Modelling of Reference Evapotranspiration for Semi-arid Climates Using Artificial Neural Network (original) (raw)
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International Journal of Biometeorology, 2012
In order to better manage the limited water resources in arid regions, accurate determination of plant water requirements is necessary. For that, the evaluation of reference evapotranspiration (ET0)-a basic component of the hydrological cycle-is essential. In this context, the Penman Monteith equation, known for its accuracy, requires a high number of climatic parameters that are not always fully available from most meteorological stations. Our study examines the effectiveness of the use of artificial neural networks (ANN) for the evaluation of ET0 using incomplete meteorological parameters. These neural networks use daily climatic data (temperature, relative humidity, wind speed and the insolation duration) as inputs, and ET0 values estimated by the Penman-Monteith formula as outputs. The results show that the proper choice of neural network architecture allows not only error minimization but also maximizes the relationship between the dependent variable and the independent variables. In fact, with a network of two hidden layers and eight neurons per layer, we obtained, during the test phase, values of 1, 1 and 0.01 for the determination coefficient, the criterion of Nash and the mean square error, respectively. Comparing results between multiple linear regression and the neural method revealed the good modeling quality and high performance of the latter, due to the possibility of improving performance criteria. In this work, we considered correlations between input variables that improve the accuracy of the model and do not pose problems of multi-collinearity. Furthermore, we succeeded in avoiding overfitting and could generalize the model for other similar areas.
Modelling Daily Evapotranspiration Using Artificial Neural Networks Under Hyper Arid Conditions
Pakistan Journal of Agricultural Sciences, 2016
Precisely determined evapotranspiration (ET) is necessary for maximization of water beneficiary use and hydrologic applications, particularly in arid and semiarid regions where water source is so limited, such as Saudi Arabia. Evapotranspiration is a complex, nonlinear process. However, data driven techniques can be used model it without requiring a complete understanding of the physics involved. Therefore, the Artificial Neural Networks (ANN) technique was used to estimate the daily reference evapotranspiration (ETref). Eight combinations of eight climatic parameters and crop height were used as input. The daily climatic variables were collected by 13 meteorological stations from 1980 to 2010. The ANN models were trained on 65% of the climatic data and tested using the remaining 35%. The generalised Penman-Monteith (PMG) model was used as a reference target for evapotranspiration values, with hc varies from 5 to 105 cm with increment of a centimeter. The developed models were spati...
Agronomy, 2022
Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected para...
The objective of this study is to investigate the potential of artificial neural networks (ANNs) for estimating reference monthly evapotranspiration under arid and semi-arid environments. A simple leave one out data analysis was carried out; one neural network solution on six inputs and another six network solutions on five inputs for each monitoring station were done. Comparison of the results showed that the accuracy of ANNs is decreased when relative humidity, wind speed and solar or extraterrestrial radiation are excluded as input variables. The results also showed that monthly evapotranspiration could be computed with relatively good accuracy compared with local calibrated Hargreaves equation based on air temperature using trained ANNs at another location. We conclude, based on our overall results, that temperature-based method ANNs can be used with relatively good accuracy for water resource management, irrigation scheduling and management, and environmental assessment when data are not enough using trained ANNs from another location.
In the present study, an attempt has been made to compare the reference evapotranspiration (ET o), computed by eight different methods, namely, Penman-Monteith, Modified Penman-Monteith, Hargreaves-Samani, Irmak, Hargreaves, Valiantzas, ANN and FAO(24) model for the dry sub-humid agro-ecological region (Varanasi). An attempt was also made to find out utility of artificial neural networks (ANN) for estimation of ET 0 with minimum input. Feed forward network has been used for prediction of ETo using resilient back-propagation method and the architecture 2-2-1(having parameters T mean and solar radiation) was found to be the best one. The average annual evapotranspiration (by Penman–Monteith method) for Varanasi was found to be 1447.4 mm. When compared among the different methods for estimation of reference evapotranspiration with Penman-Monteith method, the FAO-24 and Hargraves-Samani (3) under estimate, however, Modified Penman-Monteith, Hargreaves-Samani, Irmak, Hargreaves, Valiantzas overestimate and ANN closely estimates reference evapotranspiration.
Using Artificial Neural Network to Estimate Reference Evapotranspiration
Global Science and Technology, 2018
Irrigation, when rationally used, can contribute to the efficient performance of the agribusiness. Planning irrigation, monitoring the soil moisture, the rainfall and the reference evapotranspiration (ET 0 ) is necessary for a rational water management. The FAO Penman-Monteith (FAO PM) method is the standard method for estimating ET 0 , but in some cases, the use of this method is restricted due to missing some climatic variables. For this reason, methods with a lower number of meteorological variables, as temperature values, are quite often used. This study aims to propose an artificial neural network (ANN) to estimate the ET 0 as a function of maximum and minimum air temperatures for the city of Salinas, Minas Gerais State, Brazil. After training, validation and comparison with the Hargreaves methodology, it was observed the existence of a good correlation between the values estimated by the standard method and those estimated by ANN, with the performance index classified as optim...
Reference Evapotranspiration Estimation with Artificial Neural Networks
2017
Irrigation, when rationally used, can contribute to the efficient performance of the agribusiness. Planning irrigation, monitoring the soil moisture, the rainfall and the reference evapotranspiration (ETo) is necessary for a rational water management. The FAO Penman-Monteith (FAO PM) method is the standard method for estimating ETo, but in some cases, the use of this method is restricted due to missing some climatic variables. For this reason, methods with a lower number of meteorological variables, as temperature values, are quite often used. This study aims to propose an artificial neural network (ANN) to estimate the ETo as a function of maximum and minimum air temperatures for the city of Salinas, Minas Gerais State, Brazil. After training and validating the ANN, it was observed the existence of a good correlation between the values estimated by the standard method and those estimated by ANN, with the performance index classified as optimal. The use of ANN proved to be an excellent alternative for ETo estimation, reducing the costs of acquiring climatic data.
Evaluation of neural network techniques for estimating evapotranspiration
The evapotranspiration is one of the basic components of the hydrologic cycle and is essential for estimating irrigation water requirements. This paper describe the results obtained using neural network techniques to improve the accuracy of reference evapotranspiration estimation. A comparison was made between the estimates provided by the neural networks models and the following empirical models: Hargreaves, Hargreaves-Samani, Blaney-Criddle. The results showed that the neural networks performed much better than all the empirical models; the best results were obtained using as neural networks inputs the values of evapotranspiration estimated by the empirical models and, in addition, the values of some meteorological variables that affect the evapotranspiration phenomena. Neural networks were able to reduce both the value of root mean square error of estimates and the systematic overestimation or underestimation. The study confirm the high structural and functional modularity of neural networks and show the capabilities of neural networks as tool for modelling evapotranspiration processes.
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.