scholarly journals Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate

2009 ◽  
Vol 41 (1) ◽  
pp. 38-49 ◽  
Author(s):  
Slavisa Trajkovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Management ◽  
Weather Data ◽  
Canopy Resistance ◽  
Rbf Network ◽  
Artificial Neural ◽  
Two Parameters

Numerous approaches have been developed for estimating hourly reference evapotranspiration ET0, most of which require numerous meteorological data. In many areas, the necessary data are lacking and new techniques are required. The objectives of this study are: (1) to develop artificial neural networks for estimating hourly reference evapotranspiration from limited weather data; (2) to evaluate the reliability of obtained artificial neural networks (ANNs) and Food and Agricultural Organization—56 Penman Monteith (FAO-56 PM) equation compared to the lysimeter measurements; (3) to test the performance of the FAO-56 PM equation for hourly daytime periods using rc=70 s m−1 (PM70) and using a lower rc=50 s m−1 (PM50); and (4) to evaluate the reliability of obtained ANNs compared to the FAO-56 PM equation using an hourly dataset from a variety of locations. The accuracy of two reduced-set artificial neural networks (ANNTR and ANNTHR) and two FAO-56 Penman-Monteith equations with different canopy resistance values (PM50 and PM70) was assessed using hourly lysimeter data from Davis, California. The ANNTR required only two parameters (temperature and radiation) as inputs. Temperature, humidity and (Rn−G) term were used as inputs in the ANNTHR. The ANNTR and PM50 were best at estimating hourly grass ET0. The ANNTR approach was additionally tested using hourly FAO-56 PM ET0 data from California Irrigation Management Information System (CIMIS) dataset. The overall results recommended Radial Basis Function (RBF) network for estimating hourly ET0 from limited weather data. Also, the results support the introduction of new value for canopy resistance (rc=50 s m−1) in the hourly FAO-56 PM equation.

scholarly journals Estimating hourly reference evapotranspiration from limited weather data by sequentially adaptive RBF network

2011 ◽  
Vol 9 (3) ◽  
pp. 473-480 ◽  
Author(s):  
Slavisa Trajkovic
Keyword(s):  
Heat Flux ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Soil Heat Flux ◽  
Weather Data ◽  
Canopy Resistance ◽  
Net Radiation ◽  
Rbf Networks ◽  
Rbf Network ◽  
Two Parameters

This study investigates the utility of adaptive Radial Basis Function (RBF) networks for estimating hourly grass reference evapotranspiration (ET0) from limited weather data. Nineteen days of micrometeorological and lysimeter data collected at half-hour intervals during 1962-63 and 1966-67 in the Campbell Tract research site in Davis, California were used in this study. Ten randomly chosen days (234 patterns) were selected for the RBF networks training. Two sequentially adaptive RBF networks with different number of inputs (ANNTR and ANNTHR) and two Penman-Monteith equations with different canopy resistance values (PM42 and PM70) were tested against hourly lysimeter data from remaining nine days (200 patterns). The ANNTR requires only two parameters (air temperature and net radiation) as inputs. Air temperature, humidity, net radiation and soil heat flux were used as inputs in the ANNTHR. PM equations use air temperature, humidity, wind speed, net radiation and soil heat flux density as inputs. The results reveal that ANNTR and PM42 were generally the best in estimating hourly ET0. The ANNTHR performed less well, but the results were acceptable for estimating ET0. These results are of significant practical use because the RBF network with air temperature and net radiation as inputs could be used to estimate hourly ET0 at Davis, California.

scholarly journals Estimating Potential Evapotranspiration in Maranhão State Using Artificial Neural Networks

2020 ◽  
Vol 35 (4) ◽  
pp. 675-682
Author(s):  
Klara Cunha de Meneses ◽  
Lucas Eduardo De Oliveira Aparecido ◽  
Kamila Cunha de Meneses ◽  
Maryzélia Furtado de Farias
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Standard Method ◽  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Irrigation Management ◽  
Northeastern Brazil ◽  
Artificial Neural

Abstract The use of technology and planning in agricultural production is essential in Northeastern Brazil, which is the region of the country that most suffers from water shortage. For the best irrigation management, it is necessary to know the potential evapotranspiration rate for water control in order to increase productivity. There are several direct and indirect methods for estimating evapotranspiration, but the standard method recommended by the United Nations Agriculture Organization (FAO) is the Penman-Monteith (PETpm) method because it has higher accuracy than other methods. However, it is a difficult method to be used due to the need for a large number of meteorological elements. In this context, the objective of this study was to estimate potential evapotranspiration by the Penman-Monteith method in the micro-region of Baixo Parnaíba in Maranhão state using artificial neural networks. Agro-meteorological data were collected daily over 34 years, from 1984 to 2017, and these data were obtained from the NASA/POWER website. Subsequently, liquid radiation and potential evapotranspiration were calculated by the Penman-Monteith standard method (1998). To predict potential daily evapotranspiration, the Multi-Layer Perceptron (MLP) was chosen, which is a traditional Artificial Neural Network. The period that presented a higher evapotranspiration index was the same one that showed precipitation with a lower volume and higher temperatures. The artificial neural network model that best adapted to estimate PETpm was MLP 2-5-1. It is concluded that artificial neural networks estimate with accuracy and precision the Penman-Monteith daily potential evapotranspiration of the Lower Parnaiba in Maranhão, and potential evapotranspiration can be estimated by the Penman-Monteith method using neural networks with inputs of air temperatures.

scholarly journals Meteorological Data Analysis using Artificial Neural Networks

2019 ◽  
Vol 9 (2S) ◽  
pp. 274-276
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Data Analysis ◽  
Meteorological Data ◽  
Confusion Matrix ◽  
Heavy Rain ◽  
Optimal Number ◽  
Weather Data ◽  
Urban Region ◽  
Artificial Neural

This paper focuses on weather data analysis for Bangalore urban region(Karnataka,India) over a span of 30 years. The 30 years data is preprocessed to have average monthly temperature, vapor pressure, PET (Potential-Evapo Transpiration), cloud cover, rainfall. These features are considered as factors affecting the rainfall. The correlation between the above mentioned parameters with the monthly rainfall are found using spearman correlation. Artificial Neural Networks (ANN) is used to classify instances as less rain, medium and heavy rain. The results of accuracy, confusion matrix is tabulated. Also the optimal number epochs, number of neurons and number of hidden layers is also identified for the data. The graph of actual output and predicted output is plotted.

Ozone Concentration Prediction using Artificial Neural Networks

2017 ◽  
Vol 68 (10) ◽  
pp. 2224-2227 ◽  
Author(s):  
Camelia Gavrila
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Ozone Concentration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Time Data ◽  
Marquardt Algorithm ◽  
Sensitivity Tests ◽  
Artificial Neural ◽  
The Relationship

The aim of this paper is to determine a mathematical model which establishes the relationship between ozone levels together with other meteorological data and air quality. The model is valid for any season and for any area and is based on real-time data measured in Bucharest and its surroundings. This study is based on research using artificial neural networks to model nonlinear relationships between the concentration of immission of ozone and the meteorological factors: relative humidity (RH), global solar radiation (SR), air temperature (TEMP). The ozone concentration depends on following primary pollutants: nitrogen oxides (NO, NO2), carbon monoxide (CO). To achieve this, the Levenberg-Marquardt algorithm was implemented in Scilab, a numerical computation software. Performed sensitivity tests proved the robustness of the model and its applicability in predicting the ozone on short-term.

scholarly journals Dewpoint Temperature Prediction Using Artificial Neural Networks

2008 ◽  
Vol 47 (6) ◽  
pp. 1757-1769 ◽  
Author(s):  
D. B. Shank ◽  
G. Hoogenboom ◽  
R. W. McClendon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Air Temperature ◽  
Heat Waves ◽  
Weather Data ◽  
Temperature Prediction ◽  
Related Data ◽  
Dewpoint Temperature ◽  
Artificial Neural ◽  
Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

scholarly journals Artificial Neural Networks to Predict the Power Output of a PV Panel

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Valerio Lo Brano ◽  
Giuseppina Ciulla ◽  
Mariavittoria Di Falco
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Monitoring System ◽  
Power Output ◽  
Department Of Energy ◽  
Electricity Production ◽  
Energy Output ◽  
Weather Data ◽  
Output Data ◽  
Artificial Neural

The paper illustrates an adaptive approach based on different topologies of artificial neural networks (ANNs) for the power energy output forecasting of photovoltaic (PV) modules. The analysis of the PV module’s power output needed detailed local climate data, which was collected by a dedicated weather monitoring system. The Department of Energy, Information Engineering, and Mathematical Models of the University of Palermo (Italy) has built up a weather monitoring system that worked together with a data acquisition system. The power output forecast is obtained using three different types of ANNs: a one hidden layer Multilayer perceptron (MLP), a recursive neural network (RNN), and a gamma memory (GM) trained with the back propagation. In order to investigate the influence of climate variability on the electricity production, the ANNs were trained using weather data (air temperature, solar irradiance, and wind speed) along with historical power output data available for the two test modules. The model validation was performed by comparing model predictions with power output data that were not used for the network's training. The results obtained bear out the suitability of the adopted methodology for the short-term power output forecasting problem and identified the best topology.

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