Evapotranspiration modelling from climatic data using a neural computing technique

Kisi, Özgür

doi:10.1002/hyp.6403

Cited by 148 publications

(62 citation statements)

References 40 publications

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“…Recently, the outstanding results using the neural networks model in the fields of the evaporation and evapotranspiration have been obtained (Bruton et al, 2000;Sudheer et al, 2003;Trajkovic et al, 2003;Keskin & Terzi, 2006;Kisi, 2006b;Kisi, 2007;Parasuraman et al, 2007;Kim et al, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Chari¹,

Nemati²,

Afrasiab³

et al. 2012

JAS

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The relation between water table depth and evaporation rate from bare soil is of great importance in arid and semi-arid areas. In such areas, due to over irrigation, the water table is very close to the ground surface which leads to salinization of the soil. In this study a physical water table model was used to estimate evaporation rate in sandy loam, loam and clay loam soils under greenhouse conditions for 40, 60 and 80 cm water table depth. The evaporation from bare soil, evaporation from free surface, soil surface moisture (with using TDR) and maximum and minimum daily temperature were measured for 74 days in this study. In the next step, several nonlinear models have been efficiently developed with the aid of the Gamma test (GT), including local linear regression, two layer back propagation, and conjugate gradient descent and BFGS neural network to simulate the evaporation from soil. And finally, for evaluation of the models, the root mean square error and mean absolute error and larger determination coefficient were calculated. The results showed a suitable correlation between the predicted values and the test measures.

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Section: Introductionmentioning

confidence: 99%

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Chari¹,

Nemati²,

Afrasiab³

et al. 2012

JAS

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“…Thus, calculation of PET from the PM equation requires eight predictors that are not always available in developing countries such as India. The ANN technique is useful and suitable in estimating the evapotranspiration from limited climatic data over the conventional procedures of estimating evapotranspiration (Sudheer et al, 2003;Kisi, 2007;Chauhan and Shrivastava, 2008;Jain et al, 2008;Wang et al, 2008). Landeras et al (2008) highlighted the necessity of application of an ANN in modelling a non-linear process such as evapotranspiration and, after comparing seven ANN models against various locally calibrated reference evapotranspiration equations, they concluded that ANN-based models were more reliability compared to the locally calibrated equations.…”

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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“…Therefore, to understand the ET mechanism, it is needed to understand evaporation and transpiration. Basically, evaporation takes place at the topsoil when the water is available and transpiration is the removal of vapour to the atmosphere from the crop tissues (Kişi 2007).…”

Section: Introductionmentioning

confidence: 99%

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA

Üneş

Kaya

Mamak

et al. 2017

Proccedings of 10th International Conference "Environmental Engineering"

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Abstract. Information about Evapotranspiration (ET) calculations are not clear enough even it is an important part of hydrological cycle. There are many parameters which effect ET directly or indirectly such as Solar Radiation (SR) and Air Temperature (AT). In this study authors focused on the modelling ET using Support Vector Machines (SVM) method because this method has abilities to solve nonlinear problems. For the training SVM 1158 daily AT, SR, Wind Speed (U) and Relative Humidity (RH) meteorological parameters are used and model is tested using 385 daily parameters. Data set is taken from St. Johns, Florida, USA weather station. To understand the abilities of SVM for ET prediction against Hargreaves-Samani formula, the test set is applied to this empirical equation. Determination coefficient of SVM with observed daily ET values is calculated as 0.913 and determination coefficient of HargreavesSamani formula with observed daily ET is found as 0.910. Comparison between both methods is done using Mean Square Error (MSE), Mean Absolute Error (MEA) and determination coefficient statistics. As a result it is seen that SVM method is trustier than Hargreaves-Samani formula for daily ET prediction.

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Evapotranspiration modelling from climatic data using a neural computing technique

Cited by 148 publications

References 40 publications

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

Prediction of Evaporation from Shallow Water Table Using Regression and Artificial Neural Networks

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

Evapotranspiration Estimation Using Support Vector Machines and Hargreaves-Samani Equation for St. Johns, FL, USA

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