Estimating Evapotranspiration using Artificial Neural Network

Kumar, M.; Raghuwanshi, N. S.; Singh, Rajendra Prasad; Wallender, W. W.; Pruitt, William O.

doi:10.1061/(asce)0733-9437(2002)128:4(224)

Cited by 438 publications

(242 citation statements)

References 20 publications

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“…Birikundavyi et al ͑2002͒ found that an ANN can achieve accuracy superior to that of ARMAX and deterministic models for 7-day ahead forecasting. Kumar et al ͑2002͒ concluded that the ANN can predict reference crop evapotranspiration for an area better than the Penman-Monteith method.…”

Section: Artificial Neural Network Applications In Water Resourcesmentioning

confidence: 99%

Analysis of Soil Water Retention Data Using Artificial Neural Networks

2004

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Many studies of water flow and solute transport in the vadose zone require estimates of the unsaturated soil hydraulic properties, including the soil water retention curve ͑WRC͒ describing the relationship between soil suction and water content. An artificial neural network ͑ANN͒ approach was developed to describe the WRC using observed data from several soils. The ANN approach was found to produce equally or more accurate descriptions of the retention data as compared to several analytical retention functions popularly used in the vadose zone hydrology literature. Given sufficient input data, the ANN approach was also found to closely describe the hysteretic behavior of a soil, including observed scanning wetting and drying curves.

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Section: Artificial Neural Network Applications In Water Resourcesmentioning

confidence: 99%

Analysis of Soil Water Retention Data Using Artificial Neural Networks

2004

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“…Kumar et al (2002) The EPR model provided a performance comparable to that of GP and ANN models. Tabari and Talaee (2012) indicated that the main obstacle in application of FAO P-M Equation is the wide range of meteorological data essential as an input for calculation of ET₀, and because of the nonlinearity of ET phenomenon.…”

Section: Introductionmentioning

confidence: 87%

Hybrid of Artificial Neural Network-Genetic Algorithm for Prediction of Reference Evapotranspiration (ET?) in Arid and Semiarid Regions

Abdullah¹,

Malek²,

Mustapha³

et al. 2014

JAS

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Evapotranspiration is a principal requirement in designing any irrigation project, especially in arid and semiarid regions. Precise prediction of Evapotranspiration would reduce the squandering of huge quantities of water. Feedforward Backpropagation Neural Network (FFBPNN) model is employed in this study to evaluate the performance of Artificial Neural Networks (ANNs) in comparison with Empirical FAO Penman-Monteith (P-M) Equation in predicting reference evapotranspiration (ET₀); later, a hybrid model of ANN-Genetic Algorithm (GA) is proposed for the same evaluation function. Daily averages of maximum air temperature (T max ), minimum air temperature (T min ), relative humidity (R h ), radiation hours (R), and wind speed (U 2 ) from Mosul station (Nineveh, Iraq) are used as inputs to the ANN simulation model to predict ET₀ values obtained using P-M Equation. The main performance evaluation functions for both models are the Mean Square Errors (MSE) and the Correlation Coefficient (R 2 ). Both models yield promising results, but the hybrid model shows a higher efficiency in prediction of Evapotranspiration and could be recommended for modeling ET₀ in arid and semiarid regions.

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“…In all of the available studies, ANN models were developed using fewer predictors than required by PM equations. In many studies, wind speed, relative humidity, air temperature and solar radiation have been used as predictors in ANN based models for evapotranspiration (Kumar et al, 2002;Trajkovic et al, 2003;Dai et al, 2009;Dogan, 2008;Kisi, 2009).…”

Section: Artificial Neural Network In Evapotranspiration Study-a Reviewmentioning

confidence: 99%

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

Chattopadhyay¹,

Jain²,

Chattopadhyay

2009

Meteorological Applications

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This paper reports the development of a neurocomputing-based model for estimating the potential evapotranspiration over Gangetic West Bengal, India during the summer monsoon months of June, July and August. An artificial neural network is implemented in the form of multilayer perceptron to generate the model. Three weather variables, surface temperature, vapour pressure and rainfall are used as the independent variables in generating the model. The performance of the model is judged statistically against non-linear regression in the form of asymptotic regression. The study reveals that an artificial neural network is more efficient than the regression approach to estimate the potential evapotranspiration in the summer monsoon months. Furthermore, it is established that the artificial neural network and non-linear regression have almost equal efficiency in the previously mentioned estimation in the month of June. However, in July and August the higher values of correlation and Willmott's indices, and lower values of estimation error, indicate that the artificial neural network is more reliable than the non-linear regression approach. Since evapotranspiration is one of the basic components of the hydrological cycle and is essential for estimating irrigation water requirement, an efficient estimation procedure may help in agrometeorological modelling and irrigation scheduling in the summer monsoon months, which are of high importance for agriculture in the study zone.

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Estimating Evapotranspiration using Artificial Neural Network

Cited by 438 publications

References 20 publications

Analysis of Soil Water Retention Data Using Artificial Neural Networks

Analysis of Soil Water Retention Data Using Artificial Neural Networks

Hybrid of Artificial Neural Network-Genetic Algorithm for Prediction of Reference Evapotranspiration (ET?) in Arid and Semiarid Regions

Estimating potential evapotranspiration from limited weather data over Gangetic West Bengal, India: a neurocomputing approach

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