بهبود عملکرد مدل‌های هوشمند بر پایه الگوریتم موجک و تبدیلات لگاریتمی در تخمین بار رسوب معلق

نوع مقاله : مقالات پژوهشی

نویسندگان

1 دانشگاه علم و صنعت ایران

2 دانشگاه سمنان

3 دانشگاه تبریز

چکیده

یکی از دلایل پیچیدگی تخمین و پیش‌بینی پدیده‌های هیدرولوژیکی و به خصوص سری‌های زمانی وجود ویژگی‌هایی نظیر روند، نویز و نوسانات با فرکانس بالا در آن ها می‌باشد که با استفاده از پیش‌پردازش داده‌ها به وسیله نویززدایی و تبدیلات لگاریتمی، می‌توان برخی عوامل پنهان و تاثیرگذار در این پیچیدگی را شناسایی و حذف نمود و یا درک این ویژگی‌ها را برای مدل‌های پیش‌بینی ساده‌تر نمود. در این تحقیق با استفاده ازدو مدل هوشمند برنامه‌ریزی بیان ژن و شبکه عصبی مصنوعی تخمین بار رسوب معلق مورد بررسی قرار می‌گیرد، سپس میزان تاثیر دو رویکرد نویززدایی و تبدیلات لگاریتمی به عنوان پیش‌پردازشگر، در بهبود نتایج مورد ارزیابی و مقایسه قرار می‌گیرد. به منظور نویززدایی از تبدیلات موجک استفاده شده است. نتایج تحقیق نشان می‌دهد پس از نویززدایی، معیار نش-ساتکلیف در شبکه عصبی مصنوعی و برنامه‌ریزی بیان ژن به ترتیب 15/0 و 14/0 افزایش داشته و مقدار جذر میانگین مجذورات خطانیز در شبکه عصبی مصنوعی از 24/199 به 17/141 میلی‌گرم بر لیتر و در برنامه‌ریزی بیان ژن از 84/234 به 89/193 میلی‌گرم بر لیتر کاهش یافته است. تاثیر رویکرد تبدیلات لگاریتمی نیز در بهبود نتایج شبکه عصبی مصنوعی تا حدود زیادی مشابه با رویکرد نویززدایی می‌باشد. در حالی‌که در برنامه‌ریزی بیان ژن تاثیر نا‌مطلوب داشته و پس از تبدیلات لگاریتمی Ln و Log، معیار نش-ساتکلیف از 57/0 به ترتیب به 31/0 و 21/0 کاهش یافته است و مقدار جذر میانگین مجذورات خطا نیز از 84/234 میلی‌گرم بر لیتر به ترتیب به 41/298 میلی‌گرم بر لیتر و 72/318 میلی‌گرم بر لیتر افزایش یافته است.

کلیدواژه‌ها


عنوان مقاله [English]

Intelligent Models Performance Improvement Based on Wavelet Algorithm and Logarithmic Transformations in Suspended Sediment Estimation

نویسندگان [English]

  • Reza Hajiabadi 1
  • S. Farzin 2
  • Y. Hassanzadeh 3
1 Iran University of Science and Technology
2 University of Semnan
3 University of Tabriz
چکیده [English]

Introduction One reason for the complexity of hydrological phenomena prediction, especially time series is existence of features such as trend, noise and high-frequency oscillations. These complex features, especially noise, can be detected or removed by preprocessing. Appropriate preprocessing causes estimation of these phenomena become easier. Preprocessing in the data driven models such as artificial neural network, gene expression programming, support vector machine, is more effective because the quality of data in these models is important. Present study, by considering diagnosing and data transformation as two different preprocessing, tries to improve the results of intelligent models. In this study two different intelligent models, Artificial Neural Network and Gene Expression Programming, are applied to estimation of daily suspended sediment load. Wavelet transforms and logarithmic transformation is used for diagnosing and data transformation, respectively. Finally, the impacts of preprocessing on the results of intelligent models are evaluated.
Materials and Methods In this study, Gene Expression Programming and Artificial Neural Network are used as intelligent models for suspended sediment load estimation, then the impacts of diagnosing and logarithmic transformations approaches as data preprocessor are evaluated and compared to the result improvement. Two different logarithmic transforms are considered in this research, LN and LOG. Wavelet transformation is used to time series denoising. In order to denoising by wavelet transforms, first, time series can be decomposed at one level (Approximation part and detail part) and second, high-frequency part (detail) will be removed as noise. According to the ability of gene expression programming and artificial neural network to analysis nonlinear systems; daily values of suspended sediment load of the Skunk River in USA, during a 5-year period, are investigated and then estimated.4 years of data are applied to models training and one year is estimated by each model. Accuracy of models is evaluated by three indexes. These three indexes are mean absolute error (MAE), root mean squared error (RMSE) and Nash-Sutcliffecoefficient (NS).
Results and Discussion In order to suspended sediment load estimation by intelligent models, different input combination for model training evaluated. Then the best combination of input for each intelligent model is determined and preprocessing is done only for the best combination. Two logarithmic transforms, LN and LOG, considered to data transformation. Daubechies wavelet family is used as wavelet transforms. Results indicate that diagnosing causes Nash Sutcliffe criteria in ANN and GEPincreases 0.15 and 0.14, respectively. Furthermore, RMSE value has been reduced from 199.24 to 141.17 (mg/lit) in ANN and from 234.84 to 193.89 (mg/lit) in GEP. The impact of the logarithmic transformation approach on the ANN result improvement is similar to diagnosing approach. While the logarithmic transformation approach has an adverse impact on GEP. Nash Sutcliffe criteria, after Ln and Log transformations as preprocessing in GEP model, has been reduced from 0.57 to 0.31 and 0.21, respectively, and RMSE value increases from 234.84 to 298.41 (mg/lit) and 318.72 (mg/lit) respectively. Results show that data denoising by wavelet transform is effective for improvement of two intelligent model accuracy, while data transformation by logarithmic transformation causes improvement only in artificial neural network. Results of the ANN model reveal that data transformation by LN transfer is better than LOG transfer, however both transfer function cause improvement in ANN results. Also denoising by different wavelet transforms (Daubechies family) indicates that in ANN models the wavelet function Db2 is more effective and causes more improvement while on GEP models the wavelet function Db1 (Harr) is better.
Conclusions: In the present study, two different intelligent models, Gene Expression Programming and Artificial Neural Network, have been considered to estimation of daily suspended sediment load in the Skunk river in the USA. Also, two different procedures, denoising and data transformation have been used as preprocessing to improve results of intelligent models. Wavelet transforms are used for diagnosing and logarithmic transformations are used for data transformation. The results of this research indicate that data denoising by wavelet transforms is effective for improvement of two intelligent model accuracy, while data transformation by logarithmic transformation causes improvement only in artificial neural network. Data transformation by logarithmic transforms not only does not improve results of GEP model, but also reduces GEP accuracy.

کلیدواژه‌ها [English]

  • Artificial neural network
  • Gene Expression Programming
  • Logarithmic transformations
  • Suspended sediment load
  • wavelet Transformation
1- Alp M., and Cigizoglu, H.K. 2007. Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling & Software, 22: 2-13.
2- Aqil M., Kita I., Yano A., and Nishiyama, S. 2007. A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. Journal of Hydrology, 337: 22-34.
3- Aytek A., and Kisi, O. 2008. A genetic programming approach to suspended sediment modelling. Journal of Hydrology, 351: 288-298.
4- Danandehmehr A., Oliaie E., Ghorbani M.A. 2010. Suspended sediment load prediction based on river discharge and genetic programming method. Watershed Management Researches Journal (Pajouhesh & Sazandegi), 88: 44-54. (in Persian with English abstract)
5- Dastorani M.T., Azimi Fashi Kh., Talebi A., Ekhtesasi M.R. 2012. Estimation of suspended sediment using artificial neural network (case study: Jamishan Watershed in kermanshah). Journal of Watershed Management Research, 6: 66-74. (in Persian with English abstract)
6- Daubechies I. 1992. Ten lectures on wavelets. Society for Industrial Mathematics.
7- Ferreira C. 2001. Gene expression programming a new adaptive algorithm for solving problems.Complex Systems, 13(2): 87–129.
8- Hassanzadeh Y., Lotfollahi-Yaghin M.A., Shahverdi S., Farzin S., Farzin N. 2013. De-noising and prediction of time series based on wavelet algorithm and chaos theory (case study: SPI drought monitoring index of Tabriz city). Iran-water resources Research, 3: 1-13. (in Persian with English abstract)
9- Kakaei Lafdani E., Moghaddam Nia A., and Ahmadi A. 2013. Daily suspended sediment load prediction using artificial neural networks and support vector machines. Journal of Hydrology, 478: 50 –62.
10- Kisi O. 2010. Daily suspended sediment estimation using neuro-wavelet models. International Journal of Earth Sciences, 99: 1471 –1482.
11- Kisi O., and Cimen M. 2011. A wavelet-support vector machine conjunction model for monthly streamflow forecasting. Journal of Hydrology, 399: 132 –140.
12- Kisi O., and Shiri J. 2011. Precipitation forecasting using wavelet-genetic programming and wavelet-neuro-fuzzy conjunction models. Water Resource management, 25: 3135 –3152.
13- Luk K.C., Ball J.E., and Sharma A. 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting.Journal of Hydrology, 227:56-65.
14- Melesse A.M., Ahmad S., McClain M.E., Wang X., and Lim Y.H. 2011. Suspended sediment load prediction of river systems: An artificial neural network approach. Agricultural Water Management, 98: 855-866.
15- Nagy H.M., Watanabe K., and Hirano M. 2002. Prediction of Sediment Load Concentration in Rivers usingArtificial Neural Network Model. Journal of Hydraulic Engineering, 128: 588-595.
16- Nourani V., Yahyavi Rahimi A., and Hassan Nejad F. 2013. Conjunction of ANN and threshold based wavelet de-noising approach for forecasting suspended sediment load. International Journal of Management & Information Technology, 3(1): 9 –26.
17- Partal T., and Cigizoglu H.K. 2008. Estimation and forecasting of daily suspended sediment data using wavelet–neural networks. Journal of Hydrology, 358: 317 –331.
18- Rajaee T., Mirbagheri S.A., Zounemat-Kermani M., and Nourani V. 2009. Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models. Science of the Total Environment, 407: 4916-4927.
19- Rajaee T., Nourani V., Zounemat-Kermani M., and Kisi O. 2011. River suspended sediment load prediction: Application of ANN and Wavelet conjunction model. Journal of Hydrologic Engineering, 16(8): 613-627.
20- Salajegheh A., Fathabadi A. 2008. Estimation of the suspended sediment loud of Karaj River using fuzzy logic and neural networks.Journal of Range and Watershed Management, 62: 271-282. (in Persian with English abstract)
21- Yu H.H., and Jenq N.H. 2002. Handbook of Neural Network Signal Processing. CRC Press.
CAPTCHA Image