scholarly journals EVALUATION OF THE ACCURACY OF EVPOTRANSPIRATION IN THE EVAPO MOBILE APPLICATION

2021 ◽  
Author(s):  
R. Vozhehova ◽  
◽  
P. Lykhovyd
Keyword(s):  
Graphical Models ◽  
Mobile Application ◽  
Polynomial Regression ◽  
Meteorological Data ◽  
Warm Period ◽  
Cold Period ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Irrigation Planning

Abstract The article presents the results of the study on the accuracy of evapotranspiration in the EVAPO mobile application. The aim of the work is to provide recommendations on the effective use of the mobile application for the prompt, low-cost and convenient determination of evapotranspiration and planning the irrigation regime. Materials and methods. The study was conducted in the autumn of 2020 and in the summer of 2021 using meteorological data from Kherson Regional Hydrometeorological Station, which were used for reference calculations of evapotranspiration according to the method recommended by FAO (Penman-Monteith equation) in the ETo Calculator software. The calculated values of the reference evapotranspiration and those obtained in the EVAPO mobile application were compared with each other through the computation of the correlation coefficients, determination coefficients and mean absolute percentage errors to assess the accuracy of the data on the studied agrometeorological index in the mobile application. Statistical calculations and graphical models were performed using Microsoft Excel 365 spreadsheet processor. Polynomial regression was applied to calibrate and enhance the performance of original EVAPO application. Results. It was found that the EVAPO mobile application without additional calibration cannot provide the proper accuracy of the evapotranspiration calculation. During the cold period of the year (October-November) the mean absolute percentage error was 137.02 %, and during the warm period (May-August) it was 41.43 %. The general error of the calculation in the mobile application compared to the ETo Calculator reference values was 88.75 %. At the same time, EVAPO makes it possible to accurately track the trend of evapotranspiration dynamics, the coefficient of determination of the model is 0.86. In the warm period of the year, there is a tendency to overestimate the value of evapotranspiration, and in the cold period of the year, no clear pattern was found. The evapotranspiration values adjusted by the polynomial regression model obtained in the EVAPO mobile application allow their use in operational irrigation planning. Conclusions. The EVAPO mobile application is a convenient, accessible tool for the rapid assessment of evapotranspiration. However, its implementation on the territory of Ukraine cannot be recommended without preliminary calibration for each specific agroclimatic zone due to enormous errors in the estimation of evapotranspiration value.

Evaluation of Global Solar Radiation using Sunshine Hour, Temperature and Relative Humidity at Low Land Region of Nepal

2020 ◽  
Vol 6 (1) ◽  
pp. 16-24
Author(s):  
U. Joshi ◽  
K.N. Poudyal ◽  
I.B. Karki ◽  
N.P. Chapagain
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Energy Potential ◽  
To Come ◽  
Empirical Constants

The accurate knowledge of solar energy potential is essential for agricultural scientists, energy engineers, architects and hydrologists for relevant applications in concerned fields. It is cleanest and freely available renewable energy measured using CMP6 Pyranometer. However, it is quite challenging to acquire accurate solar radiation data in different locations of Nepal because of the high cost of instruments and maintenances. In these circumstances, it is essential to select an appropriate empirical model to predict global solar radiation for the use of future at low land, Nepalgunj (28.102°N, 81.668°E and alt. 165 masl) for the year 2011-2012. In this paper, six different empirical models have been used based on regression technique, provided the meteorological data. The empirical constants (a = 0.61, b = 0.05, c = -0.0012 and d = -0.017) are obtained to predict Global solar radiation. The values of statistical tools such as mean percentage error, mean bias error, root mean square error, and coefficient of determination obtained for Abdalla model are 1.99%, 0.003 MJ/m2/day, 2.04 MJ/m2/day and 0.74 respectively. Using the error analysis, it is concluded that the Abdalla model is better than others. So the empirical constants of this model are utilized to predict the global solar radiation to the similar geographical sites of Nepal for the years to come and it can be used to estimate the missing data of solar radiation for the respective sites.

scholarly journals Empirical models for predicting global solar radiation using meteorological parameters for Sokoto, Nigeria

2019 ◽  
Vol 7 (2) ◽  
pp. 48
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana
Keyword(s):  
Solar Radiation ◽  
Meteorological Parameters ◽  
Meteorological Data ◽  
Climatic Variability ◽  
Global Solar Radiation ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Radiation Maximum

The performances of sunshine, temperature and multivariate models for the estimation of global solar radiation for Sokoto (Latitude 13.020N, Longitude 05.250E and 350.8 m asl) located in the Sahelian region in Nigeria were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperatures, sunshine hours, rainfall, wind speed, cloud cover and relative humidity meteorological data during the period of thirty one years (1980-2010). The comparison assessment of the models was carried out using statistical indices of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA). For the sunshine based models, a total of ten (10) models were developed, nine (9) existing and one author’s sunshine based model. For the temperature based models, a total of four (4) models were developed, three (3) existing and one author’s temperature based model. The results of the existing and newly developed author’s sunshine and temperature based models were compared and the best empirical model was identified and recommended. The results indicated that the author’s quadratic sunshine based model involving the latitude and the exponent temperature based models are found more suitable for global solar radiation estimation in Sokoto. The evaluated existing Ångström type sunshine based model for the location was compared with those available in literature from other studies and was found more suitable for estimating global solar radiation. Comparing the most suitable sunshine and temperature based models revealed that the temperature based models is more appropriate in the location. The developed multivariate regression models are found suitable as evaluation depends on the available combination of the meteorological parameters based on two to six variable correlations. The recommended models are found suitable for estimating global solar radiation in Sokoto and regions with similar climatic information with higher accuracy and climatic variability.   

Development of Prediction Model for Harvest Moisture Content of Rough Rice Using Meteorological Data and Color Characteristics

2021 ◽  
Vol 37 (6) ◽  
pp. 1063-1071
Author(s):  
Hoon Kim ◽  
Oui Woung Kim ◽  
Jae Woong Han ◽  
Hyo-Jai Lee
Keyword(s):  
Moisture Content ◽  
Prediction Model ◽  
Meteorological Data ◽  
Coefficient Of Determination ◽  
Harvest Time ◽  
Leaf Color ◽  
Rough Rice ◽  
Accumulated Temperature ◽  
Harvest Moisture

HighlightsMoisture content, meteorological data, and leaf color characteristics of rice were investigated by harvest time.The moisture content decreased, and leaf color value increased as days after heading passed.Harvest moisture content prediction models were developed using meteorological data and leaf color.It is necessary to use both leaf color and meteorological data to determine the harvest time.Abstract.In this study, ambient temperature, accumulated temperature, and rice leaf color values were measured before harvest time to develop models for predicting the harvest moisture content (HMC) of short-grain rice. Field tests were conducted on Chuchung and Whang-gum-nu-ri, which are short-grain rice cultivars, at different experimental plots, for four years. As days after heading (DAH) passed, the moisture content (MC) decreased, and leaf color (L*, a*, and b* values) tended to increase. An experimental model that can predict HMC was developed based on the experimental results of 3 years, and the experimental results of the remaining 1 year were used for verification. The coefficient of determination of the HMC prediction model that used ambient and accumulated temperatures was 0.719, and that of the prediction model that used leaf color was as low as 0.418. However, the coefficient of determination of the integrated model that used all the factors, i.e. ambient and accumulated temperatures and leaf color, was as high as 0.915. Therefore, to determine the harvest time using the HMC of rough rice, leaf color, and meteorological data should be used together. Leaf color tended to increase markedly as the DAH increased, but the leaf color values were not similar for the same MC each year. This is because leaf color is influenced not only by MC but also by various cultivation factors such as soil conditions and growth rate during the rice cultivation process. Keywords: Accumulated temperature, Harvest, Harvest moisture content, Leaf color, Rice, Short variety.

scholarly journals Deep Neural Network for Ore Production and Crusher Utilization Prediction of Truck Haulage System in Underground Mine

2019 ◽  
Vol 9 (19) ◽  
pp. 4180 ◽  
Author(s):  
Jieun Baek ◽  
Yosoon Choi
Keyword(s):  
Neural Network ◽  
Test Data ◽  
Deep Neural Network ◽  
Underground Mine ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Hidden Layer ◽  
System Characteristics ◽  
Haulage System

A new method using a deep neural network (DNN) model is proposed to predict the ore production and crusher utilization of a truck haulage system in an underground mine. An underground limestone mine was selected as the study area, and the DNN model input/output nodes were designed to reflect the truck haulage system characteristics. Big data collected on-site for 1 month were processed to create learning datasets. To select the optimal DNN learning model, the numbers of hidden layers and hidden layer nodes were set to various values for analyzing the training and test data. The optimal DNN model structure for ore production prediction was set to five hidden layers and 40 hidden layer nodes. The test data exhibited a coefficient of determination of 0.99 and mean absolute percentage error (MAPE) of 2.80%. The optimal configuration for the crusher utilization prediction was set to four hidden layers and 40 hidden layer nodes, and the test data exhibited a coefficient of determination of 0.99 and MAPE of 2.49%. The trained DNN model was used to predict the ore production and crusher utilization, which were similar to the actual observed values.

scholarly journals New temperature dependent models for estimating global solar radiation across the midland climatic zone of Nigeria

2019 ◽  
Vol 7 (2) ◽  
pp. 70
Author(s):  
Davidson O. Akpootu ◽  
Bello I. Tijjani ◽  
Usman M. Gana
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Climatic Zone ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Temperature Dependent ◽  
Statistical Validation

Authentic information of the availability of global solar radiation is significant to agro/hydro meteorologists, atmospheric Physicists and solar energy engineers for the purpose of local and international marketing, designs and manufacturing of solar equipment. In this study, five new proposed temperature dependent models were evaluated using measured monthly average daily global solar radiation, maximum and minimum temperature meteorological data during the period of thirty one years (1980-2010). The new models were compared with three existing temperature dependent models (Chen et al., Hargreaves and Samani and Garcia) using seven different statistical validation indicators of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to ascertain the suitability of global solar radiation estimation in five different locations (Zaria, Bauchi, Jos, Minna and Yola) situated in the Midland climatic zone of Nigeria. In each location, the result shows that a new empirical regression model was found more accurate when compared to the existing models and are therefore recommended for estimating global solar radiation in the location and regions with similar climatic information where only temperature data are available. The evaluated existing Hargreaves and Samani and Garcia temperature based models for Jos were compared to those available in literature and was found more suitable for estimating global solar radiation for the location. The comparison between the measured and estimated temperature dependent models depicts slight overestimation and underestimation in some months with good fitting in the studied locations. However, the recommended models give the best fitting.   

scholarly journals PENGEMBANGAN MODEL REGRESI POLINOMIAL BERGANDA PADA KASUS DATA PEMASARAN

2013 ◽  
Vol 12 (2) ◽  
pp. 149 ◽  
Author(s):  
Julyanti S Malensang ◽  
Hanny Komalig ◽  
Djoni Hatidja
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Polynomial Regression ◽  
Predictor Variable ◽  
Coefficient Of Determination ◽  
Linear Regression Models ◽  
A Value ◽  
Marketing Data ◽  
Set Up

PENGEMBANGAN MODEL REGRESI POLINOMIAL BERGANDA PADA KASUS DATA PEMASARANABSTRAK Regresi polinomial merupakan regresi linier berganda yang dibentuk dengan menjumlahkan pengaruh variabel prediktor (X) yang dipangkatkan secara meningkat sampai orde ke-k. Model regresi polinomial, struktur analisisnya sama dengan model regresi linier berganda. Artinya, setiap pangkat atau orde variabel prediktor (X) pada model polinomial, merupakan transformasi variabel awal dan dipandang sebagai sebuah variabel prediktor (X) baru dalam linier berganda. Model terbaik dari kelima model yang telah diuji adalah persamaan regresi model ke-5. Hal ini dapat dilihat dari nilai koefisien determinasi sebesar 99,1% dan nilai R-Sq(adj) = 98,8%, karena nilai R2 mendekati nilai yang telah diatur dan berdasarkan pengujian yang dilakukan ternyata seluruh koefisien-koefisien dari setiap variabel bebas signifikan serta ada kelengkungan yang bersifat kubik (pangkat 3) terhadap data X3 terhadap Y. Kata kunci: Pemasaran, Regresi polynomial. DEVELOPMENT OF MULTIPOLYNOMIAL REGRESSION MODEL ON MARKETING DATA CASE ABSTRACT Polynomial regression is linear regression multiple were created by summing the effect of each predictor variable (X) is raised to increase to the order of the k.  Polynomial regression model, has the same structure with linear regression models. That is, any rank or order predictor variable (X) in polynomial models, an initial variable transformation and is seen as a predictor variable (X) has the linear regression. The best model of the six models tested were equation regression model to-5.  It can be seen from the value of the coefficient of determination of 99.1% and a value of R-Sq (adj) = 98.8%, due to the value of R2 close to the value that has been set up and based on tests performed turns all the coefficients of each independent variable significantly and there are cubic curvature (rank 3) to the data X3 to Y. Keywords : Marketing, Polynomial regression.

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.

scholarly journals A Wavelet Based Solar Radiation Prediction in Nigeria Using Adaptive Neuro-Fuzzy Approach

2018 ◽  
Vol 12 (3) ◽  
pp. 907 ◽  
Author(s):  
Sani Salisu ◽  
Mohd Wazir Mustafa ◽  
Mamunu Mustapha
Keyword(s):  
Solar Radiation ◽  
Meteorological Data ◽  
Hybrid Approach ◽  
Model Performance ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy

<p><span>In this study, a hybrid approach combining an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Wavelet Transform (WT) is examined for solar radiation prediction in Nigeria. Meteorological data obtained from NIMET Nigeria comprising of </span><span lang="EN-MY">monthly mean minimum temperature, maximum temperature, relative humidity and sunshine hours were used as inputs to the model and monthly mean solar radiation was used as the model output. The data used was divided into two for training and testing, with 70% used during the training phase and 30% during the testing phase. The hybrid model performance is assessed using three statistical evaluators, Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of determination </span><span lang="EN-SG">(R<sup>2</sup>). According to the results obtained, a very accurate prediction was achieved by the WT- ANFIS model by improving the value of (R<sup>2</sup>) by at least 14% and RMSE by at least 78% when compared with other existing models. And a MAPE of 2% is recorded using the proposed approach. The obtained results prove the developed WT-ANFIS model as an efficient tool for solar radiation prediction.</span></p>

EINE CHEMISCHE METHODE ZUR BESTIMMUNG VON 16-EPI-ÖSTRIOL IM URIN DES MENSCHEN

1963 ◽  
Vol 44 (1) ◽  
pp. 47-66 ◽  
Author(s):  
W. Nocke ◽  
H. Breuer
Keyword(s):  
Melting Point ◽  
Phosphoric Acid ◽  
Aqueous Alkali ◽  
Percentage Error ◽  
Organic Layer ◽  
Maximum Percentage ◽  
Chemical Determination ◽  
Routine Assay ◽  
Hydrolysis Of

ABSTRACT A method for the chemical determination of 16-epi-oestriol in the urine of nonpregnant women with a qualitative sensitivity of less than 0.5 μg/24 h is described. The separation of 16-epi-oestriol and oestriol is accomplished by converting 16-epi-oestriol into its acetonide, a reaction which is stereoselective for cis-glycols and therefore not undergone by oestriol as a trans-glycol. Following partition between chloroform and aqueous alkali, the acetonide of 16-epi-oestriol is completely separated with the organic layer whereas oestriol as a strong phenol remains in the alkaline phase. 16-epi-oestriol is chromatographed on alumina as the acetonide and determined as a Kober chromogen. This procedure can easily be incorporated into the method of Brown et al. (1957 b) thus making possible the simultaneous routine assay of oestradiol-17β, oestrone, oestriol and 16-epi-oestriol from one sample of urine. The specificity of the method was established by separation of 16-epi-oestriol from nonpregnancy urine as the acetonide, hydrolysis of the acetonide by phosphoric acid, isolation of the free compound by microsublimation and identification by micro melting point, colour reactions and chromatography. The accuracy of the method is given by a mean recovery of 64% for pure crystalline 16-epi-oestriol when added to hydrolysed urine in 5–10 μg amounts. The precision is given by s = 0.24 μg/24 h. For the duplicate determination of 16-epi-oestriol the qualitative sensitivity is 0.44 μg/24 h, the maximum percentage error being ± 100% The quantitative sensitivity (±25% error) is 1.7 μg/24 h.

Development and evaluation of trans-Amadi groundwater parameters: The integration of finite element techniques

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Finite Element ◽  
Polynomial Regression ◽  
Nitrate Concentration ◽  
Strong Relationship ◽  
Total Iron ◽  
Coefficient Of Determination ◽  
Port Harcourt ◽  
Polynomial Expression ◽  
The Relationship ◽  
Best Fit

Mathematical model was developed and evaluated to monitor and predict the groundwater characteristics of Trans-amadi region in Port Harcourt City. In this research three major components were considered such as chloride, total iron and nitrate concentration as well as the polynomial expression on the behavious on the concentration of each component was determined in terms of the equation of the best fit as well as the square root of the curve. The relationship between nitrate and distance traveled by Nitrate concentration by the model is given as Pc = 0.003x2 - 0.451x + 14.91with coefficient of determination, R² = 0.947, Chloride given as Pc = 0.000x2 - 0.071x + 2.343, R² = 0.951while that of Total Iron is given as Pc = 2E-05x2 - 0.003x + 0.110, R² = 0.930. All these show a strong relationship as established by Polynomial Regression Model. The finite element techniques are found useful in monitoring, predicting and simulating groundwater characteristics of Trans-amadi as well as the prediction on the variation on the parameters of groundwater with variation in time.

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