Evaluating Some Soil Water Characteristic Curve Models in Agricultural Lands of Qorveh-Dehgolan, Kurdistan Province

Authors

1 Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

2 Assistant professor, Department of Water Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

3 Assistant professor, Department of Soil Science and Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

Abstract

Soil water characteristic curve (SWCC) represents the quantative relationship between soil water content and matric suction. Direct measurement of this curve is laborious, time consuming and expensive. Hence, several experimental, mathematical and analytical models have been presented to describe the SWCC. In this research, 11 SWCC models were calibrated using volumetric water content and matric suction data of 27 soil samples of Qorveh-Dehgolan plain agricultural lands. Solver Toolbox in Excel used to calibrate the models and six other soil samples data used for result validation. The R2, RMSE, NRMSE and MBE statistics were used to assess the prediction accuracy of the models. According to the obtained results, for clay and loam textures, Libardi et al and Simmons et al models, for silty clay loam texture, Brooks and Corey model, for clay loam texture, Brooks and Corey and Campbell models, for silty clay texture, Van Genuchten m=1-2/n and Brooks and Corey models and for sandy loam texture, power model were proposed to predict soil water characteristic curve in the soils of the studied lands.

Keywords

References

Allen RG, Pereira LS, Raes D and Smith M, 1998. Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper. 56. FAO, Rome.
Behrang M, Assareh E, Ghanbarzadeh A and Noghrehabadi A, 2010. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data, Solar Energy. 84(8): 1468-1480.
Behmanesh J, Mortazavi N and Mohammadnezhad B, 2015. Estimation of reference evapotranspiration using full and limited data (case study: Tabriz and Urmia synoptic stations), Water and Soil Science. 25(3): 13-27. (In Persian with English abstract)
Behrang M, Assareh E, Noghrehabadi A and Ghanbarzadeh A, 2011. New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique, Energy. 36(5): 3036-3049.
Benghanem, M, Mellit A and Alamri S, 2009. ANN-based modelling and estimation of daily global solar radiation data: A case study, Energy Conversion and Management. 50(7): 1644-1655.
Besharat F, Dehghan AA and Faghih AR, 2013. Empirical models for estimating global solar radiation: A review and case study, Renewable and Sustainable Energy Reviews. 21: 798-821.
Bishop CM, 1995. Neural Networks for Pattern Recognition, Oxford University Press.
Daneshyar M, 1978. Solar radiation statistics for Iran, Solar Energy. 21(4): 345-349.
Erfanian M and Babaei Hesar S, 2013. Evaluation of hybrid model for estimating daily solar radiation in some solar sites of Iran, Journal of Water and Soil. 27(1): 158-168. (In Persian with English abstract)
Fallah Ghalhari Q and Shakeri F, 2016. Calibration of Angstrom- Prescott coefficients for selected stations of Khorasan-e Razavi province, Water and Soil Science. 26(3-2): 229-241. (In Persian with English abstract)
Ferreira C, 2001. Gene expression programming: A new adaptive algorithm for solving problems, complex systems. 13(2): 87-129.
Fogel LJ, 1964. On the organization of intellect, Doctoral dissertation, University of California, Los Angeles-Engineering.
Hasni A, Sehli A, Draoui B, Bassou A and Amieur B, 2012. Estimating global solar radiation using artificial neural network and climate data in the south-western region of Algeria, Energy Procedia. 18: 531-537.
Haykin S and Network N, 2004. A comprehensive foundation, Neural Networks. 2(2004): 41.
Jang JS, 1993. ANFIS: adaptive-network-based fuzzy inference system, IEEE Transactions on Systems, Man, and Cybernetics. 23(3): 665-685.
Kholghi M, Ashrafzadeh A and Maalmir M, 2009. Monthly low-flow forecasting using a stochastic model and adaptive network based fuzzy inference system, Iran-Water Resources Research. 5(2): 16-26. (In Persian with English abstract)
Khorasanizadeh H and Mohammadi K, 2013. Introducing the best model for predicting the monthly mean global solar radiation over six major cities of Iran, Energy. 51: 257-266.
Koza JR, 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, Vol 1. MIT press.
Landeras G, López JJ, Kisi O and Shiri J, 2012. Comparison of gene expression programming with neuro-fuzzy and neural network computing techniques in estimating daily incoming solar radiation in the Basque Country (Northern Spain), Energy Conversion and Management. 62: 1-13.
Li MF, Tang XP, Wu W and Liu HB, 2013. General models for estimating daily global solar radiation for different solar radiation zones in mainland China, Energy Conversion and Management. 70: 139-148.
Majnooni-Heris A, Zand-Parsa S, Sepaskhah A and Nazemosadat M, 2009. Development and evaluation of global solar radiation models based on sunshine hours and meteorological data, Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources). 12(46): 491-499. (In Persian)
Mehdizadeh S, Behmanesh J and Khalili K, 2016. Comparison of artificial intelligence methods and empirical equations to estimate daily solar radiation, Journal of Atmospheric and Solar-Terrestrial Physics. 146:215-27.
Mohammadi K, Shamshirband S, Tong CW, Alam KA and Petković D, 2015. Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year, Energy Conversion and Management. 93: 406-413.
Mohanty S, Patra PK and Sahoo SS, 2016. Prediction and application of solar radiation with soft computing over traditional and conventional approach–A comprehensive review, Renewable and Sustainable Energy Reviews. 56: 778-796.
Moieni S, Javadi S, Kokabi M and Manshadi M, 2010. Estimating the solar radiation in Iran by using the optimal model. Iranian Journal of Energy. 13(2): 1-10. (In Persian with English abstract)
Mostafavi, ES, Ramiyani SS, Sarvar R, Moud HI and Mousavi SM, 2013. A hybrid computational approach to estimate solar global radiation: an empirical evidence from Iran, Energy. 49: 204-210.
Mubiru J, 2008. Predicting total solar irradiation values using artificial neural networks, Renewable Energy. 33(10): 2329-2332.
Noorian, AM, Moradi I and Kamali GA, 2008. Evaluation of 12 models to estimate hourly diffuse irradiation on inclined surfaces, Renewable Energy. 33(6): 1406-1412.
Ozoegwu, CG, 2019. Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number, Journal of Cleaner Production. 216: 1-13.
Rahimikhoob A, 2010. Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment, Renewable Energy. 35(9): 2131-2135.
Sabziparvar AA and Khataar B, 2015. Evaluation of artificial neural network (ANN) and Irmak experimental models to predict daily solar net radiation (Rn) in cold semi-arid climate (Case study: Hamedan), Water and Soil Science. 25(2): 37-50. (In Persian with English abstract)
Sabziparvar AA and Olyaie E, 2011. Evaluation of the performance of artificial neural networks (ANN) in predicting the daily global solar radiation and comparison with results from the Angström model (case study: Tabriz synoptic station), Iranian Journal of Geophysics. 5(3): 30-41. (In Persian with English abstract)
Sabziparvar AA and Shetaee H, 2007. Estimation of global solar radiation in arid and semi-arid climates of East and West Iran, Energy. 32(5): 649-655.
Shafaei S and Dinpashoh Y, 2018. Analysis of drought characteristics of Tabriz (1951-2015), Water and Soil Science. 28(3): 117-130. (In Persian with English abstract)
Sharifi SS, Delirhasannia R, Nourani V, Sadraddini AA and Ghorbani A, 2013. Using artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) for modeling and sensitivity analysis of effective rainfall, Recent Advances in Continuum Mechanics, Hydrology and Ecology. 4: 133-139.
Sharifi SS, Rezaverdinejad V and Nourani V, 2016. Estimation of daily global solar radiation using wavelet regression, ANN, GEP and empirical models: A comparative study of selected temperature-based approaches, Journal of Atmospheric and Solar-Terrestrial Physics. 149: 131-145.
Sumithira T, Kumar AN and Rameshkumar R, 2012. An adaptive neuro-fuzzy inference system (ANFIS) based Prediction of Solar Radiation. Journal of Applied Sciences Research. 8(1): 346-351.
Wu CL, Chau KW and Li YS, 2009. Methods to improve neural network performance in daily flows prediction, Journal of Hydrology. 372(1–4): 80-93.
Yacef R, Benghanem M and Mellit A, 2012. Prediction of daily global solar irradiation data using Bayesian neural network: a comparative study, Renewable Energy. 48: 146-154.
Zhao N, Zeng X and Han S, 2013. Solar radiation estimation using sunshine hour and air pollution index in China, Energy Conversion and Management. 76: 846-851.
Water and Soil Science
Volume 31, Issue 2
June 2021
Pages 13-26

Files

Share

How to cite

Statistics