scholarly journals Development of agro-climatic grape yield model for Nashik region, Maharashtra, India

2021 ◽  
Vol 22 (4) ◽  
pp. 494-500
Author(s):  
S.J. KADBHANE ◽  
V.L.MANEKAR
Keyword(s):  
Standard Deviation ◽  
Predictive Ability ◽  
System Model ◽  
Percentage Error ◽  
Climatic Parameters ◽  
Yield Model ◽  
Climate Conditions ◽  
Sensitivity Method ◽  
Discrepancy Ratio ◽  
Grape Yield

Prediction of the crop yield is need of time according to the change in climate conditions. In the present study, the Agro-Climatic Grape Yield (ACGY) model has been developed with monthly climatic parameters using multi-regression analysis approach. The developed model was statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the model was obtained as 1.03, 0.19, 0.03% and 0.19, respectively. Sensitivity analysis was carried out for the developed ACGY model using the parametric sensitivity method. In order to know the future grape yield using ACGY model, climate scenarios were generated under Canadian Earth System Model (CanESM2) for three emissions representative concentration pathways as RCP2.6, RCP4.5, and RCP8.5. According to the analysis using ACGY model, increasing yield was observed in grape up to year 2050 as compared to current yield.

scholarly journals Development of agro-climatic grape yield model with future prospective

2021 ◽  
pp. 89-103
Author(s):  
S.J. Kadbhane ◽  
V.L. Manekar
Keyword(s):  
Climate Change ◽  
Standard Deviation ◽  
Minimum Temperature ◽  
Predictive Ability ◽  
Percentage Error ◽  
Yield Model ◽  
Agriculture Sector ◽  
Sensitivity Method ◽  
Discrepancy Ratio ◽  
Grape Yield

Agriculture sector is most vulnerable to climate change. To predict the crop yield in accordance with the changing climate is a need of hour than choice. To know the climate in advance is crucial for grape growing farmers and grape export agencies for its better planning and security of grape industries from climate change perspective. In the present study, the Agro-Climatic Grape Yield (ACGY) model is developed on monthly scale climatic parameters using correlation, significance and multi-regression analysis approach. The developed model is statistically tested for its predictive ability. The discrepancy ratio, the standard deviation of discrepancy ratio, mean percentage error and standard deviation of mean percentage error for the developed model is obtained as 1.03, 0.19, 0.03% and 0.19 respectively. Sensitivity analysis is carried out for the developed ACGY model using the parametric sensitivity method. In order to know the grape yield for future using developed ACGY model, climate scenarios are generated under Canadian Earth System Model (CanESM2) for three emissions Representative Concentration Pathways (RCP) as RCP2.6, RCP4.5, and RCP8.5. Model response variability is carried out to understand the variation of grape yield. It is observed that grape yield is showing adverse variation with the increase in minimum temperature in January and November months, and precipitation in August and November months. Whereas, minimum temperature in April and sum of monthly mean evapotranspiration showing accordance effect on the grape yield. It is recommended the use of ACGY model for grape yield estimations applicable for the present and future climate of the study area based on the predictive capability of developed model.

scholarly journals Distance variable improvement of time-series big data stream evaluation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Ari Wibisono ◽  
Petrus Mursanto ◽  
Jihan Adibah ◽  
Wendy D. W. T. Bayu ◽  
May Iffah Rizki ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Standard Method ◽  
Time Series Data ◽  
Series Data ◽  
Percentage Error ◽  
Traffic Demand ◽  
Incremental Model ◽  
Chernoff Bound ◽  
The Mean

Abstract Real-time information mining of a big dataset consisting of time series data is a very challenging task. For this purpose, we propose using the mean distance and the standard deviation to enhance the accuracy of the existing fast incremental model tree with the drift detection (FIMT-DD) algorithm. The standard FIMT-DD algorithm uses the Hoeffding bound as its splitting criterion. We propose the further use of the mean distance and standard deviation, which are used to split a tree more accurately than the standard method. We verify our proposed method using the large Traffic Demand Dataset, which consists of 4,000,000 instances; Tennet’s big wind power plant dataset, which consists of 435,268 instances; and a road weather dataset, which consists of 30,000,000 instances. The results show that our proposed FIMT-DD algorithm improves the accuracy compared to the standard method and Chernoff bound approach. The measured errors demonstrate that our approach results in a lower Mean Absolute Percentage Error (MAPE) in every stage of learning by approximately 2.49% compared with the Chernoff Bound method and 19.65% compared with the standard method.

scholarly journals Agrometeorological models for estimating sweet cassava yield

2018 ◽  
Vol 48 (1) ◽  
pp. 43-51
Author(s):  
Victor Brunini Moreto ◽  
Lucas Eduardo de Oliveira Aparecido ◽  
Glauco de Souza Rolim ◽  
José Reinaldo da Silva Cabral de Moraes
Keyword(s):  
Great Influence ◽  
Soil Water Storage ◽  
Percentage Error ◽  
P Value ◽  
Climate Conditions ◽  
Paulo State ◽  
State Water ◽  
The Mean ◽  
Sweet Cassava ◽  
Main Factor

ABSTRACT Brazil is the fourth largest producer of cassava in the world, with climate conditions being the main factor regulating its production. This study aimed to develop agrometeorological models to estimate the sweet cassava yield for the São Paulo state, as well as to identify which climatic variables have more influence on yield. The models were built with multiple linear regression and classified by the following statistical indexes: lower mean absolute percentage error, higher adjusted determination coefficient and significance (p-value < 0.05). It was observed that the mean air temperature has a great influence on the sweet cassava yield during the whole cycle for all regions in the state. Water deficit and soil water storage were the most influential variables at the beginning and final stages. The models accuracy ranged in 3.11 %, 6.40 %, 6.77 % and 7.15 %, respectively for Registro, Mogi Mirim, Assis and Jaboticabal.

scholarly journals MODEL DINAMIS SISTEM KETERSEDIAAN DAGING SAPI NASIONAL

2011 ◽  
Vol 12 (1) ◽  
pp. 128 ◽  
Author(s):  
Harmini Harmini ◽  
Ratna Winandi Asmarantaka ◽  
Juniar Atmakusuma
Keyword(s):  
Computer Program ◽  
Dynamic System ◽  
Artificial Insemination ◽  
System Model ◽  
Percentage Error ◽  
National Program ◽  
Cross Breeding ◽  
Self Sufficiency ◽  
Innovative Solution ◽  
Dynamic System Model

The purpose of this paper is to assess whether the national program on beef self sufficiency could be achieved at 2014. A dynamic system model with Vensim computer program is applied. The model validated by Mean Absolute Percentage Error. The results shows high accuracies of the model. The assessment show that, first, the beef self sufficiency would not be achieved at 2014 if the program are treated and running as usual (Scenario I). Second, the beef self sufficiency would be achieved at 2015 if government increase the cow population by reducing the slaughter of local cows and expanding the cross breeding program through artificial insemination (Scenario II). Third, the beef self sufficiency would not be achieved at 2014 if the actual beef consumption are higher than the supply that produce through Scenario II (Scenario III). Another innovative solution for increasing local cow population is needed.

scholarly journals A QSAR Study Based on SVM for the Compound of Hydroxyl Benzoic Esters

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Li Wen ◽  
Qing Li ◽  
Wei Li ◽  
Qiao Cai ◽  
Yong-Ming Cai
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
Nonlinear Models ◽  
Predictive Ability ◽  
Quantitative Structure Activity Relationship ◽  
Support Vector ◽  
Qsar Study ◽  
Stability Robustness ◽  
Qsar Models ◽  
Leave One Out

Hydroxyl benzoic esters are preservative, being widely used in food, medicine, and cosmetics. To explore the relationship between the molecular structure and antibacterial activity of these compounds and predict the compounds with similar structures, Quantitative Structure-Activity Relationship (QSAR) models of 25 kinds of hydroxyl benzoic esters with the quantum chemical parameters and molecular connectivity indexes are built based on support vector machine (SVM) by using R language. The External Standard Deviation Error of Prediction (SDEPext), fitting correlation coefficient (R2), and leave-one-out cross-validation (Q2LOO) are used to value the reliability, stability, and predictive ability of models. The results show that R2 and Q2LOO of 4 kinds of nonlinear models are more than 0.6 and SDEPext is 0.213, 0.222, 0.189, and 0.218, respectively. Compared with the multiple linear regression (MLR) model (R2=0.421, RSD = 0.260), the correlation coefficient and the standard deviation are both better than MLR. The reliability, stability, robustness, and external predictive ability of models are good, particularly of the model of linear kernel function and eps-regression type. This model can predict the antimicrobial activity of the compounds with similar structure in the applicability domain.

POSSIBLE CHANGES OF CLIMATE CONDITIONS IN UKRAINE TO THE MIDDLE OF THE XXI CENTURY

2020 ◽  
pp. 94-100
Author(s):  
L.V. Malytska ◽  
V. O Balabukh
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Climate Model ◽  
Temporal Distribution ◽  
Climatic Parameters ◽  
Average Wind Speed ◽  
Climate Conditions ◽  
A1b Scenario

In Ukraine, as in the world, substantial climatic changes have happened throughout past decades. It is a fact that they are manifested in changing of parameters of the thermal regime, regimes of wind and humidity. It is expected that they will be observed also in future that will lead to aggravation of negative effects and risks due to climate change. That determines the relevance of the problem of forecasting such changes in future both globally and regionally. After all, knowledge of climate’s behavior in future is very important in the development of strategies, program and measures to adapt to climate change. The article is devoted to assessing spatio-temporal distribution main climatic indicators (air temperature, wind speed and relative humidity) in Ukraine, their variability and the probable values to the middle of the 21st century (2021-2050). Projection of changes in meteorological conditions was made for A1B scenario of SRES family using data of the regional climate model REMO and data from the hydrometeorological observation network of Ukraine (175 stations). Estimated data obtained from the European FP-6 ENSEMBLES project with a resolution of 25 km. For spatial distribution (mapping) we used open-source Geographic Information System QGIS, type of geographic coordinate system for project is WGS84. In the middle of the XXI century, if A1B scenario is released, it is expected a significant changes of climatic parameters regarding the 1981-2010 climatic norm: air temperature is rise by 1,5 °C, average wind speed is decrease by 5-8%, relative humidity in winter probably drop by 2%, but in summer it rises by 1,5%. The unidirectionality of the changes is characteristic only of air temperature, for wind speed and relative humidity the changes are in different directions. The intensity of changes is also not uniform across the country for all climatic parameters, has its regional and seasonal features. Statistical likelihood for most of highlighted changes for all climatic parameters is 66 % and more, the air temperature change is virtually certain (p-level <0.001).

Design Sensitivity Method for Sampling-Based RBDO With Varying Standard Deviation

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Hyunkyoo Cho ◽  
K. K. Choi ◽  
Ikjin Lee ◽  
David Lamb
Keyword(s):  
Standard Deviation ◽  
Design Variable ◽  
Score Function ◽  
Random Variable ◽  
Probability Of Failure ◽  
Design Sensitivity ◽  
Mean Value ◽  
Reliability Based Design ◽  
The Mean ◽  
Sensitivity Method

Conventional reliability-based design optimization (RBDO) uses the mean of input random variable as its design variable; and the standard deviation (STD) of the random variable is a fixed constant. However, the constant STD may not correctly represent certain RBDO problems well, especially when a specified tolerance of the input random variable is present as a percentage of the mean value. For this kind of design problem, the STD of the input random variable should vary as the corresponding design variable changes. In this paper, a method to calculate the design sensitivity of the probability of failure for RBDO with varying STD is developed. For sampling-based RBDO, which uses Monte Carlo simulation (MCS) for reliability analysis, the design sensitivity of the probability of failure is derived using a first-order score function. The score function contains the effect of the change in the STD in addition to the change in the mean. As copulas are used for the design sensitivity, correlated input random variables also can be used for RBDO with varying STD. Moreover, the design sensitivity can be calculated efficiently during the evaluation of the probability of failure. Using a mathematical example, the accuracy and efficiency of the developed design sensitivity method are verified. The RBDO result for mathematical and physical problems indicates that the developed method provides accurate design sensitivity in the optimization process.

scholarly journals Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Solar Radiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Climate Conditions ◽  
The Neural Networks ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Radiation Conditions

This paper presents an assessment for the artificial neural network (ANN) based approach for hourly solar radiation prediction. The Four ANNs topologies were used including a generalized (GRNN), a feed-forward backpropagation (FFNN), a cascade-forward backpropagation (CFNN), and an Elman backpropagation (ELMNN). The three statistical values used to evaluate the efficacy of the neural networks were mean absolute percentage error (MAPE), mean bias error (MBE) and root mean square error (RMSE). Prediction results show that the GRNN exceeds the other proposed methods. The average values of the MAPE, MBE and RMSE using GRNN were 4.9%, 0.29% and 5.75%, respectively. FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. The average values of the MAPE, MBE and RMSE using the FFNN were 23%, −.09% and 21.9%, respectively, while the average values of the MAPE, MBE and RMSE using CFNN were 22.5%, −19.15% and 21.9%, respectively. ELMNN fared the worst among the proposed methods in predicting hourly solar radiation with average MABE, MBE and RMSE values of 34.5%, −11.1% and 34.35%. The use of the GRNN to predict solar radiation in all climate conditions yielded results that were highly accurate and efficient.

scholarly journals SPECTROPHOTOMETRIC DETERMINATION OF CEFPIROME IN PHARMACEUTICAL PREPARATION

2020 ◽  
pp. 124-127
Author(s):  
DILIP M CHAFLE
Keyword(s):  
Standard Deviation ◽  
Spectrophotometric Method ◽  
Limit Of Detection ◽  
Pharmaceutical Preparation ◽  
Effect Of Temperature ◽  
Percentage Error ◽  
Ferric Nitrate ◽  
Red Color ◽  
Relative Standard

Objective: A simple, sensitive and precise visible spectrophotometric method has been proposed for the determination of cefpirome (CFM) in pure and oral injectable dosage form. Methods: A spectrophotometric method is based on the formation of stable red color product by oxidation of drugs by ferric nitrate and subsequent complexation with 1, 10 – phenanthroline with maximum absorption at 515 nm. Result: The red color complex was formed between Fe (II) and 1, 10 – phenanthroline after reduction of Fe (III) to Fe (II) in the presence of CFM drug. The phosphoric acid solution was used only for quenching the complex formation reaction. Several parameters such as the maximum wavelength of absorption, the volume of reagents, sequence of addition and effect of temperature and time of heating were optimized to achieve high sensitivity, stability and reproducible results. Under the optimum conditions, linear relationship with good correlation coefficient (0.994) was found over the concentration range from 0.20 to 6.00 μg/mL with a molar extinction coefficient 7.7813 × 104 L/mol/cm, limit of detection 0.2026 and limit of quantification 0.6141 μg/mL, respectively. Conclusion: The proposed method was evaluated statistically for linearity, accuracy, and precision in terms of standard deviation, percentage recovery, percentage error and relative standard deviation. The proposed method can be applied for the routine estimation of CFM in the laboratory.

scholarly journals Comparative study between Johnson’s formula and Dare’s formula of fetal weight estimation at term

2021 ◽  
Vol 10 (9) ◽  
pp. 3418
Author(s):  
Anitha C. ◽  
Deepa V. Kanagal
Keyword(s):  
Standard Deviation ◽  
Active Phase ◽  
Fetal Weight ◽  
Percentage Error ◽  
Weight Estimation ◽  
Actual Weight ◽  
Weight Percentage ◽  
Fetal Weight Estimation ◽  
Clinical Methods ◽  
Pregnancy And Delivery

Background: Prediction of fetal weight is one of the methods towards effective management of pregnancy and delivery. To assess and compare the accuracy of clinical and sonographic fetal weight estimation in predicting birth weight at term pregnancy, patients who were in latent or in active phase of labour. In the present study, an effort is made to compare two different clinical methods and USG and relate to the actual weight of the baby at birth.Methods: It is a prospective observational study of one hundred pregnant women satisfying the criteria, consenting for the study was recruited. Both USG and clinical methods will be done and compared with estimated the fetal weight. Weight of the baby at birth will be measured.Results: All the three methods had significant relationship with the baby weight. Percentage error was least with USG and the standard deviation of error was lower with Dare’s formula. The standard deviation was minimal for Dare`s formula EFW followed closely by USG.Conclusions: It can be concluded that Dare’s formula of clinical methods can be a potential option to be promoted in predicting the fetal weight in the absence of USG facilities. Training in this method is very important and can be an integral part in managing pregnancy during delivery in primary care setting.

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