Development of Generalized Higher-Order Synaptic Neural–Based Models for Different Agroecological Regions in India
Publication: Journal of Irrigation and Drainage Engineering
Volume 140, Issue 12
Abstract
This paper aims at developing generalized higher-order synaptic neural (GHSN), i.e., generalized quadratic synaptic neural (GQSN) and generalized cubic synaptic neural (GCSN), reference evapotranspiration () models corresponding to various methods. The GHSN models (GHSNs) were developed using pooled climate data of different locations under four agroecological regions (semiarid, arid, subhumid, and humid) in India. The inputs for the development of GHSNs include daily climate data of minimum and maximum air temperatures, minimum and maximum relative humidity, wind speed, solar radiation, and pan evaporation with different combinations, and the target consists of estimated by one of the methods. Comparisons of developed GHSNs with the generalized first-order neural network, i.e., generalized linear synaptic neural (GLSN) models, were made to test the relative merits of one model over the other. Comparisons were also made between GHSNs and conventional methods. Based on the comparisons, it is concluded that the GHSNs along with GLSN models performed better than their conventional methods. Comparison of GHSNs and GLSN models among themselves reveals that the GQSN followed by GCSN models performed superior to the GLSN for almost all regions. The GHSNs corresponding to one of the methods ranked first and GHSNs corresponding to another method ranked second for all regions. For semiarid and arid regions, the GHSNs corresponding to one of the methods ranked third and for subhumid and humid regions the GHSNs corresponding to another method ranked third. Further, GHSNs were applied to model development and model testing locations to test the generalizing capability. The testing results suggest that the GQSN followed by GLSN models have a better generalizing capability than GCSN for almost all regions.
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Acknowledgments
The authors wish to thank All India Coordinated Research Project on Agrometeorology (AICRPAM), Central Research Institute for Dryland Agriculture (CRIDA), Hyderabad, Andhra Pradesh, India for providing the requisite climate data to carry out this study. Also, the authors express their gratitude to the reviewers for useful comments and suggestions.
References
Abrahart, R. J., et al. (2012). “Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting.” Prog. Phys. Geog., 36(4), 480–513.
Adamala, S., Raghuwanshi, N. S., Mishra, A., and Tiwari, M. K. (2013). “Evapotranspiration modeling using second-order neural networks.” J. Hydrol. Eng., 1131–1140.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M. (1998). “Crop evapotranspiration: Guidelines for computing crop water requirements.” FAO Irrigation and Drainage Paper No. 56, Rome, Italy.
Allen, R. G., and Pruitt, W. O. (1986). “Rational use of the FAO Blaney-Criddle formula.” J. Irrig. Drain. Eng., 139–155.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology—I: Preliminary concepts.” J. Hydrol. Eng., 115–123.
ASCE Task Committee on Application of Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology—II: Hydrologic applications.” J. Hydrol. Eng., 124–137.
Brutsaert, W. H. (1982). Evaporation into the atmosphere: Theory, history and applications, D. Reidel, Dordrecht, Netherlands.
Chakra, N. C., Song, K.-Y., Gupta, M. M., and Sarafa, D. N. (2013). “An innovative neural forecast of cumulative oil production from a petroleum reservoir employing higher-order neural networks (HONNs).” J. Petrol. Sci. Eng., 106, 18–33.
Chiew, F. H. S., Kamaladassa, N. N., Malano, H. M., and McMahon, T. (1995). “Penman-Monteith, FAO-24 reference crop evapotranspiration and class-A pan data in Australia.” Agric. Water Manage., 28(1), 9–21.
Daniel, E. B., Camp, J. V., LeBoeuf, E. J., Penrod, J. R., Dobbins, J. P., and Abkowitz, M. D. (2011). “Watershed modeling and its applications: A state-of-the-art review.” Open Hydrol. J., 5(1), 26–50.
Dawson, C. W., and Wilby, R. L. (2001). “Hydrological modeling using artificial neural network.” Prog. Phys. Geog., 25(1), 80–108.
Doorenbos, J., and Pruitt, W. O. (1977). “Guidelines for prediction of crop water requirements.” FAO Irrigation and Drainage Paper no. 24, Rome.
Elshorbagy, A., and Parasuraman, K. (2008). “On the relevance of using artificial neural networks for estimating soil moisture content.” J. Hydrol., 362(1–2), 1–18.
Giles, L., and Maxwell, T. (1987). “Learning, invariance and generalization in high-order neural networks.” Appl. Opt., 26(23), 4972–4978.
Gupta, M. M., Jin, L., and Homma, N. (2003). Static and dynamic neural networks: From fundamentals to advanced theory, Wiley, New York.
Hargreaves, G. H., and Samani, Z. A. (1985). “Reference crop evapotranspiration from temperature.” Appl. Eng. Agric., 1(2), 96–99.
Homma, N., and Gupta, M. M. (2002). “Superimposing learning for backpropagation neural networks.” Bull. Coll. Med., 11(2), 253–259.
Huo, Z., Feng, S., Kang, S., and Dai, X. (2012). “Artificial neural network models for reference evapotranspiration in an arid area of northwest China.” J. Arid. Environ., 82, 81–90.
Itenfisu, D., Elliott, R. L., Allen, R. G., and Walter, I. A. (2003). “Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort.” J. Irrig. Drain. Eng., 440–448.
Jahanbani, H., and El-Shafie, A. H. (2011). “Application of artificial neural network in estimating monthly time series reference evapotranspiration with minimum and maximum temperatures.” Paddy Water Environ., 9(2), 207–220.
Jennifer, M. J., and Sudheer, R. S. (2001). “Evaluation of reference evapotranspiration methodologies and AFSIRS crop water use simulation model.” Final Rep., Division of Water Supply Management, St. Johns River Water Management District, Palatka, FL.
Jensen, M. E., Burman, R. D., and Allen, R. G. (1990). “Evapotranspiration and irrigation water requirements.” ASCE Manuals Rep. Eng. Pract. 70, ASCE, New York.
Kisi, O. (2011). “Modeling reference evapotranspiration using evolutionary neural networks.” J. Irrig. Drain. Eng., 636–643.
Kumar, M., Raghuwanshi, N. S., and Singh, R. (2009). “Development and validation of GANN model for evapotranspiration estimation.” J. Hydrol. Eng., 131–140.
Kumar, M., Raghuwanshi, N. S., and Singh, R. (2011). “Artificial neural networks approach in evapotranspiration modeling: A review.” Irrig. Sci., 29(1), 11–25.
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., and Pruitt, W. O. (2002). “Estimating evapotranspiration using artificial neural network.” J. Irrig. Drain. Eng., 224–233.
Laaboudi, A., Mouhouche, B., and Draoui, B. (2012). “Neural network approach to reference evapotranspiration modeling from limited climatic data in arid regions.” Int. J. Biometeorol., 56(5), 831–841.
Maier, H. R., and Dandy, G. C. (2000). “Neural network for the prediction and forecasting of water resources variables: A review of modeling issues and applications.” Environ. Modell. Softw., 15(1), 101–124.
Marti, P., and Gasque, M. (2010). “Ancillary data supply strategies for improvement of temperature-based ANN models.” Agric. Water Manage., 97(7), 939–955.
Marti, P., Royuela, A., Manzano, J., and Palau-Salvador, G. (2010). “Generalization of ANN models through data supplanting.” J. Irrig. Drain. Eng., 161–174.
MATLAB 7.0 [Computer software]. The MathWorks., Natick, MA.
Raghuwanshi, N. S., Singh, R., and Reddy, L. S. (2006). “Runoff and sediment yield modeling using artificial neural networks: Upper Siwane river, India.” J. Hydrol. Eng., 71–79.
Rahimikhoob, A. (2010). “Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran.” Theor. Appl. Climatol., 101(1-2), 83–91.
Redlapalli, S. K. (2004). “Development of neural units with higher-order synaptic operations and their applications to logic circuits and control problems.” M.S. thesis, Dept. of Mechanical Engineering, Univ. of Saskatchewan, Saskatoon, Canada.
Rumelhart, D. E., and McClelland, J. L. (1986). Parallel distributed processing: Explorations in the microstructure of cognition, MIT Press, Cambridge, MA.
Shiri, J., et al. (2013). “Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration.” J. Hydrol., 480, 46–57.
Smith, M., Allen, R., and Pereira, L. (1997). “Revised FAO methodology for crop water requirement.” Land and Water Development Division FAO, Rome.
Taylor, J. G., and Commbes, S. (1993). “Learning higher order correlations.” Neural Networks, 6(3), 423–427.
Tiwari, M. K., Song, K. Y., Chatterjee, C., and Gupta, M. M. (2012). “River flow forecasting using higher-order neural networks.” J. Hydrol. Eng., 655–666.
Turc, L. (1961). “Estimation of irrigation water requirements, potential evapotranspiration: A simple climatic formula evolved up to date.” Ann. Agron., 12(1), 13–49.
Zhang, M., Xu, S., and Fulcher, J. (2002). “Neuron-adaptive higher order neural-network models for automated financial data modeling.” IEEE Trans. Neural Networks, 13(1), 188–204.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 140 • Issue 12 • December 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Oct 16, 2013
Accepted: May 28, 2014
Published online: Jun 17, 2014
Discussion open until: Nov 17, 2014
Published in print: Dec 1, 2014
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