Comparative Analysis of MLR, ANN, and ANFIS Models for Prediction of Field Capacity and Permanent Wilting Point for Bafra Plain Soils

References

• Aali, K. A., M. Parsinejad, and B. Rahmani. 2009. Estimation of saturation percentage of soil using multiple regression, ANN, and ANFIS techniques. Computer and Information Science 2 (3):127–36. doi:10.5539/cis.v2n3p127.
   Google Scholar
• Abdipour, M., S. H. R. Ramazani, M. Younessi‐Hmazekhanlu, and M. Niazian. 2018. Modeling oil content of sesame (Sesamum indicum L.) using artificial neural network and multiple linear regression approaches. Journal of the American Oil Chemists’ Society 95 (3):283–97. doi:10.1002/aocs.12027.
   Web of Science ®Google Scholar
• Adamowski, J., and K. Sun. 2010. Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. Journal of Hydrology 390 (1–2):85–91. doi:10.1016/j.jhydrol.2010.06.033.
   Web of Science ®Google Scholar
• Albaradeyia, I., A. Hani, and I. Shahrour. 2011. WEPP and ANN models for simulating soil loss and runoff in a semi-arid Mediterranean region. Environmental Monitoring and Assessment 180 (1–4):537–56. doi:10.1007/s10661-010-1804-x.
   PubMed Web of Science ®Google Scholar
• Anonymous. 2018. Training algorithms. Accessed 2018 www.mathworks.com.
   Google Scholar
• Balathandayutham, A., A. Valliammai, and M. Krishnaveni. 2017. Evaluation of artificial neural network and regression PTFs in estimation soil hydraulic properties. Agriculture Update 12 (4):1105–12. doi:10.15740/HAS/AU/12.TECHSEAR(4)2017/1105-1112.
   Google Scholar
• Barzegar, R., J. Adamowski, and A. A. Moghaddam. 2016. Application of wavelet-artificial intelligence hybrid models for water quality prediction: A case study in Aji-Chay River, Iran. Stochastic Environmental Research and Risk Assessment 30 (7):1797–819. doi:10.1007/s00477-016-1213-y.
   Web of Science ®Google Scholar
• Belayneh, A., J. Adamowski, and B. Khalil. 2016. Short-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet transforms and machine learning methods. Sustainable Water Resources Management 2 (1):87–101. doi:10.1007/s40899-015-0040-5.
   Web of Science ®Google Scholar
• Blake, G., and K. Hartge. 1986. Bulk density. In Methods of soil analysis, part 1-physical and mineralogical methods, ed. A. Klute, 2nd ed., Agronomy Monograph 9. 363–82. Madison: American Society of Agronomy, Soil Science Society of America.
   Google Scholar
• Bortolini, D., and J. A. Albuquerque. 2018. Estimation of the retention and availability of water in soils of the state of Santa Catarina. Revista Brasileira De Ciência Do Solo, 42:e0170250. doi: 10.1590/18069657rbcs20170250.
   Web of Science ®Google Scholar
• Botula, Y. D., W. M. Cornelis, G. Baert, and E. Van Ranst. 2012. Evaluation of pedotransfer functions for predicting water retention of soils in Lower Congo (DR Congo). Agricultural Water Management 111:1–10. doi:10.1016/j.agwat.2012.04.006.
   Web of Science ®Google Scholar
• Bouma, J. 1989. Using soil survey data for quantitative land evaluation. Advances in Soil Science, 177–213. New York, NY:Springer.
   Google Scholar
• Bouyoucos, G. J. 1951. A recalibration of the hydrometer method for making mechanical analysis of soils 1. Agronomy Journal 43 (9):434–38.
   Web of Science ®Google Scholar
• Calp, M. H. 2019. A Hybrid ANFIS-GA Approach for Estimation of Regional Rainfall Amount. Gazi University Journal of Science 32 (1):145–62.
   Web of Science ®Google Scholar
• Çaydaş, U., A. Hasçalık, and S. Ekici. 2009. An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Systems with Applications 36 (3):6135–39. doi:10.1016/j.eswa.2008.07.019.
   Web of Science ®Google Scholar
• Chiu, S. L. 1994. Fuzzy model identification based on cluster estimation. Journal of Intelligent & Fuzzy Systems 2 (3):267–78. doi:10.3233/IFS-1994-2306.
   Google Scholar
• Christodoulou, C. G., and M. Georgiopoulos. 2001. Applications of neural networks in electromagnetics. Artech House, Boston, MA.
   Google Scholar
• Da Silva, I. N., D. H. Spatti, R. A. Flauzino, L. H. B. Liboni, and S. F. Dos Reis Alves. 2017. Artificial neural networks, 309. Cham: Springer International Publishing.
   Google Scholar
• Dahmardeh, M., B. Keshtegar, and J. Piri. 2017. Assessment chemical properties of soil in intercropping using ANN and ANFIS models. Bulgarian Journal of Agricultural Science 23 (2):265–73.
   Google Scholar
• Dharumarajan, S., R. Hegde, M. Lalitha, B. Kalaiselvi, and S. K. Singh. 2019. Pedotransfer functions for predicting soil hydraulic properties in semi-arid regions of Karnataka Plateau, India. Current Science 116 (7):1237. doi:10.18520/cs/v116/i7/1237-1246.
   Web of Science ®Google Scholar
• Dinkar, K. D. 2017. Modelling of Reference Evapotranspiration for Western Maharashtra. [dissertation] Maharana Pratap Unıversıty of Agrıculture and Technology, Udaipur.
   Google Scholar
• Emamgholizadeh, S., M. Parsaeian, and M. Baradaran. 2015. Seed yield prediction of sesame using artificial neural network. European Journal of Agronomy 68:89–96. doi:10.1016/j.eja.2015.04.010.
   Web of Science ®Google Scholar
• Esen, H., and M. Inalli. 2010. ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system. Expert Systems with Applications 37 (12):8134–47. doi:10.1016/j.eswa.2010.05.074.
   Web of Science ®Google Scholar
• Farrokhzad, F., A. Choobbasti, and A. Barari. 2010. Artificial neural network model for prediction of liquefaction potential in soil deposits. International Conferences on Recent Advances in Geotechnical Earthquake Engineering and Soil Dynamics 4. https://scholarsmine.mst.edu/icrageesd/05icrageesd/session04/4
   Google Scholar
• Fashi, F. H. 2016. Evaluation of adaptive neural-based fuzzy inference system approach for estimating saturated soil water content. Modeling Earth Systems and Environment 2 (4):1–6. doi:10.1007/s40808-016-0255-y.
   Web of Science ®Google Scholar
• Fashi, F. H., F. Sharifi, and M. Kheirkhah. 2019. Adaptive neuro fuzzy inference system approach for developing soil water retention pedotransfer functions in flood spreading areas. Journal of Soil and Water Conservation 74 (2):180–87. doi:10.2489/jswc.74.2.180.
   Web of Science ®Google Scholar
• Ghasemi, E., M. Ataei, and K. Shahriar. 2014. An intelligent approach to predict pillar sizing in designing room and pillar coal mines. International Journal of Rock Mechanics and Mining Sciences 65:86–95. doi:10.1016/j.ijrmms.2013.11.009.
   Web of Science ®Google Scholar
• Ghorbani, H., H. Kashi, N. Hafezi Moghadas, and S. Emamgholizadeh. 2015. Estimation of soil cation exchange capacity using multiple regression, artificial neural networks, and adaptive neuro-fuzzy inference system models in Golestan Province, Iran. Communications in Soil Science and Plant Analysis 46 (6):763–80. doi:10.1080/00103624.2015.1006367.
   Web of Science ®Google Scholar
• Ghorbani, M. A., S. Shamshirband, D. Z. Haghi, A. Azani, H. Bonakdari, and I. Ebtehaj. 2017. Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil and Tillage Research 172:32–38. doi:10.1016/j.still.2017.04.009.
   Web of Science ®Google Scholar
• Haykin, S. 1994. Neural networks, Vol. 2. New York: Prentice hall.
   Google Scholar
• Haykin, S. 1999. Neural Networks: A Comprehensive Foundation, 2nd. ed. Prentice-Hall, Englewood Cliffs, New Jersey.
   Google Scholar
• Hesami, M., R. Naderi, M. Tohidfar, and M. Yoosefzadeh-Najafabadi. 2019. Application of adaptive neuro-fuzzy inference system-non-dominated sorting genetic Algorithm-II (ANFIS-NSGAII) for modeling and optimizing somatic embryogenesis of Chrysanthemum. Frontiers in Plant Science 10:869. doi:10.3389/fpls.2019.00869.
   PubMed Web of Science ®Google Scholar
• Honarbakhsh, A., M. Tahmoures, Y. Ostovari, and S. Noroozi. 2017. Developing Pedotransfer Functions for Predicting FC and PWP. Communications in Soil Science and Plant Analysis 48 (2):1–11. doi:10.1080/00103624.2017.1414829.
   Web of Science ®Google Scholar
• Huntington, T. G. 2007. Available water capacity and soil organic matter. In Encyclopedia of Soil Science, ed. Lal, R., 2nd ed. Taylor and Francis: New York, Published online: 12 Dec 2007, 139-43.
   Google Scholar
• Jafarnejadi, A., G. Abbssayyad, R. R. Arshad, and A. Davami. 2012. Pedotransfer functions development for field capacity and permanent wilting points using artificial neural networks and regression models. Int J Agric 2:1079–84.
   Google Scholar
• Jafarzadeh, A. A., M. Pal, M. Servati, M. H. FazeliFard, and M. A. Ghorbani. 2016. Comparative analysis of support vector machine and artificial neural network models for soil cation exchange capacity prediction. International Journal of Environmental Science and Technology 13 (1):87–96. doi:10.1007/s13762-015-0856-4.
   Web of Science ®Google Scholar
• Jang, J. S. 1992. Self-learning fuzzy controllers based on temporal backpropagation. IEEE Transactions on Neural Networks 3 (5):714–23. doi:10.1109/72.159060.
   PubMed Web of Science ®Google Scholar
• Jang, J. S. 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE transactions on systems. Man, and Cybernetics 23 (3):665–85. doi:10.1109/21.256541.
   Web of Science ®Google Scholar
• Kacar, B. 1995. Bitki ve Toprağın Kimyasal Analizleri 3: Toprak Analizleri [Chemical analysis of plant and soil 3: Soil analysis]. Ankara Üniversitesi, Ziraat Fakültesi Eğitim Araştirma ve Geliştirme Vakfı Yayınları (3).
   Google Scholar
• Kashi, H., S. Emamgholizadeh, and H. Ghorbani. 2014. Estimation of soil infiltration and cation exchange capacity based on multiple regression, ANN (RBF, MLP), and ANFIS models. Communications in Soil Science and Plant Analysis 45 (9):1195–213. doi:10.1080/00103624.2013.874029.
   Web of Science ®Google Scholar
• Kay, B. D. 1997. Soil structure and organic carbon: A review. In Soil processes and the carbon cycle, ed. Lal, R., CRC Press, Boca Raton, 169-97.
   Google Scholar
• Keshavarzi, A., A. Bagherzadeh, E.-S. E. Omran, and M. Iqbal. 2016. Modeling of soil exchangeable sodium percentage using easily obtained indices and artificial intelligence-based models. Modeling Earth Systems and Environment 2 (3):130. doi:10.1007/s40808-016-0185-8.
   Web of Science ®Google Scholar
• Keshavarzi, A., F. Sarmadian, M. Sadeghnejad, and P. Pezeshki. 2010. Developing pedotransfer functions for estimating some soil properties using artificial neural network and multivariate regression approaches. ProEnvironment/ProMediu 3:6.
   Google Scholar
• Keshavarzi, A., F. Sarmadian, R. Labbafi, and A. Ahmadi. 2011. Developing pedotransfer functions for estimating field capacity and permanent wilting point using fuzzy table look-up scheme. Computer and Information Science 4 (1):130. doi:10.5539/cis.v4n1p130.
   Google Scholar
• Khaledian, Y., J. N. Quinton, E. C. Brevik, P. Pereira, and M. Zeraatpisheh. 2018. Developing global pedotransfer functions to estimate available soil phosphorus. Science of the Total Environment 644:1110–16. doi:10.1016/j.scitotenv.2018.06.394.
   PubMed Web of Science ®Google Scholar
• Lakzian, A., M. B. Aval, and N. Gorbanzadeh. 2010. Comparison of pattern recognition, artificial neural network and pedotransfer functions for estimation of soil water parameters. Notulae Scientia Biologicae 2 (3):114–20. doi:10.15835/nsb234737.
   Google Scholar
• Li, H., J. Wang, H. Du, and H. R. Karimi. 2018. Adaptive sliding mode control for Takagi–Sugeno fuzzy systems and its applications. IEEE Transactions on Fuzzy Systems 26 (2):531–42. doi:10.1109/TFUZZ.2017.2686357.
   Web of Science ®Google Scholar
• Li, Y., D. Chen, R. White, A. Zhu, and J. Zhang. 2007. Estimating soil hydraulic properties of Fengqiu county soils in the North China plain using pedo-transfer functions. Geoderma 138 (–4):261–71. doi:10.1016/j.geoderma.2006.11.018.
   Web of Science ®Google Scholar
• Looy, K. V., J. Bouma, M. Herbst, J. Koestel, B. Minasny, U. Mishra, and M. G. Schaap. 2017. Pedotransfer functions in Earth system science: Challenges and perspectives. Reviews of Geophysics 55 (4):1199–256. doi:10.1002/2017RG000581.
   Web of Science ®Google Scholar
• Luk, K. C., J. E. Ball, and A. Sharma. 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. Journal of Hydrology 227 (1–4):56–65. doi:10.1016/S0022-1694(99)00165-1.
   Web of Science ®Google Scholar
• May, R., G. Dandy, and H. Maier. 2011. Review of input variable selection methods for artificial neural networks. Artificial neural networks-methodological advances and biomedical applications. InTech.
   Google Scholar
• Merdun, H., Ö. Çınar, R. Meral, and M. Apan. 2006. Comparison of artificial neural network and regression pedotransfer functions for prediction of soil water retention and saturated hydraulic conductivity. Soil and Tillage Research 90 (1–2):108–16. doi:10.1016/j.still.2005.08.011.
   Web of Science ®Google Scholar
• Minasny, B., and A. McBratney. 2002. The neuro-m method for fitting neural network parametric pedotransfer functions. Soil Science Society of America Journal 66 (2):352–61. doi:10.2136/sssaj2002.1407a.
   Web of Science ®Google Scholar
• Mohanty, M., N. K. Sinha, D. Painuli, K. Bandyopadhyay, K. Hati, K. S. Reddy, and R. Chaudhary. 2015. Modelling soil water contents at field capacity and permanent wilting point using artificial neural network for Indian soils. National Academy Science Letters 38 (5):373–77. doi:10.1007/s40009-015-0358-4.
   Web of Science ®Google Scholar
• Monjezi, M., and M. Rezaei. 2011. Developing a new fuzzy model to predict burden from rock geomechanical properties. Expert Systems with Applications 38 (8):9266–73. doi:10.1016/j.eswa.2011.01.029.
   Web of Science ®Google Scholar
• Nelson, D. W., and L. E. Sommers. 1982. Total carbon, organic carbon, and organic matter 1. Methods of Soil Analysis Part 2 Chemical and Microbiological Properties (methodsofsoilan2): 539–79.
   Google Scholar
• Ostovari, Y., K. Asgari, W. Cornelis, and H. Beigi-Harchegani. 2015. Simple methods for estimating field capacity using mamdani inference system and regression tree. Archives of Agronomy and Soil Science 61 (6):851–64. doi:10.1080/03650340.2014.957687.
   Web of Science ®Google Scholar
• Polykretis, C., C. Chalkias, and M. Ferentinou. 2019. Adaptive neuro-fuzzy inference system (ANFIS) modeling for landslide susceptibility assessment in a Mediterranean hilly area. Bulletin of Engineering Geology and the Environment 78 (2):1173–87. doi:10.1007/s10064-017-1125-1.
   Web of Science ®Google Scholar
• Pribyl, D. W. 2010. A critical review of the conventional SOC to SOM conversion factor. Geoderma 156 (3–4):75–83. doi:10.1016/j.geoderma.2010.02.003.
   Web of Science ®Google Scholar
• Qiao, J., Y. Zhu, X. Jia, L. Huang, and M. A. Shao. 2019. Pedotransfer functions for estimating the field capacity and permanent wilting point in the critical zone of the Loess Plateau, China. Journal of Soils and Sediments 19 (1):140–47. doi:10.1007/s11368-018-2036-x.
   Web of Science ®Google Scholar
• Rab, M., S. Chandra, P. Fisher, N. Robinson, M. Kitching, C. Aumann, and M. Imhof. 2011. Modelling and prediction of soil water contents at field capacity and permanent wilting point of dryland cropping soils. Soil Research 49 (5):389–407. doi:10.1071/SR10160.
   Web of Science ®Google Scholar
• Richards, L. 1954. Diagnosis and improvement of saline and alkali soils. Handbook no. 60. Washington, DC: US Department of Agriculture.
   Google Scholar
• Robbins, C. W. 1984. Sodium adsorption ratio-exchangeable sodium percentage relationships in a high potassium saline-sodic soil. Irrigation Science 5 (3):173–79. doi:10.1007/BF00264606.
   Web of Science ®Google Scholar
• Ross, T. J. 2012. Fuzzy logic with engineering applications. 3rd ed. New York: Wiley.
   Google Scholar
• Saracoglu, Ö. 2008. An artificial neural network approach for the prediction of absorption measurements of an evanescent field fiber sensor. Sensors 8 (3):1585–94. doi:10.3390/s8031585.
   PubMed Web of Science ®Google Scholar
• Sarmadian, F., and R. Taghizadeh Mehrjardi. 2008. Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan Province, North of Iran. Global Journal of Environmental Research 2 (1):30–35.
   Google Scholar
• Sarmadian, F., R. T. Mehrjardi, and A. Akbarzadeh. 2009. Modeling of some soil properties using artificial neural network and multivariate regression in Gorgan Province, north of Iran. Australian Journal of Basic and Applied Sciences 3 (1):323–29.
   Google Scholar
• Schaap, M. G., F. J. Leij, and M. T. Van Genuchten. 1998. Neural network analysis for hierarchical prediction of soil hydraulic properties. Soil Science Society of America Journal 62 (4):847–55. doi:10.2136/sssaj1998.03615995006200040001x.
   Web of Science ®Google Scholar
• Şenol, C. 2010. Yapay sinir ağı ve bulanık mantık hibrit yapı ve algoritmalarının geliştirilmesi [Development of artificial neural network-fuzzy logic hybrid strucrtures and algorithms] dissertation, Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Elektronik ve Haberleşme Mühendisliği Anabilim Dalı.
   Google Scholar
• Seyedmohammadi, J., L. Esmaeelnejad, and H. Ramezanpour. 2016. Determination of a suitable model for prediction of soil cation exchange capacity. Modeling Earth Systems and Environment 2 (3):156. doi:10.1007/s40808-016-0217-4.
   Web of Science ®Google Scholar
• Shirani, H., and N. Rafienejad. 2011. Estimation of some soils characteristics Kerman province with using pedotransfer functions and artificial neural network. Journal of Soil Researches (In Persian) 25 (4):349–59.
   Google Scholar
• Slatyer, R. O. 1967. Plant-water relationships. New York; San Frncisco; London: Academic Press.
   Google Scholar
• Sparks, D. L., A. Page, P. Helmke, R. Leoppert, P. Soltanpour, M. Tabatabai, G. Johnston, and M. Sumner. 1996. Methods of soil analysis. Madison, Wisconsin, USA: Soil Science Society of America.
   Google Scholar
• Stewart, B. A. 2017. Soil Health Concerns Facing Dryland Agroecosystems. Soil Health and Intensification of Agroecosytems, ed. M. M. Al-Kaisi and B. Lowery, Academic Press, 51-77.
   Google Scholar
• Tabari, H., S. Marofi, and -A.-A. Sabziparvar. 2010. Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrigation Science 28 (5):399–406. doi:10.1007/s00271-009-0201-0.
   Web of Science ®Google Scholar
• Turan, M., O. Dengiz, and İ. D. Turan. 2018. Samsun ilinin newhall modeline göre toprak sıcaklık ve nem rejimlerinin belirlenmesi [Determination of soil moisture and temperature regimes for Samsun Province according to Newhall model]. Türkiye Tarımsal Araştırmalar Dergisi 5 (2):131–42. doi:10.19159/tutad.382340.
   Google Scholar
• Wagner, B., V. Tarnawski, V. Hennings, U. Müller, G. Wessolek, and R. Plagge. 2001. Evaluation of pedo-transfer functions for unsaturated soil hydraulic conductivity using an independent data set. Geoderma 102 (3–4):275–97. doi:10.1016/S0016-7061(01)00037-4.
   Web of Science ®Google Scholar