New Approaches for Estimation of Monthly Rainfall Based on GEP-ARCH and ANN-ARCH Hybrid Models

Accurate estimation of rainfall has an important role in the optimal water resources management, as well as hydrological and climatological studies. In the present study, two novel types of hybrid models, namely gene expression programming-autoregressive conditional heteroscedasticity (GEP-ARCH) and artificial neural networks-autoregressive conditional heteroscedasticity (ANN-ARCH) are introduced to estimate monthly rainfall time series. To fulfill this purpose, five stations with various climatic conditions were selected in Iran. The lagged monthly rainfall data was utilized to develop the different GEP and ANN scenarios. The performance of proposed hybrid models was compared to the GEP and ANN models using root mean square error (RMSE) and coefficient of determination (R2). The results show that the proposed GEP-ARCH and ANN-ARCH models give a much better performance than the GEP and ANN in all of the studied stations with various climates. Furthermore, the ANN-ARCH model generally presents better performance in comparison with the GEP-ARCH model.

[1]  Özgür Kisi,et al.  Precipitation forecasting by using wavelet-support vector machine conjunction model , 2012, Eng. Appl. Artif. Intell..

[2]  Xinguang He,et al.  A hybrid wavelet neural network model with mutual information and particle swarm optimization for forecasting monthly rainfall , 2015 .

[3]  C. Kuo,et al.  Forecasting Caspian Sea level changes using satellite altimetry data (June 1992-December 2013) based on evolutionary support vector regression algorithms and gene expression programming , 2014 .

[4]  Keivan Khalili,et al.  Investigation of spatial and temporal variability of precipitation in Iran over the last half century , 2016, Stochastic Environmental Research and Risk Assessment.

[5]  X. Wen,et al.  Wavelet Analysis-Support Vector Machine Coupled Models for Monthly Rainfall Forecasting in Arid Regions , 2015, Water Resources Management.

[6]  Rajib Maity,et al.  Prediction of monthly rainfall on homogeneous monsoon regions of India based on large scale circulation patterns using Genetic Programming , 2012 .

[7]  Narendra Singh Raghuwanshi,et al.  Estimating Evapotranspiration using Artificial Neural Network , 2002 .

[8]  N. J. Ferreira,et al.  Artificial neural network technique for rainfall forecasting applied to the São Paulo region , 2005 .

[9]  A. A. Alazba,et al.  A new predictive model for furrow irrigation infiltration using gene expression programming , 2016, Comput. Electron. Agric..

[10]  J. Behmanesh,et al.  Estimation of soil temperature using gene expression programming and artificial neural networks in a semiarid region , 2017, Environmental Earth Sciences.

[11]  Saeid Mehdizadeh,et al.  Application of gene expression programming to predict daily dew point temperature , 2017 .

[12]  B. Krishna,et al.  Monthly Rainfall Prediction Using Wavelet Neural Network Analysis , 2013, Water Resources Management.

[13]  B. Yegnanarayana,et al.  Artificial Neural Networks , 2004 .

[14]  Amir Hossein Zaji,et al.  Gene expression programming to predict the discharge coefficient in rectangular side weirs , 2015, Appl. Soft Comput..

[15]  Shervin Motamedi,et al.  Soft computing approaches for forecasting reference evapotranspiration , 2015, Comput. Electron. Agric..

[16]  S. S. Zanetti,et al.  Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data , 2007 .

[17]  Saeid Mehdizadeh,et al.  Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation , 2016 .

[18]  K. P. Moustris,et al.  Precipitation Forecast Using Artificial Neural Networks in Specific Regions of Greece , 2011 .

[19]  Özgür Kisi,et al.  Modeling soil cation exchange capacity using soil parameters , 2017 .

[20]  Jalal Shiri,et al.  Artificial neural networks vs. Gene Expression Programming for estimating outlet dissolved oxygen in micro-irrigation sand filters fed with effluents , 2013 .

[21]  M. Imteaz,et al.  Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate modes , 2013 .

[22]  M. Bannayan,et al.  Investigation of temporal and spatial climate variability and aridity of Iran , 2014, Theoretical and Applied Climatology.

[23]  O. Kisi,et al.  Wavelet and neuro-fuzzy conjunction model for precipitation forecasting , 2007 .

[24]  Shahaboddin Shamshirband,et al.  Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform , 2015, Water Resources Management.

[25]  K. Chau,et al.  Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques , 2010 .

[26]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[27]  Cândida Ferreira,et al.  Gene Expression Programming: A New Adaptive Algorithm for Solving Problems , 2001, Complex Syst..

[28]  J. Abbot,et al.  Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks , 2014 .

[29]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .