Evaluation of Integrated Models of Wavelet-Artificial Neural Network and Wavelet-Gene Expression Programming in the Short-Term Drought Prediction

Document Type : Research

Authors

1 Ph.D. Student in Water Resources Engineering, Department of Water Sciences Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

2 Associate Professor. Department of Water Sciences Engineering, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran

Abstract

Drought prediction plays an important role in the planning and management of natural resources, water resources and plant water requirements. Wavelet transform is one of the new and highly effective methods for analysing signals and time series. Using the mother wavelet, the standard precipitation index (SPI) signal was analyzed and the results were considered as inputs of the artificial neural network models and the gene expression programming (GEP). Multi-layer perceptron (MLP), radial basis function (RBF), (GEP), as well as the wavelet-artificial neural networks integrated model and multi-layer perceptron (WA-MLP), radial basis function (WA- RBF) and wavelet- gene expression programming (WA- GEP) were used for drought forecasting. The rainfall data collected at the Bidestan Station for a period of 44 years were used on the Shoor Watershed in the Province of Qazvin. Moisture condition was calculated using the SPI in the short-term period of 3 months. To estimate the SPI in each period, the respective amounts were considered from the previous cycles. The results showed that among the six applied models, the WA-GEP predicted the SPI values and the short-term drought condition with a higher accuracy. The WA-GEP model proved to be the best scenario in the validation stage of R2, RMSE, MAE and NS of 0.911, 0.037, 0.022 and 0.845, respectively.

Keywords

References

Adamowski J, Fung Chan H. 2011. Awavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology. 407: 28­–40.
Bahmani A, Ebrahimi SH, Gholinejad S. 2006. Development of rainfall-runoff ANN model for watershed and investigation its extend ability for neighborhood stations. Proceedings of second conference of water resource management.  22–23 January, Isfahan university of technology. 1–8.
Belayneh A, Adamowski J, Khalil , Ozga-Zielinki B. 2014. Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression. Journal of Hydrology. 508: 418–429.
Broomhed D.S, Lowe  D. 1988. Multivariate functional interpolation and adaptive networks. Complex system.2: 321–355.
Crespo J, Mora E. 1993. Drought estimation with neural networks. Advances in engineering software. 8(3):167–170.
Danandeh Mehr A, Kahya E, Ozger M. 2014. A gene-wavelet model for long lead time drought forecasting. Journal of Hydrology. 517: 691–699.
Eivazi M, Mosaedi A, Dehghani A. 2010. Comparison of different approaches for predicting SPI. Journal of Water and soil Conservation.16(2):145­167.
Ferreira C. 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems. 13(2): 87–129.
Ghorbani M, Dehghani R. 2016. Application of Bayesian Neural Networks, Support Vector Machines and Gene Expression Programming Analysis of Rainfall - Runoff Monthly (Case Study: Kakarza River). Science and irrigation engineering Scientific and Research Journal. 39(2): 125­–138.
Ghorbani M, Khatibi R, Asadi H, Yousefi P. 2012. Inter comparison of an evolutionary programming model of suspended sediment time-series whit other local model. Journal of  Hydrology. 511: 530–545.
Golabi M, Radmanesh F, Akhondali A. 2014. An investigation of artificial neural network and time series performance in the index standard precipitation drought modeling (Case study: selected stations of Khuzestan Province). Arid Biome Scientific and Research Journal. 3(1): 82–­87. (In Persian).
Hosseini Moghari S, Araghinejad SH. 2017. Application of Statistical, Fuzzy and Perceptron Neural Networks in Drought Forecasting (Case Study: Gonbad-e Kavous Station). Journal of Water and Soil. 30(1): 247–­259. (In Persian).
Karayiannis N. B, Venetsanopoulos A. N. 1993. Artificial neural network: Learning algorithms, performance evaluation, and application. kluwer academic publisher, Boston. 523 pp.
Komasi M, Nozari H, Gheshlaghi N. 2017. Forecasting of water level in Urmia Lake using Time series, Artificial Neural Network and Neural Network-Wavelet. Water Engineering and Irrigation Science and Research Quarterly. 6(24): 64­–77.
McKee T, Doesken N, Kleist J. 1993. The relation of drought frequency and duration to time scales. Preprints. 8th Conference on Applied Climatology, 17­-22 January, 379–384.
Mehdizadeh S, Behmanesh J, Khalili K. 2017. Application of gene expression programming to predict daily dew point temperature. Applied Thermal Engineering. 112: 1097–1107
Mishra A, Desai V. 2006. Drought forecasting using feed- forward recursive neural network. Ecological modeling. 198(1–2): 127–138.
Mishra A, Singh V. 2010. A review of drought concepts, Journal of Hydrology. 391 (1–2): 202–216.
Moosavi V,Vafakhah M, Shirmohammadi B, Behnia N. 2013. A wavelet- ANFIS hybrid model for groundwater level forecasting for different prediction periods. Journal of Water Resource Management. 27(5): 1301–­1321.
Moried S, Moghaddasi M, Paemozd SH, Ghaemi H. 2005. Monitoring the Drought During 1998 to 2000 in Tehran Province, Using EDI, SPI, DI Indices and Geographical Information System. Journal of Spatial Planning. 9(1): 197–215.
Nakhaei M, Saberi Nasr A. 2012. A Combined wavelet-artificial neural network model and its applications to the prediction of groundwater level fluctuations. Geopersia. 2(2): 77–91.
Nourani V, Alami M, Aminfar M. 2009. A combined neural- wavelet model for prediction of Lighvanchai watershed precipitation. Engineering Applications of Artificial Intelligence. 22: 466–472.
Prathumchai K, Honda K, Nualchawee K. 2001.Drought risk evaluation using remote sensing and GIS: A case study in Lopburi Province. 22nd Asian Conference on Remote Sensing. National University of Singapore. Singapore.
Rajaee T. 2011. Wavelet and ANN combination model for prediction of daily suspended sediment load in river. Science of the Total Environment. 409(15): 2917–2928.
Rajaee T, Ebrahimi H. 2013. Modeling of groundwater fluetuations by wavelet transform and dynamic neural network. Water and irrigation mamagement. 4(14): 73–­87. (In Persian).
Ravansalar M, Rajaee T, Zounemat-kermani M. 2016. A wavelet- linear genetic programming model for sodium concentration forecasting in rivers. Journal of  Hydrology. 537: 398–­407.
Shafaei M, Fakheri Fard A, Darbandi S, Ghorbani M. 2014. Predicrion Daily Flow of Vanyar Station Using ANN and Wavelet Hybrid Procedure. Water Engineering and Irrigation Science and Research Quarterly. 4(14): 113­–128. (In Persian).
Sharma B, Smakhtin V. 2004. Potential of water harvesting as a strategic tool for drought mitigation.International Water Management Institue. Colombo, Sri Lanka.
Shoaib M, Shamseldin A, Melville B, and Munner Khan M. 2015. Runoff forecasting using hybrid Wavelet Gene Expression Programming( WGEP) approach. Journal of Hydrology. 527:326–­344
Sifuzzaman M, M Islam, M Ali. 2009. Application of wavelet transform and its advantages compared to fourier transform. Journal of Physical Sciences, 13:121–134.
Solgi A, Zarei H, Golabi M.R. 2018. Performance assessment of gene expression programming model using data preprocessing methods to modeling river flow. Journal of Water and Soil Conservation. 27(2):185–201. (In Persian).
Toufani P, Mosaedi A, Fakheri Fard A. 2012. Predication of Precipitation Applying Wavelet Network Model (Case study: Zarringol station, Golestan province, Iran). Journal of Water and Soil. 25(5): 1217­–1226. (In Persian).
Wang Sh, Lian J, Peng Y, Hu B, and Chen H. 2019. Generalized reference evapotranspiration models with limited climatic data based on random forest and gene expression programming in Guangxi, China. Agricultural Water Management. 221: 220–230
Yousefi P, Shabani S, Mohammadi H, and Naser Gh. 2017.  Gene expression programing in long term water demand forecasts using wavelet decomposition. Procedia Engineering. 186: 544 – 550

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History

  • Receive Date: 11 April 2019
  • Revise Date: 30 September 2019
  • Accept Date: 17 December 2019

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