Estimation of advance time in furrow irrigation using Artificial Neural Network and Principle Component Analysis (PCA) technique

Document Type : Original Article

Authors

1 water sciences and engineering department, agriculture and natural resources Fctuly, Arak university

2 Department of water sciences and engineering, Factuly of agriculture and natural resources, Arak university

3 Department of water sciences and engineering, Factuly of agriculture and natural resources, Univercity of Shahrkord

4 Professor of Department of Irrigation and Reclamation Engineering, University of Tehran

5 Associate Professor of Department of Irrigation and Reclamation Engineering, University of Tehran

Abstract

In this study, Neural Network Technique is used to predict advance time using inflow rate, n coefficient, slope, length of furrow, infiltration curve number, initial soil moisture and bulk density by ANN and PCA Technique. Field measurements on furrows of different length and slopes in Mashhad, Dezful, Orumia, Birjand and Karaj having various soil Textures were used in this study. In the Training phase 144 advance time measured data were initially used and then 96 other field measurements were used for cross validation (48) and evaluation (48) phase. The enter parameters determined by using the sensitivity analysis Network and Principle Component Analysis (PCA) technique The obtained results showed Neural Network Technique is well capable of estimating advance Time with high accuracy. The best results (R2= 0.995) obtained from models that used of Principle Component Analysis as enter parameters. The models that used of initial soils moisture content (R2= 0.848) have higher accuracy in comparison to models that used of infiltration curve number (R2= 0.417) and Coefficient of Manning formula (R2= 0.492).

Keywords

References

عباسی،ف.، جلینی،م.، معیری،م و طائفه­ی رضایی،ب. 1378. تهیه­ی یک مدل ریاضی برای طراحی و ارزیابی روش­های آبیاری سطحی. نشریه­ی شماره­ی 122، گزارش پژوهشی موسسه تحقیقات فنی و مهندسی کشاورزی.
کازرونیان،س.م.، عباسی،ف.، صدقی،ح. 1396. نشریه پژوهش­های حفاظت آب و خاک. 24. 4: 101-83.
کمالی،پ.، ابراهیمیان،ح. 1396. مقایسه و ارزیابی روش‏های مختلف برآورد معکوس ضرایب معادله نفوذ در شرایط کشت داخل جویچه. مجله تحقیقات آب و خاک ایران. 48. 1: 48-39.
گنجه،ا. 1385. ارزیابی یکنواختی کود آبیاری در آبیاری جویچه­ای، پایان نامه کارشناسی ارشد، گروه آبیاری و آبادانی، دانشکده مهندسی آب و خاک دانشگاه تهران.
معروف­پور،ع.، سیدزاده،ا.، بهزادی­نسب،م. 1396. بررسی دقت روش‌های غیرنقطه­ای اندازه­گیری نفوذ در طراحی سامانه آبیاری جویچه­ای. نشریه پژوهش­های حفاظت آب و خاک. 24. 2 :  257-271.
منهاج،م. 1377. مبانی شبکه­های عصبی. انتشارات دانشگاه امیرکبیر.
نوابیان،م. 1382. تخمین هدایت هیدرولیکی اشباع با استفاده از توابع انتقالی، پایان نامه کارشناسی ارشد، گروه آبیاری و آبادانی، دانشکده مهندسی آب و خاک دانشگاه تهران.
Amini,M., Abbaspour,K.C., Khademi,H., Fathianpour,N., Afyoni,M and Schulin,R. 2005. Neural network models to predict cation exchange capacity in arid regions of Iran. European journal of soil science. 56: 551-559.
Alvarez,J.A.R. 2003. Estimation of advance and infiltration equations in furrow irrigation for untested discharges. Journal of agricultural water management. 60: 227–239.
Bradley,A., King,P.E., David,L., Bjorneberg,P.E., Thomas,J., Trout,P.E., Luciano, Mateos,A.M. 2016. Estimation of furrow irrigation sediment loss using an artificial neural network. Journal of irrigation and drainage engineering. 142: 311- 318.
Camdevyren,H., Demyr,N., Kanik,A and Keskyn,S. 2005. Use of principal component scores in multiple linear regression models for prediction of chlorophyll-a in reservoirs. Ecological modeling. 181:581–589.
Cheng-Jian,L and Ming-Hua,H. 2009. Classification of mental task from EEG data using neural networks based on particle swarm optimization. Neurocomputing. 72:  1121–1130.
Dani,O and Henoque,R. 1996. Prediction of surface irrigation advance using soil intake properties. Irrigation science. 16.4: 157-159.
Demuth,H and Beale,M. 1998. Neural network toolbox for use with Matlab, users guide, the mathworks, Howard Demuth Mark Beale.
Jain,A and Kumar,A. 2006. An evalution of artificial neural network technique for the determination of infiltration model parameters. Applied soft Computing. 6: 272-282.
Elliot,R.L., Walker,W.R. 1982. Field evaluation of furrow infiltration and advance functions. Transactions of the ASAE. 15.2: 369–400.
Gillies,M.H and  Smith,R.J. 2005. Infiltration parameters from surface irrigation advance and run-off data. Irrigation Science. 24: 25–35.
Lark,R.M. 1999. Soil-landform relationships at within-field scales: an investigation using continuous classification. Geoderma. 92: 141–165.
Kaustra,I and Boyd,M.S. 1995. Forecasting futures trading volume using neural network. Journal of feauture markets. 15.8: 953-970.
McKenzie,N.J and Ryan,P.J. 1999. Spatial prediction of soil properties using environmental correlation. Geoderma. 89: 67–94.
Minasny,B and McBratney,A.B. 2002. The neuro-m methods for fitting neural network parametric pedotransfer functions. Soil science society of America journal. 66: 352–361.
Qiu,Y., Fu,B., Wang,J and Chen,L. 2001. Spatial variability of soil moisture content and its relation to environmental factors in a semi-arid gully catchment of the Loess Plateau, China. Journal of arid environments. 49.4: 723–750.
Qiu,Y and Zhang,J.T. 1999. Quantitative analysis to the gradients in space and time of natural plant communities in Bashuigou catchment of the Guandi Mountain. Chinese journal of applied and environmental biology. 5: 113–120.
Odeh,I.O.A., Mc Bratney,A.B and Chittleborough,D.J. 1994. Spatial prediction of soil properties from landform attributes derived from a digital elevation model. Geoderma. 63: 197–214.
Parinet,B., Lhote,A and Legube,B. 2004. Principal component analysis: an appropriate tool for water quality evaluation and management dapplication to a tropical lake system. Ecological modeling. 178: 295–311.
Pearson,K. 1901. On lines and planes of closest fit to systems of points in space. Philosophical magazine. 2.6: 559–572.
Scaloopi,E.J., Markely,G.P and Willardson,L.S. 1995. Intake parpmeter from advance and wetting phase of surface irrigation. Journal of irrigation and drainage engineering. 121: 57-69.
Walker,W.R and Skogerboe,G.V. 1987. Surface irrigation theory and practice. Englewood Cliffs, NJ, USA: Prentice-Hall Inc. xiii, 386p.
Webster,R and McBratney,A.B. 1989. On the Akaike information criterion for choosing models for variograms of soil properties .European journal of soil science. 40: 493–496.
Willardson,L.S and Bishop,A.A. 1967. Analysis of surface irrigation application efficiency, Journal of the irrigation and drainage division. 93.2: 21-36. 
Wotling,G., Bouvier,C.H., Danloux,J and Fritsch,J.M. 2000. Regionalization of extreme precipitation distribution using the principal components of the topographical environment. Journal of hydrology. 233:86–101
Iranian Journal of Irrigation & Drainage
Volume 13, Issue 1 - Serial Number 73
July and August 2019
Pages 45-57

Files

Share

How to cite

Statistics