Open Access Open Access  Restricted Access Subscription Access
Cover Image

Evaporation modeling with artificial neural network: A review

Parameshwar Sidramappa Shirgure


Evaporation from the open pan as well as surface is a complex phenomenon of the hydrological cycle and influenced by many meteorological parameters, such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours. Measurement of evaporation with accuracy is and continuous is a difficult operation. In such situations, it becomes an imperative to use neural network models that can estimate evaporation from available climatic data and may give more accurate results than the measured pan evaporation. In this regard, a number of models for predicting the pan evaporation have been developed by several investigators for different locations of India and abroad. Most of the current models for predicting evaporation use the principles of the deterministically based combined energy balance – vapor transfer approach or empirical relationships based on climatological variables. This resulted in relationships that were often subjected to rigorous local calibrations and therefore proved to have limited global validity. Due to these limitations the conventionally applied regression modeling techniques need to be further refined to achieve improved performance by adopting new and advanced technique like neural networks. Evaporation process is complex and needs non-linear modeling and hence, can be modeled through Artificial Neural Networks (ANN). Large number of researchers have been established the applicability of artificial neural networks (ANNs) to the problems in agricultural, hydrological, meteorological and environmental fields. The review related to evaporation modeling using neural networks is discussed here in brief.


Altendorf, C.T., Elliott R.C., Stevens E.W., Stone M.L., 1999. Development and validation of a neural network model for soil water content prediction with comparison to regression techniques. Trans. ASAE 42 (3), 691-699.

Anderson, M.E., Jobson, H.E., 1982. Comparison of techniques for estimating annual lake evaporation using climatological data. Water Resources Res., 18, 630-636.

Arca, B., Benincasa, F., De Vincenzi, M., Ventura, A., 1998. Neural network to simulate evaporation from Class A pan. In: Proc. 23rd Conf. on Agricultural and Forest Meteorology, 2-6 November 1998, Albuquerque, New Mexico, Am. Meteorol. Soc., Boston, MA.

Ashrafzadeh, A., 1999. Application of artificial neural networks for prediction of evaporation from evaporative ponds, M.S. thesis, University of Tehran, Tehran.(in Persian), p 131.

Baier, W., Robertson, G.W., 1965. Evaluation of meteorological factors influencing evaporation. J. Hydrology. 45, 276 – 284.

Bocco, M., Ovando, G., Sayago, S., 2006. Development and evaluation of neural network models to estimate daily solar radiation at Cordoba, Argentina. Pesq. Agropec. Bras., Brasilia, 41(2), 179-184.

Bruin, H.A.R.D., 1978. A simple model for shallow lake evaporation. Applied Meterol., 17, 1132-1134.

Bruton, J.M., McClendon, R.W., Hoogenboom, G., 2000. Estimating daily pan evaporation with artificial neural networks. Trans. ASAE, 43 (2), 491-496.

Brutsaert, W., 1982. Evaporation into the atmosphere: Theory, history, and application. D. Reidel, 299 pp.

Chattopadhyay, N., Hulme M., 1997. Evaporation and potential evapotranspiration in India under conditions of recent and future climate change. Agriculture and Forest Met. 87 (1), 55-73.

Chattopadhyay, S., 2006. Mulilayered feed forward artificial neural network model to predict the average summer monsoon rainfall in India. URL :

Chaudhuri, S., Chattopadhyay, S., 2005. Neuro-Computing based short range prediction of some meteorological parameters during pre-monsoon season. Soft-computing – A fusion of foundations, Methodologies and Applications, 9 (5), 349-354.

Clayton, L.H., 1989. Prediction of class A pan evaporation in south Idaho. ASCE J. of Irrig. And Drain. Eng., 115 (2), 166-171.

Cook., D.F., Wolfe, M.L., 1991. A back propagation neural network to predict average air temperatures. AI applications, 5, 40-46.

Coulomb, C.V., Legesse, D., Gasse, F., Travi, Y., and Cherner, T., 2001. Lake evaporation estimates in tropical Africa (Lake Ziway, Ethopia). J. of Hydrology, 245, 1-18.

Deswal, S., Mahesh, P., 2008. Artificial Neural Network based Modeling of Evaporation Losses in Reservoirs. Procc. of World Academy of Science, Engineering and Technology, Vol. 29, 279-283.

Dimri, A.P., Mohanty, U.C., Madan, O.P., Ravi, N., 2002. Statistical model-based forecast of minimum and maximum temperatures at Manali. Current Science, 82 (8), 25-27.

Dogan, E., Demir, A.S., 2006. Evaporation amount estimation using Genetic algorithm and Neural networks. Proceedings of 5th International Symposium on Intellegent Manufacturing Systems, May 29-31, 2006, p 1239-1250.

Dorvlo, A.S.S., Jervase, J.A., Al-Lawati, A., 2002. Solar radiation estimation using artificial neural networks. Applied Energy, 71, 307-319.

Elizondo, D., Hoogenboom, G., McClendon, R.W., 1994. Development of a neural network model to predict daily solar radiation. Agric. For. Met., 71(1-2), 115-132.

Gavin, H., Agnew, C.A., 2004. Modeling actual, reference and equilibrium evaporation from a temperate wet grassland. Hydrol. Processes, 18, 229-246.

Gardner, M.W., Dorling, S.R., 1998. Artificial neural network (Multilayer Perceptron) a review of applications in atmospheric sciences. Atmospheric Environment, 32, 2627-2636.

Han, H., Felkar, P., 1997. Estimation of daily soil water evaporation using an artificial neural network. J. Arid Environ. 37 (2), 251-260.

Hanson, C.L., 1989. Prediction of class A pan evaporation in Southwest Idaho. J. Irrig. Drain. Engg., ASCE, 115 (2), 166-171.

Hoogenboom, G., 2000. Contribution of agrometeorology to the simulation of crop production and its applications, Agril. and Forest Met., 103 (1-2), 137-157.

Hu, M.J.C., 1964. Application of ADLINE system to weather forecasting, Technical Report, Stanford Electron.

Irmak, S., Irmak A., Jones J. W., Howell T. A., Jacobs J. M., Allen R. G. and Hoogenboom G. (2003). Predicting daily net radiation using minimum climatological data. J. Irri. Drain. Engg. ASCE, 129 (4), 256-269.

Jhajharia, D., Fancon, A.K., Kithan, S.B., 2005. Relationship between USWB class A pan evaporation and meteorological parameters under humid climatic conditions of Umiam, Meghalaya. In Proc. of International Conference on Recent advances in Water Resources Development and Management, Nov. 23-25, 2005, IIT Roorkee, Allied Pubs. PVT. 71 -83.

Jones, F.E., 1992. Evaporation of Water: With Emphasis on Applications and Measurements. Lewis, Chelsea, Michigan, 188 pp.

Keskin, M.E., Terzi, Ö., Taylan, D., 2004. Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrological Sciences Journal, 49 (6), 1001-1010.

Keskin, M.E., Terzi, O., 2006. Artificial Neural Network Models of Daily Pan Evaporation. J. Hydrologic Engrg., 11 (1), 65-70.

Khan, M.A., 1992. Evaporation of water from free water surface. Ind. J. Soil Cons. 20 (1/2), 22-27.

Kumar, M., Raghuwanshi, N.S., Singh, R., Wallender, W.W., Pruitt, W.O., 2002. Estimating evapotranspiration using artificial neural network. J. Irri. Drain. Engg. ASCE, 128 (4), 224-233.

Lee, S., Cho, S., Wong, P.M., 1998. Rainfall prediction using Artificial neural network. J. of Geographic Information and Decision Analysis, 2, 254-264.

Li, B., McClendon, R.W., Hoogenboom, G., 2004. Spatial interpolation of weather data for single locations using artificial neural networks. Transactions of the ASAE 47 (2), 629- 637.

Linacre, E.T., 1994. Class ‘A’ pan evaporation from few climatic data. Water Inter., 19 (1), 5 – 14.

Maqsood, I., Muhammad, R.K., Abraham, A., 2002. Neuro-computing based Canadian weather analysis. Computational Intelligence and applications, Dynamic Publishers, Inc. USA, 39-44.

Mehuys, G.R., Patni N.K., Prasher, S.O., Yang C.C., 1997. Application of artificial neural networks for simulation of soil temperature, Trans. of the ASAE, 40 (3), 649-656.

Mohandes, M.A., Rehman, S., Halawani, T.O., 1998. A neural networks approach for wind speed prediction. Renewable Energy, 13, 345-354.

Molina Martinez, J.M., Alvarezv, M., Gonzalez Real, M.M., Baille, A., 2006. A simulation model for predicting hourly pan evaporation from meteorological data. Journal of hydrology, 318 (1-4), 250-261.

Murthy, S., Gawande, S., 2006. Effect of metrological parameters on evaporation in small reservoirs ‘Anand Sagar’ Shegaon - a case study. J. Prudushan Nirmulan, 3 (2), 52-56.

Ozlem, T., Evolkesk, M., 2005. Modeling of daily pan evaporation. J. of Applied Sciences, 5 (2), 368-372.

Pachepsky, Y. A., Timlin, D.J., Varallvay, G., 1996. Artificial neural networks to estimate soil water retension from easily measurable data. Soil sciences soc. of American Journal, 60, 727-733.

Reddy, K.S., Ranjan, M., 2003. Solar resource estimation using artificial neural networks and comparison with other correlation models. Energy Conversion and Management, 44, 2519-2530.

Reis, R.J., Dias, N.L., 1998. Multi-season lake evaporation : energy budget estimates and CRLE model assessment with limited meteorological observations. J. of Hydrology, 208, 135-147.

Schaap, M.G., Bouten, W., 1996. Modeling water retention curves of sandy soils using neural networks. Water Resour. Res. 32 (10), 3033-3040.

Sharma, M.K., 1995. Estimation of pan evaporation using meteorological parameters. Hydrology. J. 18 (3-4), 1-9.

Shirgure, P.S., Srivastava, A.K., Singh, S., 2001. Effect of pan evaporation based irrigation scheduling on yield and quality of drip irrigated Nagpur mandarin. Indian Journal of Agricultural Sciences, 71 (4), 264 – 266.

Shirgure, P.S., Rajput, G.S., Seth N.K., 2011. Artificial neural network models for estimating daily pan evaporation. Abst. In The 45th Annual Convention of ISAE and International Symposium on Water for agriculture, held at College of Agriculture, Dr. PDKV, Nagpur during 17-19th Janurary, 2011, p-81.

Shirgure, P.S., Rajput G.S., 2011. Evaporation modeling with neural networks – A Research Review. International Journal of Research and Review in Soft Intelligent Computing. 1 (2), 37-47

Shirgure, P.S., Rajput, G.S., 2012. Prediction of daily pan evaporation using neural networks models. Scientific Journal of Agricultural, 1 (5), 126-137.

Shirgure, P.S., 2012. Evaporation modeling with multiple linear regression techniques - A review. Scientific Journal of Review, 1 (6), 170-182.

Singh, R.V., Chauhan, H.S., Ali, A.B.M., 1981. Pan evaporation as related to meteorological parameters. J. Agri. Engg., 18 (1), 48 – 53.

Smith, B.A., McClendon, R.W., Hoogenboom, G., 2006. Improving Air Temperature Prediction with Artificial Neural Networks, International Journal of Computational Intelligence. 3(3),179-186.

Snyder, R.L., 1993. Equation for evaporation pan to evapotranspiration conversions. J. Irrig. Drain. Engg., ASCE, 118 (6), 977-980.

Sudheer, K.P., Gosain, A.K., Mohana, R.D., Saheb, S.M., 2002. Modeling evaporation using an artificial neural network algorithm. Hyd. Processes, 16, 3189-3202.

Sudheer, K.P., Gosain, A.K., Ramasastri, K.S., 2003. Estimating actual evapotranspiration from limited climatic data using neural computing technique. J. Irri. Drain. Engg. ASCE. 129 (3), 214-218.

Tan Stephen Boon Kean , Eng Ban Shuy and Chua Lloyd Hock Chye (2007). Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrological processes, 21 (4), 486-499.

Tasadduq, I., Rehman ,S., Bubshait, K., 2002. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renewable Energy, 25, 545-554.

Taher, S.A., 2003. Estimation of potential evaporation : Artificial Neural Networks versus Conventional methods. J. King Saud Univ. Vol. 17. Eng. Sci., (1), 1-14.

Terzi, O., Keskn, M.E., 2005. Modeling of daily pan evaporation. Applied Sc., 5, 368-372.

Trajkovic, S., Stankovic, M., Todorovic, B., 2000. Estimation of FAO Blaney-Criddle b factor by RBF network. J. Irri. Drain. Engg. ASCE. 126(4), 268-274.

Trajkovic, S., Todorovic, B., Stankovic, M., 2001. Estimation of FAO Penman C factor by RBF networks. Architecture and Civil Engineering. 2 (3), 185-191.

Trajkovic, S., Todorovic, B., Stankovic, M., 2003. Forecasting of reference evapotranspiration using artificial neural network. J. Irri. Drain. Engg. ASCE 129 (6), 454-457.

Xu, C.Y., Singh, V.P., 1998. Dependence of evaporation on meteorological variables at different time scales and inter comparison of estimation methods. Hyd. Processes, 12 (3), 429-442.

Xu, C.Y., Singh, V.P., 2001. Evaluation and generalization of temperature-based methods for calculating evaporation. Hyd. Processes, 15 (2), 305 – 319.

Full Text: PDF


  • There are currently no refbacks.