Design and implementation of a hybrid MLP-FFA model for soil salinity prediction

  • Nastaran Pouladi
  • Ali Asghar Jafarzadeh
  • Farzin Shahbazi
  • Mohammad Ali GhorbaniEmail author
Original Article


In this research, the ability of a hybrid model, which is integrating the firefly algorithm with the multilayer perceptron (MLP-FFA), is investigated for prediction of soil salinity (EC) using remote sensing and topography data in Miandoab city, northwest of Iran. Some important variables including the salinity ratio (SR), slope, ratio vegetation index (RVI), normalized differential vegetation index (NDVI), gypsum index, soil-adjusted vegetation index (SAVI), elevation and normalized difference salinity index (NDSI) are used as inputs of the model, while the EC is the output variable. The data consist of 80 soil samples. The estimates provided by the MLP-FFA model are compared with the standalone MLP model based on the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe coefficient (ENS), Willmott’s Index of agreement (Wi) and percentage peak deviation (Pdv). The obtained results showed more precise estimations of the MLP-FFA than the MLP model with R2, MAE, RMSE, ENS, Wi and Pdv values of 0.641, 1.195, 1.672, 0.626, − 0.247 and 4.603, respectively, in training phase, while in the testing phase these values were equal to 0.662, 0.439, 0.538, 0.566, − 0.15 and − 32.013, respectively. Therefore, the results specified that the developed MLP-FFA model can be selected as an efficient technique over the MLP model for accurate prediction of EC.


Firefly algorithm Hybrid model MLP Prediction Soil salinity 



  1. Alexakis D, Gotsis D, Giakoumakis S, 2014, Evaluation of soil salinization in a Mediterranean site (Agoulinitsa district—West Greece). Arab J Geosci 8(3):1373–1383CrossRefGoogle Scholar
  2. Allbed A, Kumar L (2013) Soil salinity mapping and monitoring in arid and semi-arid regions using remote sensing technology: a review. Adv Rem Sens 2(4):373–385CrossRefGoogle Scholar
  3. Amare B, 2013, Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil SpectroscopyGoogle Scholar
  4. Banaei MH (1998) Map of Iran soil moisture and heat regimes. Institute of Soil and Water Research, Ministry of Agriculture, Tehran, Iran. (In Persian)Google Scholar
  5. Bell JC, Grigal DF, Bates PC (2000) A soil terrain model for estimating spatial patterns of soil organic carbon. In: J.C., W. J. P. a. G., ed., Terrain analysis: principles and applications. Wiley, New York, pp 295–310Google Scholar
  6. Deo RC, Ghorbani MA, Samadianfard S, Maraseni T, Bilgili M, Biazar M (2018) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Energy 116:309–323CrossRefGoogle Scholar
  7. Erzin Y, Rao BH, Singh DN (2008) Artificial neural network models for predicting soil thermal resistivity. Int J Thermal Sci 47(10):1347–1358CrossRefGoogle Scholar
  8. Gorji T, Tanik A, Sertel E (2015) Soil salinity prediction, monitoring and mapping using modern technologies. Procedia Earth and Planetary Science 15:507–512CrossRefGoogle Scholar
  9. Haghverdi A, Mohammadi K, Mohseni Movahed SA, Ghahreman B, Afshar M (2011) Estimation of soil salinity profile in Tabriz irrigation and drainage network using SaltMod and ANN models: Water and Soil, v. 25, p. 174–186. PersianGoogle Scholar
  10. Haykin S (1998) Networks N: A Comprehensive Foundation (2nd), Prentice Hall. ISBN 0-13-273350-1Google Scholar
  11. Huete AR, 1988, A soil-adjusted vegetation index (SAVI): Remote Sensing of Environment, v. 25, no. 3, p. 295–309Google Scholar
  12. Jha P, Biswal BB, Sahu OP (2015) Inverse kinematic solution of robot manipulator using hybrid neural network. Int J Mater Sci Eng 31–38Google Scholar
  13. Jordan CF (1969) Derivation of leaf area index from quality of light on the forest floor. Ecology 50(4):663–666CrossRefGoogle Scholar
  14. Kuang BY, Tekin Y, Mouazen AM (2015) Comparison between artificial neural network and partial least squares for on-line visible and near infrared spectroscopy measurement of soil organic carbon, pH and clay content: Soil & Tillage Research, v. 146, p. 243–252Google Scholar
  15. Legates DR, McCabe GJ (1999) Evaluating the use of ‘‘goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resource Research v 35:233–241CrossRefGoogle Scholar
  16. Lhissoui R, Harti AE, Chokmani K (2014) Mapping soil salinity in irrigated land using optical remote sensing data. Eurasian Journal of Soil Science (Ejss) 3(2):82CrossRefGoogle Scholar
  17. Meersmans J, De Ridder F, Canters F, De Baets S, Van Molle M, 2008, A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium): Geoderma, v. 143, no. 1–2, p. 1–13Google Scholar
  18. Minasny B, McBratney AB, 2016, Digital soil mapping: A brief history and some lessons: Geoderma, v. 264, p. 301–311Google Scholar
  19. Nandy S, Sarkar P, Abraham A, Karmakar M, Das A, Paul D (2012) Agent based adaptive firefly back-propagation neural network training method for dynamic systems, Hybrid Intelligent Systems (HIS). 12th International Conference, p. 449–454Google Scholar
  20. Nash J, Sutcliffe J, 1970, River flow forecasting through conceptual models part I. A discussion of principles: Hydrology, v. 10, p. 282–290Google Scholar
  21. Olatomiwa L, Mekhilef S, Shamshirband S, Mohammadi K, Petković D, Sudheer C (2015) A support vector machine–firefly algorithm-based model for global solar radiation prediction. Solar Energy 115:632–644CrossRefGoogle Scholar
  22. Poursalehi N, Zolfaghari A, Minuchehr A, Moghaddam HK, 2013, Continuous firefly algorithm applied to PWR core pattern enhancement: Nuclear Engineering and Design, v. 258, p. 107–115Google Scholar
  23. Raheli B, Aalami MT, El-Shafie A, Ghorbani MA, Deo RC, 2017, Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River: Environmental Earth Sciences, v. 76, no. 14Google Scholar
  24. Rowell DL (1994) Soil Science: Method and application. Longman Group, London. 345Google Scholar
  25. Roy C, Motamedi S, Hashim R, Shamshirband S, Petković D (2016) A comparative study for estimation of wave height using traditional and hybrid soft-computing methods. Environ Earth Sci 75:7CrossRefGoogle Scholar
  26. Shahabi M, Jafarzadeh AA, Neyshabouri MR, Ghorbani MA, Valizadeh Kamran K (2016) Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods: Arch Agron Soil Sci 63(2):151–160CrossRefGoogle Scholar
  27. Singh G, Bundela DS, Sethi M, Lal K, Kamra SK (2010) Remote sensing and geographic information system for appraisal of salt-affected soils in India. J Environ Qual v 39(1):5–15CrossRefGoogle Scholar
  28. Soil Survey S (2014) Keys to soil taxonomy, 12th. USDA-Natural Resources Conservation Service, Washington, DCGoogle Scholar
  29. Sudheer CH (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288CrossRefGoogle Scholar
  30. Sumfleth K, Duttmann R (2008) Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators. Ecol Indicators 8(5):485–501CrossRefGoogle Scholar
  31. Wang B, Waters C, Orgill S, Clark A, Liu DL, Simpson M, Cowie A, McGowen I, Sides T (2017) Estimating soil organic carbon stocks using machine learning methods in the semi-arid rangelands of New South Wales, 22nd International Congress on Modelling and Simulation: Hobart, Tasmania, Australia, 3 to 8 December 2017Google Scholar
  32. Willmott CJ (1984) On the evaluation of model performance in physical geography. In: Spatial statistics and models: Springer, p. 443–460Google Scholar
  33. Yang XS (2010) Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2:78–84CrossRefGoogle Scholar
  34. Yang XS, He X (2013) Firefly algorithm: recent advances and applications. Int J Swarm Intell 1:1CrossRefGoogle Scholar
  35. Yilmaz I, Kaynar O (2011) Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 38(5):5958–5966CrossRefGoogle Scholar
  36. Zhao Z, Yang Q, Beno G, Chow L, Xing TL, Rees Z, Meng FR, 2010, Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes. Can J Soil Sci 90:75–87CrossRefGoogle Scholar
  37. Zribi M, Baghdadi N, Nolin M (2011) Remote sensing of soil: applied and environmental soil science, v. 2011, p. 1–2Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Nastaran Pouladi
    • 1
  • Ali Asghar Jafarzadeh
    • 1
  • Farzin Shahbazi
    • 1
  • Mohammad Ali Ghorbani
    • 2
    • 3
    Email author
  1. 1.Department of Soil ScienceUniversity of TabrizTabrizIran
  2. 2.Department of Water EngineeringUniversity of TabrizTabrizIran
  3. 3.Department of Civil EngineeringNear East UniversityNicosia, North CyprusTurkey

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