scholarly journals Performance evaluation of artificial neural networks in estimating reference evapotranspiration with minimal meteorological data

2013 ◽  
Vol 13 (1) ◽  
pp. 18-27
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Learning Rule ◽  
Food And Agriculture ◽  
Air Temperatures ◽  
Food And Agriculture Organization ◽  
Cascade Correlation ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Detailed meteorological data required for the equation of FAO-56 Penman-Monteith (P-M) method that was adopted by Food and Agriculture Organization (FAO) as a standard method in estimating reference evapotranspiration (ETo) are not often available, especially in developing countries. The Hargreaves equation (HG) has been successfully used in some locations to estimate ETo where sufficient data were not available to use the P-M method. This paper investigates the potential of two Artificial Neural Network (ANN) architectures, the multilayer perceptron architecture, in which a backpropagation algorithm (BPANN) is used, and the cascade correlation architecture (CCANN), in which Kalman’s learning rule is embedded in modeling the daily ETo with minimal meteorological data. An overview of the features of ANNs and traditional methods such as P-M and HG is presented, and the advantages and limitations of each method are discussed. Daily meteorological data from three automatic weather stations located in Greece were used to optimize and test the different models. The exponent value of the HG equation was locally optimized, and an adjusted HGadj equation was used. The comparisons were based on error statistical techniques using P-M daily ETo values as reference. According to the results obtained, it was found that taking into account only the mean, maximum and minimum air temperatures, the selected ANN models markedly improved the daily ETo estimates and provided unbiased predictions and systematically better accuracy compared with the HGadj equation. The results also show that the CCANN model performed better than the BPANN model at all stations.

scholarly journals Comparison of meteorological data, related to reference evapotranspiration, from conventional and automatic stations in the Sertão and Agreste regions of Pernambuco, Brazil

DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 176-183
Author(s):  
Iug Lopes ◽  
Miguel Julio Machado Guimarães ◽  
Juliana Maria Medrado de Melo ◽  
Ceres Duarte Guedes Cabral de Almeida ◽  
Breno Lopes ◽  
...  
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Concordance Index ◽  
Good Relationship ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Scale ◽  
Weather Stations ◽  
Meteorological Elements

The objective was to perform a comparative study of the meteorological elements data that most cause changes in the reference Evapotranspiration (ETo, mm) and its own value, of automatic weather stations AWS and conventional weather stations CWS of the Sertão and Agreste regions of Pernambuco State. The ETo was calculated on a daily scale using the standard method proposed by the Food and Agriculture Organization (FAO), Penman-Monteith (FAO-56). The ETo information obtained from AWS data can be used to update the weather database of stations, since there is a good relationship between the ETo data obtained from CWS and AWS, statistically determined by the Willmott's concordance index (d > 0.7). The observed variations in the weather elements: air temperature, relative humidity, wind speed, and global solar radiation have not caused significant changes in the ETo calculation.

scholarly journals Modeling of hourly river water temperatures using artificial neural networks

2014 ◽  
Vol 49 (2) ◽  
pp. 144-162 ◽  
Author(s):  
Cindie Hebert ◽  
Daniel Caissie ◽  
Mysore G. Satish ◽  
Nassir El-Jabi
Keyword(s):  
Water Temperature ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Air Temperatures ◽  
Level Data ◽  
Water Level Data ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Many

Water temperature is an important component for water quality and biotic conditions in rivers. A good knowledge of river thermal regime is critical for the management of aquatic resources and environmental impact studies. The objective of the present study was to develop a water temperature model as a function of air temperatures, water temperatures and water level data using artificial neural network (ANN) techniques for two thermally different streams. This model was applied on an hourly basis. The results showed that ANN models are an effective modeling tool with overall root-mean-square-error of 0.94 and 1.23 °C, coefficient of determination (R2) of 0.967 and 0.962 and bias of −0.13 and 0.02 °C, for Catamaran Brook and the Little Southwest Miramichi River, respectively. The ANN model performed best in summer and autumn and showed a poorer performance in spring. Results of the present study showed similar or better results to those of deterministic and stochastic models. The present study shows that the predicted hourly water temperatures can also be used to estimate the mean and maximum daily water temperatures. The many advantages of ANN models are their simplicity, low data requirements, their capability of modeling long-term time series as well as having an overall good performance.

scholarly journals Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

2018 ◽  
pp. 75
Author(s):  
D. Montero ◽  
F. Echeverry ◽  
F. Hernández
Keyword(s):  
Land Surface Temperature ◽  
Land Surface ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Meteorological Station ◽  
Significant Relation ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Digital Elevation ◽  
Weather Stations

<p>The Food and Agriculture Organization of the United Nations (FAO) in its publication No. 56 of the Irrigation and Drainage Series presents the FAO Penman-Monteith procedure for the estimation of reference evapotranspiration from meteorological data, however, its calculation may be complicated in areas where there are no weather stations. This paper presents an evaluation of the potential of the Land Surface Temperature and Digital Elevation Models products derived from the MODIS and ASTER sensors, both on board the Terra EOS AM-1 satellite, for the estimation of reference evapotranspiration using the Penman-Monteith FAO-56, Hargreaves, Thornthwaite and Blaney-Criddle models. The four models were compared with the method proposed by FAO calculated with the observed data of a ground based meteorological station, finding a significant relation with the models Penman-Monteith FAO-56 and Hargreaves.</p>

scholarly journals Optimal Selection of Empirical Reference Evapotranspiration Method in 36 Different Agricultural Zones of China

Agronomy ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 31
Author(s):  
Yong Yang ◽  
Rensheng Chen ◽  
Chuntan Han ◽  
Zhangwen Liu ◽  
Xiqiang Wang
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Aridity Index ◽  
Empirical Method ◽  
Climatic Conditions ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Temperature Method ◽  
The Relationship ◽  
Selection Of

The Food and Agriculture Organization has proposed the current version of the Penman–Monteith method (FAO56-PM) as the standard for calculating reference evapotranspiration (ET0); however, high meteorological data requirements limit its application in many areas. There is thus an urgent need to identify the best alternative empirical method to accurately calculate ET0 in regions that lack sufficient meteorological data. In this study, three temperature-based methods and five radiation-based methods were evaluated using ET0 values generated using the FAO56-PM method in 36 agricultural zones in China based on meteorological data from 823 stations, measured between 2011 and 2020. The results showed that the optimal temperature-based method and radiation-based method differed for different agricultural zones, and no one temperature method or radiation method could be suitable for all agricultural zones. The eight empirical methods were regionally calibrated to improve the ET0 calculation accuracy in the different zones. The relationship between the optimal methods and climatic conditions showed that the most reliable empirical method could be selected according to the local annual mean temperature and aridity index. The results provide useful guidance for the selection of reliable empirical ET0 methods in agricultural zones outside China.

scholarly journals Modifying Hargreaves–Samani equation with meteorological variables for estimation of reference evapotranspiration in Turkey

2016 ◽  
Vol 48 (2) ◽  
pp. 480-497 ◽  
Author(s):  
Murat Cobaner ◽  
Hatice Citakoğlu ◽  
Tefaruk Haktanir ◽  
Ozgur Kisi
Keyword(s):  
Wind Speed ◽  
Reference Evapotranspiration ◽  
Meteorological Variables ◽  
Measured Temperature ◽  
Error Statistics ◽  
Explanatory Variables ◽  
Food And Agriculture ◽  
Air Temperatures ◽  
Food And Agriculture Organization ◽  
Geographical Regions

The Food and Agriculture Organization advocates the Penman–Monteith (FAO-56 PM) equation as the standard model for estimation of the reference evapotranspiration (ET0) because it is considered to have better accuracy. However, in regions where meteorological variables such as solar radiation, wind speed, and relative humidity are not gauged, the Hargreaves–Samani (HS) equation is resorted to as an alternative simply because it needs minimum and maximum air temperatures only as the explanatory variables. In this study, first the HS equation is applied to the monthly means of measured temperature data recorded at 275 meteorology stations in Turkey. Next, the coefficients of the HS equation are calibrated using the ET0 values given by the FAO-56 PM equation at all these stations. Next, the HS equation is modified by adding the wind speed as an extra explanatory variable, separately in each one of seven geographical regions of Turkey, which is observed to yield smaller error statistics as compared to the original HS equation. It is concluded that for estimation of the ET0 in regions where meteorological measurements are scarce, the HS equation modified in a similar manner can be used with better precision.

scholarly journals CALIBRATION OF THE HARGREAVES-SAMANI EQUATION FOR SPECIFIC PERIODS OF THE YEAR IN THE MUNICIPALITY OF JAÍBA-MG, BRAZIL

2017 ◽  
Vol 25 (5) ◽  
pp. 445-453
Author(s):  
Anunciene Barbosa Duarte ◽  
Lucas Borges Ferreira ◽  
Edson Fagne Dos Santos
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Climatic Conditions ◽  
Crop Water ◽  
Climatic Effects ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Crop Water Demand ◽  
Limited Application ◽  
Monteith Equation

Reference evapotranspiration (ET0) explains the climatic effects on crop water demand. The Food and Agriculture Organization (FAO) recommends the Penman Monteith equation as a standard method for estimating ET0. However, because this equation requires a large amount of meteorological data, it has limited application. An alternative is the Hargreaves-Samani (HS) equation, which only requires air temperature data, and can be calibrated to specifc locations and periods. The present study aimed to calibrate the empirical parameters (coeffcients and exponent) of the HS equation for specifc periods of the year, as well as evaluate the behavior and calibration of this equation throughout the year in the municipality of Jaíba-MG, Brazil. The daily meteorological data from 1996 to 2011 were gathered from a weather station located in the municipality of Jaíba-MG. A general calibration was performed per semester, per season, per month, and during periods with similar climatic conditions. The calibration of the HS equation, in all of the forms studied, promoted better ET0estimations. The calibrations for specifc periods of the year only promoted slight increases in performance in relation to the general calibration, therefore they, in general, presented equal performance to each other.

scholarly journals Prediction of Hourly Solar Radiation in Amman-Jordan by Using Artificial Neural Networks

2017 ◽  
Vol 14 (2) ◽  
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Narx Models ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive ◽  
Yearly Data

In this study, three Artificial Neural Network (ANN) models (Feedforward network, Elman, and Nonlinear Autoregressive Exogenous (NARX)) were used to predict hourly solar radiation in Amman, Jordan. The three models were constructed and tested by using MATLAB software. Meteorological data for the years from 2000 to 2010 were used to train the ANN while the yearly data of 2011 was used to test it. It was found that ANN technique may be used to estimate the hourly solar radiation with an excellent accuracy, and the coefficient of determination of Elman, feedforward and NARX models were found to be 0.97353, 0.97376, and 0.99017, respectively. The obtained results showed that NARX model has the best ability to predict the required solar data, while Elman and feedforward models have the lowest ability to predict it.

Empirical Equations to Estimate Evapotranspiration in Mosul City

2020 ◽  
Vol 27 (4) ◽  
pp. 98-102
Author(s):  
Haqqi Yasin ◽  
Luma Abdullah
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Data ◽  
Daily Sunshine ◽  
Measured Wind Speed

Average daily data of solar radiation, relative humidity, wind speed and air temperature from 1980 to 2008 are used to estimate the daily reference evapotranspiration in the Mosul City, North of Iraq. ETo calculator software with the Penman Monteith method standardized by the Food and Agriculture Organization is used for calculations. Further, a nonlinear regression approach using SPSS Statistics is utilized to drive the daily reference evapotranspiration relationships in which ETo is function to one or more of the average daily air temperature, actual daily sunshine duration, measured wind speed at 2m height and relative humidity

ARTIFICIAL NEURAL NETWORK (ANN)-BASED PREDICTIVE TOOL FOR ESTIMATING LIGHTNING DAMAGE IN COMPOSITES

2021 ◽  
Author(s):  
DEVIN NIELSEN ◽  
TYLER LOTT ◽  
SOM DUTTA ◽  
JUHYEONG LEE
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Training Dataset ◽  
Predictive Tool ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

In this study, three artificial neural network (ANN) models are developed with back propagation (BP) optimization algorithms to predict various lightning damage modes in carbon/epoxy laminates. The proposed ANN models use three input variables associated with lightning waveform parameters (i.e., the peak current amplitude, rising time, and decaying time) to predict fiber damage, matrix damage, and through-thickness damage in the composites. The data used for training and testing the networks was actual lightning damage data collected from peer-reviewed published literature. Various BP training algorithms and network architecture configurations (i.e., data splitting, the number of neurons in a hidden layer, and the number of hidden layers) have been tested to improve the performance of the neural networks. Among the various BP algorithms considered, the Bayesian regularization back propagation (BRBP) showed the overall best performance in lightning damage prediction. When using the BRBP algorithm, as expected, the greater the fraction of the collected data that is allocated to the training dataset, the better the network is trained. In addition, the optimal ANN architecture was found to have a single hidden layer with 20 neurons. The ANN models proposed in this work may prove useful in preliminary assessments of lightning damage and reduce the number of expensive experimental lightning tests.

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