteria decision making. Science of the Total Environment. 647: 1468-1477.
Shahabi, S., Khanjani, M. J. and Hessami Kermani, M. 2016. Hybrid wavelet-GMDH model to forecast significant wave height. Water Science and Technology: Water Supply. 16(2): 453-459.
Sreekanth, P., Geethanjali, N., Sreedevi, P., Ahmed, S., Kumar, N. R. and Jayanthi, P. K. 2009. Forecasting groundwater level using artificial neural networks. Current science: 933-939.
Solgi, A., Pourhaghi, A., Bahmani, R. and Zarei, H. 2017. Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation. Journal of Earth System Science. 126(5): 1-17.
Solomatine, D. P. and Xue, Y. 2004. M5 Model Trees and Neural Networks: Application to Flood Forecasting in the Upper Reach of the Huai River in China. Journal of Hydrologic Engineering. 9(6): 491-501.
Tao, H., Hameed, M.M., Marhoon, H.A., Zounemat-Kermani, M., Salim, H., Sungwon, K., Sulaiman, SO., Tan, M.L., Sa’adi, Z., Mehr, AD., Allawi, MF. 2022. Groundwater level prediction using machine learning models: a comprehensive review. Neurocomputing.
Üneş, F., Maruf, A. G. and Taşar, B. 2019. Ground Water Level Estimation for Dörtyol region in HATAY. International Journal of Environment, Agriculture and Biotechnology. 4(3): 859-864.
Vadiati, M., Rajabi Yami, Z., Eskandari, E., Nakhaei, M., and Kisi, O. 2022. Application of artificial intelligence models for prediction of groundwater level fluctuations: Case study (Tehran-Karaj alluvial aquifer). Environmental Monitoring and Assessment, 194(9), 619.
Wang, Y. and Witten, I. H. 1996. Induction of model trees for predicting continuous classes. Computer Science Working Papers.
Wei, A., Chen, Y., Li, D., Zhang, X., Wu, T., and Li, H. 2022. Prediction of groundwater level using the hybrid model combining wavelet transform and machine learning algorithms. Earth Science Informatics. 15(3): 1951-1962.
Yue, Q., Zhang, F. and Guo, P. 2018. Optimization-based agricultural water-saving potential analysis in Minqin County, Gansu Province China. Water. 10(9): 1125.
Zyoud, S. H., Kaufmann, L. G., Shaheen, H., Samhan, S. and Fuchs-Hanusch, D. 2016. A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS. Expert Systems with Applications. 61: 86-105.
نشریه آبیاری و زهکشی ایران
دوره 17، شماره 3 - شماره پیاپی 99
مرداد و شهریور 1402
صفحه 413-427

فایل ها

هم رسانی

ارجاع به این مقاله

آمار