Crop Water Requirement Under Micro Irrigation Systems Using Different Evapotranspiration Estimation Techniques (original) (raw)
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Modelling of Reference Evapotranspiration for Semi-arid Climates Using Artificial Neural Network
2021
Reference Evapotranspiration (ET0) is one of the prominent hydrologic variables affecting water and energy balances and critical factors for crop water requirements and irrigation scheduling. Evapotranspiration is a complex hydrological variable defined by various climatic variables. Various empirical formulations have been developed to estimate ET0 depending upon the availability of meteorological variables. Such empirical formulations are region-specific and are for particular climatic conditions. In this context, mathematical models have emerged as simple and readily implementable for the estimation of ET0 with measured meteorological parameters as independent variables. Such data-driven models can be valuable to predict ET0 when climate data is insufficient. The present study compared various empirical models and data-driven algorithms to predict ET0 using various climate variables. Artificial neural networks (ANN) were adopted to estimate reference ET0. Four empirical methods P...
This study is an attempt to find best alternative method to estimate reference evapotranspiration (ETo) for the Nagarjuna Sagar Reservoir Project [NSRP], command area located at Andhra Pradesh, India. When input climatic parameters are insufficient to apply standard Food and Agriculture Organization (FAO) of the United Nations Penman–Monteith (P–M) method. To identify the best alternative climatic based method that yield results closest to the P–M method, performances of four climate based methods namely Blaney–Criddle, Radiation, Modified Penman and Pan evaporation were compared with the FAO-56 Penman–Monteith method. Performances were evaluated using the statistical indices. The statistical indices used in the analysis were the standard error of estimate (SEE), raw standard error of estimate (RSEE) and the model efficiency. Study was extended to identify the ability of Artificial Neural Networks (ANNs) for estimation of ETo in comparison to climatic based methods. The networks, using varied input combinations of climatic variables have been trained using the back propagation with variable learning rate training algorithm. ANN models were performed better than the climatic based methods in all performance indices. The analyses of results of ANN model suggest that the ETo can be estimated from maximum and minimum temperature using ANN approach in NSRP area.
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...
This study compares the daily crop evapotranspiration estimated by artificial neural network (ANN), neural network-genetic algorithm (NNG), and nonlinear regression (NLR) methods. Using a 6-year (2000)(2001)(2003)(2004)(2005) daily meteorological data, recorded at Tabriz synoptic station, and Penman-Monteith FAO 56 standard approach (PMF 56), the daily crop evapotranspiration (ET C ) was determined during the growing season (April-September). Air temperature, wind speed at 2 meters height, net solar radiation, air pressure, relative humidity and crop coefficient for every day of the growing season were selected as the input of artificial neural network models. In this study, the genetic algorithm was applied for optimization of parameters which used in ANN approach. It was found that the optimization of input parameters did not improve the performance of ANN method. The results indicate that NLR, ANN and NNG methods were able to predict potato crop evapotranspiration (ET C ) at the desirable level of accuracy. However, the NLR method with highest coefficient of determination (R 2 > 0.96, P value<0.05) and minimum errors provided the superior performance among the other methods.
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.
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.
Theoretical and Applied Climatology
Evapotranspiration (ET) is a main factor of the hydrologic balance. Estimating precise ET is necessary for managing the water supply in a basin. In this study, daily barley standard evapotranspiration (DBSE) is obtained (1) directly by weighing lysimeter and (2) indirect methods. In the first step, DBSE was obtained by two weighing lysimeters in a semi-arid region (Kooshkak, Iran). In the next step, indirect methods for estimating the DBSE, the Penman–Monteith (PM), and the artificial neural networks (ANNs), including the radial basis functions (RBF), and the multi-layer perceptron (MLP), were utilized. Results showed that DBSE can be successfully calculated in semi-arid region by MLP-ANN, RBF-ANN, and PM methods. The ANN methods were offered as the best method because they need fewer input data and can be easily used for other developed programs that applied for water allocation and therefore solve the conflicts between stakeholders and optimize water usage. Finally, the sensitivity of ANNs to input data was investigated by relating changes in the daily metrological data to the dimensionless scaled sensitivities (DSS) index. Results showed that in the multi-layer perceptron, ET c was more sensitive to sunshine hours and less sensitive to wind speed and the radial basic function has different patterns which are more sensitive to sunshine hours. When the sunshine hours decrease by more than 10%, the standard crop evapotranspiration (ET c ) was more sensitive to average humidity and less sensitive to wind speed.
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...
In the present study an attempt has been develop artificial neural network (ANN) for estimation of daily pan evaporation (Ep) for Hawalbagh, Uttarakhand, India. The daily data of temperature (T), relative humidity (Rh), wind velocity (W), sunshine hours (S) and evaporation (Ep) data of years 2010-2012 were used to train the models and remaining data of year 2013 were used for test the models of ANN. The daily meteorological data of 4 years such as temperature, relative humidity, wind velocity and sunshine hours as input parameters and evaporation as output. The artificial neural networks were used for designing of models based on different learning algorithm (DBD, L-M, Step, Momentum, C-G and Quickprop) activation function TanhAxon, sigmoid and linear sigmoid were used evaporation estimation. The performances indices of the models were evaluated by; viz. normalized mean square error (NMSE), and coefficient of efficiency (CE). The comparisons of the different combinations of ANN models and select a best suited model for estimating daily pan evaporation of the Hawalbagh Uttarakhand. Evaporation is one of the major processes in the hydrologic cycle, and its accurate estimation is essential for hydrologic water-balance, irrigation, and water resources planning and management. correlation coefficient (r) Water resources of a country constitute one of its vital assets. The evaporation process represents a major component of the energy and water balance of bare soil and green cover (i.e. forest and farm) ecosystems. Estimation of the water loss by evaporation is of primary importance for monitoring, survey and management of water resources, design of irrigation and drainage systems and irrigation scheduling (Molina Martinez et al., 2005; Gundekar et al., 2008). It is generally made by some commonly used approaches such as mass transfer or water balance methods. One of the direct methods for Ep measurements is the pan evaporation (Eslamian et al., 2008). Pan performance is affected by instrumental limits and operational problems such as human errors, instrumentation errors, turbidity of water, watering of birds or other animals, as well as other maintenance problems which can affect the accuracy of Ep measurements. Accurate estimation of evaporation is essential for the balancing of irrigation water use in arid and semiarid regions, highly conditioned by water shortages, where responsible irrigation engineering and watershed management is mandatory. It is a key factor for irrigation system design and management, crop production, environmental assessment water resources management and planning. Estimation of evaporation for remote rural areas where no evaporation data are available is of great attention to the hydrologists and meteorologists (Kisi 2006). The most important factors affecting evapotranspiration are solar radiation, air temperature, air humidity and wind speed, vapour pressure deficit (Allen et al., 1998). Sudheer et al. (2002) showed that the neural computing technique could be employed successfully in modelling the evaporation process from the available climatic data set. Evaporation estimation by multilayer perceptron based artificial neural network and multiple linear regression techniques (Bhagwat et al., 2017). However, an analysis of the residuals from the ANN models developed revealed that the models showed significant error in predictions during the validation, implying loss of generalization of ANN models unless trained carefully. Recent experiment have reported that ANN may offer a promising alternative in the hydrological context developed feed forward ANN models for modelling daily evaporation and found that the ANN model performed better the conventional method. An attempt has been made to develop models for computation of evaporation by the available wheatear data of Hawalbagh. The objectives to develop the ANN based evaporation estimation models with different learning algorithms and to evaluate performance and adequacy of the developed models.
Estimating wheat and maize daily evapotranspiration using artificial neural network
Theoretical and Applied Climatology, 2018
In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RH max), minimum relative humidity (RH min), average relative humidity (RH ave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ET C) values calculated by ET C = ET 0 × K C equation (ET 0 from Penman-Monteith equation and K C from FAO-56, ANN C) were used as outputs, and for the other five structures, the ET C values measured by weighing lysimeter (ANN M) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN 1 C (8-4-2-1), with T max , T min , RH max , RH min , U 2 , n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R 2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN 5 C (5-2-3-1), with T max , T min , Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ET C used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.