| تعداد نشریات | 24 |
| تعداد شمارهها | 859 |
| تعداد مقالات | 7,646 |
| تعداد مشاهده مقاله | 13,578,492 |
| تعداد دریافت فایل اصل مقاله | 11,684,909 |
Evaluation of Combination Methods for Garlic Evapotranspiration Estimation | ||
| Iran Agricultural Research | ||
| مقاله 11، دوره 34، شماره 2، اسفند 2015، صفحه 91-100 اصل مقاله (1 M) | ||
| نوع مقاله: Full Article | ||
| شناسه دیجیتال (DOI): 10.22099/iar.2016.3507 | ||
| نویسندگان | ||
| Seyed Morteza Seyedian* 1؛ M. Farasati2؛ O. Bahmani3؛ J. Sajad1 | ||
| 1Department of Agriculture, University of Gonbad-Kavous, Gonbad-Kavous, I. R. Iran. | ||
| 2Department of Water Engineering, Razi University, Kermanshah, I. R. Iran. | ||
| 3Department of Water Engineering, BualiSina University, Hamedan, I. R. Iran. | ||
| چکیده | ||
| ABSTRACT-Different evapotranspiration (ET) estimation equations having different accuracy with different conditions have been developed for ET estimation. This study will firstly focus on the estimation of 13 climatic equations of daily garlic ET estimation whose ET is measured by lysimeter to provide information which can be helpful in selecting an appropriate ET equation. The paper aims at showing the potential for combining the result of the best equation to improve the overall accuracy. The findings showed that the five equations of FAO 56 Penman–Monteith, ASCE Penman–Monteith, Kimberly Penman, Penman, and FAO-24 Blaney-Criddle were the most accurateequations for estimating garlic ET. The results of these five equations were combined using the three combination methods of Simple Average Method (C-SAM), multiple linear regression (C-MLR) and Adaptive Neuro-Fuzzy Interface System (C-ANFIS).The comparison of combination methods at the test stage showed that although C-SAM used simpler equations than C-MLR but its results were more reasonable than C-MLR. Overall, the results of these two combination methods did not significantly surpass those of the best ET estimation equations (FAO 56 PM); however,C-ANFIS combination method estimated ET better than the other techniques. Based on the results of this study, the C-ANFIS combination method is recommended for estimating garlic ET. | ||
| کلیدواژهها | ||
| Keywords:؛ Combination Method Evapotranspiration؛ Garlic؛ Lysimeter | ||
| مراجع | ||
|
Allen, R.G. (2000). REF-ET: Reference Evapotranspiration Calculation Software for FAO and ASCE Standardized Equations, Version 2.0. University of Idaho, Kimberly.
Allen, R.G., Jensen, M.E., Wright, J.L., & Burman, R.D. (1989). Operational estimates of reference evapotranspiration. Agronomy Journal, 81, 650–662.
Allen, R.G., Pereira, L.S., Raes, D., & Smith, M. (1998). Crop evapotranspiration guidelines for computing crop water requirements, FAO Irrigation and Drainage, Paper No. 56, Food and Agriculture Organization of the United Nations, Rome.
Batchelor, R., & Dua, P. (1995).Forecaster diversity and the benefits of combining forecasts.Management Science, 41, 68–75.
Bates, J. M., & Granger, C.W.J. (1969).The combination of forecasts.Operations Research Quarterly, 20, 451–468.
Clemen, R.T. (1989). Combining forecasts: a review and annotated bibliography. International Journal of forecasting, 5, 559-583.
Doorenbos, J., & Pruitt, W.O. (1977). Crop Water Requirements, FAO Irrigation and Drainage Paper 24, (1st and 2nd ed), Food and Agricultural Organization of the United Nations. Rome, Italy.
Drake, J.T. (2000). Communications phase synchronization using the adaptive network fuzzy inference system. PhD Thesis, New Mexico State University, Las Cruces, New Mexico, USA.
FAO. 2011. Food and Agricultural Commodities Production. Food and Agriculture Organization of the United Nations.
Frere, M., & Popov, G.F. (1979).Agrometeorological crop monitoring and forecasting. FAO plant production and protection paper 17, FAO, United Nations, Rome, 36-43.
Granger, C.W.J., & Terasvirta, T. (1992).Experiments in modeling nonlinear relationships between time series. In Casdagli, M., & Eubank, S. (Eds.), Nonlinear Modeling and Forecasting. Redwood City, CA: Addison-Wesley.
Gocic, M., & Trajkovic, S. (2010). Software for estimating reference evapotranspiration using limited weather data.Computers and Electronics in Agriculture, 71, 158–162.
Hargreaves, G.H., & Samani, Z.A. (1985).Reference crop evapotranspiration from temperature.Transaction of ASAE, 1, 96-99.
Jabloun, M., & Sahli, A. (2008). Evaluation of FAO-56 methodology for estimating reference evapotranspiration using limited climatic data: applications to Tunisia. Agricultural Water Management, 95, 707–715.
Jang, J. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems Man and Cybernetics, 23, 665–685.
Jang, J.S.R., & Sun, C.T. (1995).Neuro-Fuzzy Modeling and Control.The Proceedings of the IEEE, 83, 378-406.
Jensen, M.E., Burman, R.D., & Allen, R.G. (1990). Evapotranspiration and irrigation water requirements. ASCE Manuals and Reports on Engineering Practices No. 70. ASCE, New York, USA.
Kang, H. (1986). Unstable weights in the combination of forecasts.Management Science, 32, 683–695.
Kim, S., & Kim, H.S. (2008). Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. Journal of Hydrology, 351, 299–317.
Kisi, O. (2006). Daily pan evaporation modelling using a neuro-fuzzy computing technique.Journal of Hydrology, 329, 636–646.
Kisi, O., & Ozturk, O. (2007).Adaptive neurofuzzy computing technique for evapotranspiration estimation.Journal of Irrigation and Drainage Engineering, 133, 368–379.
Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., & Pruitt, W.O. (2002).Estimating evapotranspiration using artificial neural network.Journal of Irrigation and Drainage Engineering, 128, 224–233.
Lebaron, B. (1992). Nonlinear forecasts for the S and P stock index. In Casdagli, M., & Eubank, S (Eds.), Nonlinear Modeling and Forecasting. Redwood City, CA: Addison-Wesley.
Lisboa, P.G.J. (1992). Neural Network-Current Applications, 1st ed. London: Chapman and Hall.
Lopez-Urrea, R., Martin De Santa Olalla, F., Fabeiro, C., & Moratalla, A. (2006).Testing evapotranspiration equations using lysimeter observations in a semiarid climate.Agricultural Water Management, 85, 15–26.
Makkink, G.F. (1957). Testing the Penman formula by means of lysimeters.Journal of the Institution of Water Engineers, 11, 277–288.
Moghaddamnia, A., GhafariGousheh, M., Piri, J., Amin, S., & Han, D. (2009).Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques.Advances in Water Resources, 32, 88–97.
Mohan, S., & Arumugam, N. (1995).An intelligent front-end for selecting evapotranspiration estimation methods.Computers and Electronics in Agriculture, 12, 295-309.
Nandagiri, L., & Kovoor, M.G. (2006). Performance evaluation of reference evapotranspiration equations across a range of indian climates. Journal of Irrigation and Drainage Engineering, 132, 238-249.
Nessabian, S. (2009). Forecasting value added of agricultural sub-sectors during fourth five-year development plan in iran. Pakistan Journal of Agriculture Sciences, 46, 144-147.
Papadakis, J. (1975). Climates of the world and their agricultural potentialities. Buenos Aires: published by the author. Cordoba, Spain, Papadakis.
Penman, H.L. (1948).Natural evaporation from open water, bare soil and grass.Proceeding of Royal Society London, A193, 120-146.
Penman, H.L. (1963).Vegetation and hydrology.Technical Communication No. 53, Commonwealth Bureau of Soils, Harpenden, England.
Pereira, A.R., Sentelhas, P.C., Folegatti, M.V., Villa Nova, N.A., Maggiotto, S.R., & Pereira, F.A.C. (2002).Substantiation of the daily FAO-56 reference evapotranspiration with data from automatic and conventional weather stations.RevistaBrasileira de Agrometeorologia, 10, 251–257.
Popova, Z., Kercheva, M., & Pereira, L.S. (2006).Validation of the FAO methodology for computing ET0 with limited data, application to south Bulgaria.Journal of Irrigation and Drainage, 55, 201–215.
Priestly, C.H.B., & Taylor, R.J. (1972).On the assessment of surface heat flux and evaporation using large-scale parameters.Monthly weather review, 100, 81-92.
See, L. &Openshaw, S.A. (2000). Hybrid multi-model approach to river level forecasting. Hydrological Sciences Journal, 45, 523–536.
Sentelhas, P.C., Gillespie, T.J., & Santos, E.A. (2010).Evaluation of FAO Penman– Monteith and alternative methods for estimating reference evapotranspiration with missing data in Southern Ontario, Canada.Agricultural Water Management, 97, 635–644.
Shamseldin, A.Y., O’connor, K.M., & Liang, G.C. (1997).Methods for combining the outputs of different rainfall-runoff models.Journal of Hydrology, 197, 203–229.
Shiri, J., & Kisi, O. (2011a).Application of artificial intelligence to estimate daily pan evaporation using available and estimated climatic data in the Khozestan Province (Southwestern Iran).Journal of Irrigation and Drainage Engineering, 137, 412–425.
Shiri, J., & Kisi, O. (2011b).Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations.Computers and Geosciences, 37, 1692–1701.
Takagi, T., & Sugeno, M. (1985).Fuzzy identification of systems and its applications to modeling and control.IEEE Transactions on Systems, Man, and Cybernetics, 15, 116–132.
Terui, N., & Van Dijk, H.K. (2002).Combined forecasts from linear and nonlinear time series models.International Journal of Forecasting, 18, 421–438.
Terzi, O., Keskin, M.E., &,Taylan, E.D. (2006).Estimating Evaporation Using ANFIS.Journal of Irrigation and Drainage Engineering, 132, 503–507.
Thiesing, F.M., & Vornberger, O. (1997).Sales forecasting using neural networks.Proceedings of International Conference on Neural Networks (ICNN ’97), Houston, 2125–2128.
Traore, S., Wang, Y.M., & Kerh, T. (2010).Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone.Agricultural Water Management, 97, 707-714.
Turc, L. (1961). Evaluation des besoins en eau d irrigation, evapotranspiration potentielle, formule climatique simplific mise a jour. Annals of Agronomy, 12, 13-49. .(in French).
Widmoser, P. (2009). A discussion on alternative to Penman–Monteith equation.Agricultural Water Management, 96, 711–721.
Wright, J.L. (1996). Derivation of Alfalfa and Grass Reference Evapotranspiration.ASAE Proceeding of International Conference, Irrigation Association and International Committee on Irrigation Drainage, San Antonio, TX 3-6 November.
Wright, J.L., & Jensen, M.E. (1972). Peak water requirements of crops in Southen Idaho.Journal of Irrigation Drainage Deviation, 98 (IR1), 193–201.
Yip, D.H.F., Hines, E.L., & Yu, W.H. (1997).Application of artificial neural networks in sales forecasting.Proceedings of 1997 International Conference on Neural Networks (ICNN ’97), Houston, 2121–2124. | ||
|
آمار تعداد مشاهده مقاله: 1,812 تعداد دریافت فایل اصل مقاله: 1,829 |
||