A Hybrid Artificial Neural Network and Particle Swarm Optimization Algorithm for Statistical Downscaling of Precipitation in Arid Region

Authors
M. Alizamir 1
M. Azhdary Moghadam 2
A. Hashemi Monfared 3
A. Shamsipour 4
1 Ph.D. Student, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 Associate Professor, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
3 Assistant Professor, Department of Civil Engineering, University of Sistan and Baluchestan, Zahedan, Iran
4 Associate Professor, Faculty of Geography, University of Tehran, Tehran, Iran
Abstract
Background: Prediction of future climate change is based on output of global climate models (GCMs). However, because of coarse spatial resolution of GCMs (tens to hundreds of kilometers), there is a need to convert GCM outputs into local meteorological and hydrological variables using a downscaling approach. Downscaling technique is a method of converting the coarse spatial resolution of GCM outputs at the regional or local scale. This study proposed a novel hybrid downscaling method based on artificial neural network (ANN) and particle swarm optimization (PSO) algorithm.
Materials and Methods: Downscaling technique is implemented to assess the effect of climate change on a basin. The current study aims to explore a hybrid model to downscale monthly precipitation in the Minab basin, Iran. The model was proposed to downscale large scale climatic variables, based on a feed-forward ANN optimized by PSO. This optimization algorithm was employed to decide the initial weights of the neural network. The National Center for Environmental Prediction and National Centre for Atmospheric Research reanalysis datasets were utilized to select the potential predictors. The performance of the artificial neural network-particle swarm optimization model was compared with artificial neural network model which is trained by Levenberg–Marquardt (LM) algorithm. The reliability of the models were evaluated by using root mean square error and coefficient of determination (R2).
Results: The results showed the robustness and reliability of the ANN-PSO model for predicting the precipitation which it performed better than the ANN-LM. It was concluded that ANN-PSO is a better technique for statistically downscaling GCM outputs to monthly precipitation than ANN-LM.
Discussion and Conclusions: This method can be employed effectively to downscale large-scale climatic variables to monthly precipitation at station scale.
Keywords
Artificial neural network (ANN)
Climate Change
Multi-layer perceptron
Particle Swarm Optimization (PSO)
Statistical downscaling
  1. Bouraoui F, Vachaud G, Li LZX, Le H, Treut T. Evaluation of the impact of climate changes on water storage and groundwater recharge at the watershed scale. Clim Dyn Chen 1999; 15(2): 153-161.

  2. Khazaei MR, Zahabiyoun B, Saghafian B. Assessment of climate change impact on floods using weather generator and continuous rainfall‐runoff model. Int J Climatol. 2012; 32(13): 1997-2006.

  3. Wilby RL, Charles SP, Zorita E, Timbal B, Whetton P, Mearns LO. Guidelines for use of climate scenarios developed from statistical downscaling methods, Supporting material of the Intergovernmental Panel on Climate Change, DDC of IPCCTGCIA, 2004; p. 27.

  4. Fistikoglu O, Okkan U. Statistical downscaling of monthly precipitation using NCEP/NCAR reanalysis data for Tahtali River Basin in Turkey. J Hydrol Eng. 2010; 16(2): 157-164.

  5. Hashmi MZ, Shamseldin AY, Melville BW. Statistical downscaling of watershed precipitation using Gene Expression Programming (GEP). Environ Modell Softw. 2011; 26(12): 1639-1646.

  6. Anandhi A, Srinivas VV, Nanjundiahband RS, Kumara DN. Downscaling precipitation to river basin in India for IPCCSRES scenarios using support vector machine. Int J Climatol. 2008; 28(3): 401-420.

  7. Yang C, Wang N, Wanga S. A comparison of three predictor selection methods for statistical downscaling. Int J Climatol. 2016; 37(3): p. 12.

  8. Chithra NR, ThampiSG, Surapaneni S, Nannapaneni R, Kumar Reddy AA, Dinesh Kumar J. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in the Chaliyar river basin, India, using ANN-based models. Theor Appl Climatol. 2014; 121(3-4): 581-590.

  9. Ahmed K, Shahid S, Haroon SB, Xiao-jun W. Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan. J Earth Syst Sci. 2015; 124(6): 1325-1341.

  10. Lin GF, Chang MJ, Wu JT. A Hybrid Statistical Downscaling Method Based on the Classification of Rainfall Patterns. Water Resour Manag. 2016; 1-25.

  11. Mendes D, Marengo JA. Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios. Theor Appl Climatol. 2010; 100(3-4): 413-421.

  12. Srinivas VV, Basu B, Nagesh Kumara D, Jainb SK. Multi‐site downscaling of maximum and minimum daily temperature using support vector machine. Int J Climatol. 2013; 34(5): 1538-1560.

  13. Okkan U, Inan G. Statistical downscaling of monthly reservoir inflows for Kemer watershed in Turkey: use of machine learning methods, multiple GCMs and emission scenarios. Int J Climatol. 2015; 35(11): 3274-3295.

  14. Okkan U, Kirdemir U. Downscaling of monthly precipitation using CMIP5 climate models operated under RCPs. Meteorol Appl. 2016; 23(3): 514-528.

  15. Ahmadi MA. Neural network based unified particle swarm optimization for prediction of asphaltene precipitation. Fluid Phase Equilibr. 2012; 314: 46-51.

  16. Duan H, Huang L. Imperialist competitive algorithm optimized artificial neural networks for UCAV global path planning. Neurocomputing. 2014; 125: 166-171.

  17. Kisi Ö. River suspended sediment concentration modeling using a neural differential evolution approach. J Hydrol. 2010; 389(1): 227-235.

  18. Wang Y, Wang H, Leib X, Jiangb Y, Songa X. Flood simulation using parallel genetic algorithm integrated wavelet neural networks. Neurocomputing 2011; 74(17): 2734-2744.

  19. Cheng X, Zhang X, Zhao L, Deng A, Bao Y, Liu Y, Jiang Y. The application of Shuffled Frog Leaping Algorithm to Wavelet Neural Networks for acoustic emission source location. Comptes Rendus Mecanique., 2014; 342(4): 229-233.

  20. Reed R. Pruning algorithms – a survey. IEEE Trans Neural Network. 1993; 4: 740-747.

  21. Hykin S. Neural networks: A comprehensive foundation. New Jersey: Printice-Hall, Inc. 1999.

  22. Hornik K, Stinchcombe M, White H. Neural Network. 1990; 3(5): 551-60.

  23. Kisi, Ö. Constructing neural network sediment estimation models using a data-driven algorithm. 2008; MATH COMPUTSIMULAT. 79(1): 94-103.

  24. Banerjee P, Prasad RK, Singh VS, Forecasting of groundwater level in hard rock region using artificial neural network. Environ Geol. 2009; 58(6): 1239-1246.

  25. Eberhart RC, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of 6th symposium micro machine and human science.1995; p. 39-43.

  26. Eberhart RC, Simpson PK, Dobbins RW, Computational intelligence PC tools. Boston: Academic Press Professional. 1996.

  27. Clerc M, Kennedy J. The particle swarm explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation. 2002; 6(1): 58-73.

  28. Ahmadi MA, Shadizadeh SR. New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel, 2012; 102: 716-723.

  29. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, et al. The NCEP/NCAR reanalysis project. Bulletin of the American Meteorological Society, 1996; 77: 437-471.

  30. Sachindra D, Perera B. Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships. PloS one 2016; 11(12): p. 21.

  31. Kouhestani A, Eslamiana SS, Abedi-Koupai J, Besalatpour AA. Projection of climate change impacts on precipitation using soft-computing techniques: A case study in Zayandeh-rud Basin, Iran. Glob Planet Chang. 2016; 144: 158-170.

  32. Sarhadi A, Burn DH, Johnson F, Mehrotra R, Sharma A. Water resources climate change projections using supervised nonlinear and multivariate soft computing techniques. J Hydrol. 2016; 536: 119-132.

  33. Sachindra DA, Huang F, Barton A, Perera BJC. Least square support vector and multi-linear regression for statistically downscaling general circulation model outputs to catchment streamflows. Int J Climatol. 2012; 2013; 33(5): 1087-1106.

  34. Daliakopoulos IN, Coulibaly P, TsanisIK. Groundwater level forecasting using artificial neural networks. J Hydrol. 2005; 309: 229-240.

  35. Mohanty S, Jha MK, Kumar A, SudheerKP. Artificial Neural Network Modeling for Groundwater Level Forecasting in a River Island of Eastern India. Water Resour Manage. 2010; 24(9): 1845-1865.

  36. NajafiMR, Moradkhani H, Wherry, SA .Statistical Downscaling of Precipitation Using Machine Learning with Optimal Predictor Selection. J. Hydrol Eng. 2010; 16(8): 650-664.

  37. Goyal MK, OjhaC.S.P. Downscaling of surface temperature for lake catchment in an arid region in India using linear multiple regression and neural networks. Int. J. Climatol. 2012; 32: 552–566.

  38. Joshi D, St-Hilaire A, Ouarda, T, Daigle A. Statistical downscaling of precipitation and temperature using sparse Bayesian learning, multiple linear regression and genetic programming frameworks. Can Water Resour J. 2015; 40(4), 392-408.

  39. Vu MT, Aribarg T, Supratid S, Raghavan SV, Liong SY. Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok? Theor Appl Climatol; 2015; 126(3-4): 453-467.

  40. Goodarzi E, Dastorani MT, Massahbavani A, Talebi A. Evaluation of the Change-Factor and LARS-WG Methods of Downscaling for Simulation of Climatic Variables in the Future (Case study: Herat Azam Watershed, Yazd - Iran). JECOPERSIA; 2015; 3(1); 833-846

  41. Motiee H, McBean E. Assessment of Climate Change Impacts on Groundwater Recharge for Different Soil Types-Guelph Region in Grand River Basin, Canada. J ECOPERSIA; 2017; 5(2): 1731-1744.

ECOPERSIA
Volume 5, Issue 4 - Serial Number 22
December 2017
Pages 1991-2006