Estimating Evapotranspiration Using an Extreme Learning Machine Model: Case Study in North Bihar, India
This article has a reply.
VIEW THE REPLYPublication: Journal of Irrigation and Drainage Engineering
Volume 142, Issue 9
Abstract
The effective scheduling of irrigation requires knowledge of a crop’s consumptive water use according to its metabolic activities. Conversely, to know a crop’s consumptive water use, one must know its exact evapotranspiration (ET) rate. Although the United Nations’ Food and Agricultural Organization (FAO) has recommended using the standard Penman-Monteith method to determine crop ET (), the method’s intricacies render it impractical to use in the field in predicting agricultural and irrigation requirement-based water needs. The present study investigated the use of a new approach, extreme learning machines (ELMs), for estimating using climatic variables such as temperature, relative humidity, rainfall, sunshine hours, and wind speed. ELM is a single, hidden layer, feed-forward network that provides a unified learning platform with widespread types of feature mappings. It can also be applied in regression. This study compares results obtained using the standard Penman-Monteith method, ELM, artificial neural networks (ANNs), genetic programming (GP), and support vector machines (SVMs). Results suggest that ELM can predict ET more quickly and accurately than all other techniques tested. An ELM with sigmoid transfer function predicted ET with greater accuracy than a hard limit transfer function.
Get full access to this article
View all available purchase options and get full access to this article.
References
Adamowski, J., Adamowski, K., and Bougadis, J. (2010). “Influence of trend on short duration design storms.” Water Resour. Manage., 24(3), 401–413.
Adamowski, J., Chan, H., Prasher, S., and Sharda, V. N. (2012a). “Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data.” J. Hydroinf., 14(3), 731–744.
Adamowski, J., and Chan, H. F. (2011). “A wavelet neural network conjunction model for groundwater level forecasting.” J. Hydrol., 407(1-4), 28–40.
Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga-Zielinski, B., and Sliusarieva, A. (2012b). “Comparison of multiple linear and nonlinear regressions, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada.” Water Resour. Res., 48(1), W01528.
Adamowski, J. F. (2008). “Peak daily water demand forecast modeling using artificial neural networks.” J. Water Resour. Plann. Manage., 119–128.
Adamowski, K., Prokoph, A., and Adamowski, J. (2009). “Development of a new method of wavelet aided trend detection and estimation.” Hydrol. Process., 23(18), 2686–2696.
Allen, R. G. (2005). “FAO-56 dual crop coefficient method for estimating evaporation from soil and application extensions.” J. Irrig. Drain. Eng., 2–13.
Allen, R. G., Pereira, L. A., Raes, D., and Smith, M. (1998). “Crop evapotranspiration.” 〈http://www.fao.org/docrep/x0490e/x0490e00.htm〉 (Jul. 17, 2015).
Allen, R. G., Smith, M., Pereira, L. S., and Perrier, A. (1994). “An update for the calculation of reference evapotranspiration.” Int. Commission Irrig. Drain. (ICID) Bull., 43(2), 1–34.
Allen, R. G., Tasumi, M., and Trezza, R. (2007). “Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)—Model.” J. Irrig. Drain. Eng., 380–394.
Bai, Z., Kasun, L. L. C., and Huang, G.-B. (2015). “Generic object recognition with local receptive fields based extreme learning machine.” IEEE Comput. Intell. Mag., 10(2), 18–29.
Belayneh, A., Adamowski, J., Khalil, B., and Ozga-Zielinski, B. (2014). “Long-term SPI drought forecasting in the Awash River basin in Ethiopia using wavelet-support vector regression models.” J. Hydrol., 508, 418–429.
Blaney, H. F., and Criddle, W. D. (1950). “Determining water requirements in irrigated area from climatological and irrigation data.”, USDA, Soil Conservation Service, Washington, DC.
Campisi, S., Adamowski, J., and Oron, G. (2012). “Forecasting urban water demand via wavelet- denoising and neural network models. Case study: City of Syracuse, Italy.” Water Resour. Manage., 26(12), 3539–3558.
Chang, C.-C., and Lin, C.-J. (2011). “LIBSVM: A library for support vector machines.” ACM Trans. Intell. Syst. Technol., 2(3), 1–27).
Discipulus version 3 [Computer software]. Register Machine Learning Technologies, Littleton, CO.
Doorenbos, J., and Pruitt, W. O. (1975). “Guidelines for predicting crop water requirements.” FAO, Rome.
Doorenbos, J., and Pruitt, W. O. (1977). “Guidelines for predicting crop water requirements. Revised.” 〈http://www.fao.org/docrep/018/f2430e/f2430e.pdf〉 (Jul. 17, 2015).
Efthimiou, N., Alexandris, S., Karavitis, C., and Mamassis, N. (2013). “Comparative analysis of reference evapotranspiration estimation between various methods and the FAO56 Penman-Monteith procedure.” Eur. Water, 42, 19–34.
Goyal, M. K., Bharti, B., Quilty, J., Adamowski, J., and Pandey, A. (2014). “Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS.” Expert Syst. Appl., 41(11), 5267–5276.
Haidary, A., Amiri, B. J., Adamowski, J., Fohrer, N., and Nakane, K. (2013). “Assessing the impacts of four land use types on the water quality of wetlands in Japan.” Water Resour. Manage., 27(7), 2217–2229.
Halbe, J., Pahl-Wostl, C., Sendzimir, J., and Adamowski, J. (2013). “Towards adaptive and integrated management paradigms to meet the challenges of water governance.” Water Sci. Technol.: Water Supply, 67(11), 2651–2660.
Huang, G., Song, S., Gupta, J. N. D., and Wu, C. (2014). “Semi-supervised and unsupervised extreme learning machines.” IEEE Trans. Cybern., 44(12), 2405–2417.
Huang, G.-B. (2014). “An insight into extreme learning machines: Random neurons, random features and kernels.” Cognit. Comput., 6(3), 376–390.
Huang, G.-B., Bai, Z., Kasun, L. L. C., and Vong, C. M. (2015). “Local receptive fields based extreme learning machine.” IEEE Comput. Intell. Mag., 10(2), 18–29.
Huang, G.-B., Zhou, H., Ding, X., and Zhang, R. (2012). “Extreme learning machine for regression and multiclass classification.” IEEE Trans. Syst. Man Cybern. Part B: Cybern., 42(2), 513–529.
Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2006). “Extreme learning machine: Theory and applications.” Neurocomputing, 70(1), 489–501.
Inam, A., Adamowski, J., Halbe, J., and Prasher, S. (2015). “Using causal loop diagrams for the initialization of stakeholder engagement in soil salinity management in agricultural watersheds in developing countries: A case study in the Rechna Doab watershed, Pakistan.” J. Environ. Manage., 152, 251–267.
Iosifidis, A., Tefas, A., and Pitas, I. (2013). “Dynamic action recognition based on dynemes and extreme learning machine.” Pattern Recognit. Lett., 34(15), 1890–1898.
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.
Karimi, Y., Prasher, S. O., Patel, R. M., and Kim, S. H. (2006). “Application of support vector machine technology for weed and nitrogen stress detection in corn.” Comput. Electron. Agric., 51(1–2), 99–109.
Kasun, L. L. C., Zhou, H., Huang, G.-B., and Vong, C. M. (2013). “Representational learning with extreme learning machine for big data.” IEEE Intell. Syst., 28(6), 31–34.
Koirala, S., Jung, M., Carvalhais, N., de Graaf, I. E. M., and Reichstein, M. (2014). “Spatio-temporal covariation of evapotranspiration, plant productivity, and groundwater dynamics at the global scale.” AGU Fall Meeting Abstracts, Vol. 1, San Francisco, 926.
Kolinjivadi, V., Adamowski, J., and Kosoy, N. (2014). “Recasting payments for ecosystem services (PES) in water resource management: A novel institutional approach.” Ecosyst. Serv., 10, 144–154.
Kolinjivadi, V., Adamowski, J., and Kosoy, N. (2015). “Juggling multiple dimensions in a complex socioecosystem: The issue of targeting in payments for ecosystem services.” GeoForum, 58, 1–13.
Kuo, S. F., Chen, F. W., Liao, P. Y., and Liu, C. W. (2011). “A comparative study on the estimation of evapotranspiration using backpropagation neural network: Penman-Monteith method versus pan evaporation method.” Paddy Water Environ., 9(4), 413–424.
Lakshman, N., and Kovoor, G. M. (2005). “Sensitivity of the food and agriculture organization Penman-Monteith evapotranspiration estimates to alternative procedures for estimation of parameters.” J. Irrig. Drain. Eng., 238–248.
Lee, Y.-S., and Tong, L.-I. (2011). “Forecasting energy consumption using a grey model improved by incorporating genetic programming.” Energy Convers. Manage., 52(1), 147–152.
Liang, N.-Y., Huang, G.-B., Saratchandran, P., and Sundararajan, N. (2006). “A fast and accurate on-line sequential learning algorithm for feed forward networks.” IEEE Trans. Neural Networks, 17(6), 1411–1423.
Lowry, R. C., and Johnson, A. P. (1941). “Consumptive use of water for agriculture.” Trans. ASCE, 107, 1213–1302.
Makkeasorn, A., Chang, N. B., and Zhou, X. (2008). “Short-term stream flow forecasting with global climate change implications—A comparative study between genetic programming and neural network models.” J. Hydrol., 352(3-4), 336–354.
MATLAB version 7.10.0 [Computer software]. MathWorks, Natick, MA.
Mohan, S., and Arumugam, N. (1996). “Discussion of comparison of methods for estimating REF-ET.” J. Irrig. Drain. Eng., 361–364.
Mu, Q., Zhao, M., and Running, S. W. (2011). “Improvements to a MODIS global terrestrial evapotranspiration algorithm.” Remote Sens. Environ., 115(8), 1781–1800.
Nalley, D., Adamowski, J., and Khalil, B. (2012). “Using discrete wavelet transforms to analyze trends in streamflow and precipitation in Quebec and Ontario (1954– 2008).” J. Hydrol., 475, 204–228.
Nalley, D., Adamowski, J., Khalil, B., and Ozga-Zielinski, B. (2013). “Trend detection in surface air temperature in Ontario and Quebec, Canada during 1967-2006 using the discrete wavelet transform.” J. Atmos. Res., 132/133, 375–398.
Oliveira, C. W., and Yoder, R. E. (2000). “Evaluation of daily reference evapotranspiration estimation methods for a location in southeastern USA.”, ASAE, St. Joseph, MI.
Penman, H. L. (1963). “Vegetation and hydrology.” Commonwealth bureau of soils, Commonwealth Agricultural Bureaux, Farham Royal, U.K.
Saadat, H., Adamowski, J., Bonnell, R., Sharifi, F., Namdar, M., and Ale-Ebrahim, S. (2011). “Land use and land cover classification over a large area in Iran based on single date analysis of satellite imagery.” J. Photogramm. Remote Sens., 66(5), 608–619.
Smith, M. (1992). “CROPWAT-A Computer programme for Irrigation planning and management.” FAO, Rome.
Snyder, R. L., Orang, M., and Grismer, M. E. (2005). “Simplified estimation of Reference evapotranspiration from Pan evaporation data in California.” J. Irrig. Drain. Eng., 249–253.
Straith, D., Adamowski, J., and Reilly, K. (2014). “Exploring the attributes, strategies and contextual knowledge of champions of change in the Canadian water sector.” Can. Water Resour. J., 39(3), 255–269.
Sudheer, C., Kumar, D., Prasad, R. K., and Mathur, S. (2013). “Optimal design of an in-situ bioremediation system using support vector machine and particle swarm optimization.” J. Contam. Hydrol., 151, 105–116.
Suleiman, A., and Hoogenboom, G. (2007). “Comparison of Priestley-Taylor and FAO-56 Penman-Monteith for daily reference evapotranspiration estimation in Georgia.” J. Irrig. Drain. Eng., 175–182.
Suryanaryana, C., Sudheer, C., Mahammood, V., and Panigrahi, B. K. (2014). “An integrated wavelet-support vector machine for groundwater level prediction in Visakhapatnam, India.” Neurocomputing, 145, 324–335.
Tegos, A., Efstratiadis, A., and Koutsoyiannis, D. (2013). “A parametric model for potential evapotranspiration estimation based on a simplified formulation of the Penman-Monteith equation.” Evapotranspiration—An overview, S. Alexandris, InTech.
Thornthwaite, C. W. (1948). “An approach toward a rational classification of climate.” Geog. Rev., 38(1), 55–94.
Tiwari, M., and Adamowski, J. (2013). “Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models.” Water Resour. Res., 49(10), 6486–6507.
Trebar, M., and Steele, M. (2008). “Application of distributed SVM architectures in classifying forest data cover types.” Comput. Electron. Agric., 63(2), 119–130.
Turc, L. (1961). “Evaluation des besoins en eau d’irrigation. Evapotranspiration potentielle [Formule climatique simplifiée et mise a jour].” Ann. Agronomiques, 12(1), 13–49 (in French).
Uno, Y., et al. (2005). “Artificial neural networks to predict corn yield from compact airborne spectrographic imager data.” Comput. Electron. Agric., 47(2), 149–161.
Vapnik, V., and Cortes, C. (1995). “Support-vector networks.” Mach. Learn., 20(3), 273–297.
Vicente-Serrano, S. M., Beguería, S., and López-Moreno, J. I. (2010). “A multiscalar drought index sensitive to global warming: The standardized precipitation evapotranspiration index.” J. Clim., 23(7), 1696–1718.
Wright, J. (1982). “New evapotranspiration crop coefficients.” J. Irrig. Drain. Div., 108(1), 57–74.
Zotarelli, L., Dukes, M. D., Romero, C. C., Migliaccio, K. W., and Morgan, K. T. (2010). “Step by step calculation of the Penman-Monteith evapotranspiration (FAO-56 method).”, Agricultural and Biological Engineering Dept., Florida Cooperative Extension Service.
Information & Authors
Information
Published In
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Aug 12, 2015
Accepted: Feb 8, 2016
Published online: May 6, 2016
Published in print: Sep 1, 2016
Discussion open until: Oct 6, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.