A Comparative Study of Simple Regression Models to Estimate Fibre Length Growth in Chios Sheep from Common Meteorological Variables

2020 ◽  
Vol 8 (3) ◽  
pp. 187-192
Author(s):  
Aristidis Matsoukis ◽  
Aikaterini Chronopoulou-Sereli ◽  
George Stratakos
Keyword(s):  
Air Temperature ◽  
Regression Models ◽  
Fibre Length ◽  
Meteorological Variables ◽  
Visual Examination ◽  
Length Growth ◽  
Statistical Measures ◽  
Improvement Programs ◽  
Chios Sheep ◽  
Simple Regression

Chios sheep is a promising sheep breed, with wool, one of its products, to be of special interest to genetic improvement programs. Recently, it has been reported significant linear correlation between the fibre length growth (FLG) of Chios sheep, an important component of its wool quality, and each of the meteorological variables air temperature (T) and sunshine (SUNS), but nothing is known about the prediction of FLG from T and SUNS. Thus, this work aims to investigate the effectiveness of five simple regression models (linear, quadratic, cubic, logarithmic and inverse), concerning the aforementioned prediction, using visual examination and two widely accepted statistical measures, the adjusted coefficient of determination (R2adj) and the root mean square error (RMSE). Results showed that the applied nonlinear regression models were characterized by higher R2adj and lower RMSE in comparison to the linear one, irrespective of input variable. The inverse model presented the greatest effectiveness to predict FLG from T and SUNS, separately (maximum R2adj and minimum RMSE), followed by the logarithmic and the linear ones, under visual examination and applied statistical measures. Air temperature was superior to SUNS in all cases (higher R2adj and lower RMSE), when comparing the regression models of the same type to check their effectiveness for predicting FLG. The findings of our study could be a decisive step towards a better exploitation of the examined meteorological variables for the sustainable production of Chios sheep.

scholarly journals Seasonal variation of wool fibre length in Karagouniko and Chios sheep in relation to meteorological factors

2019 ◽  
pp. 788
Author(s):  
Aristidis Matsoukis
Keyword(s):  
Meteorological Factors ◽  
Fibre Length ◽  
Meteorological Variables ◽  
Wool Fibre ◽  
New Horizons ◽  
Sheep Breeds ◽  
Length Growth ◽  
Chios Sheep ◽  
The Impact ◽  
Opening Up

Karagouniko and Chios sheep breeds present a lot of advantages on the implementation of sheep farming in Greece, a country with diverse relief resulting in a microclimatic variety, but nothing is known about the impact of season, as the outcome of important meteorological variables, on the fibre length growth (FLG) of the aforementioned breeds. Thus, the effect of season (Winter, Spring, Summer, Autumn) on the FLG of these breeds (by using analysis of variance) was studied in the Artificial Insemination Center of Karditsa (39021΄18΄΄N, 21054΄19΄΄E), Periphery of Thessaly, Greece, combined with a correlation analysis between FLG of each examined sheep breed and each of the studied meteorological variables, air temperature (AIRT), relative humidity (RH), sunshine (SUNS) and rainfall (R) for a two-year period. It was found that the FLG of Karagouniko sheep was significantly higher than the respective growth of Chios sheep, for each examined season, while the descending order of seasonal FLG for both breeds was Winter>Spring>Autumn>Summer. Fibre length growth of Karagouniko and Chios breeds correlated negatively with AIRT and SUNS and positively with RH, implying a better FLG in cooler, more overcast and wetter time periods. Our study adds new knowledge concerning the effect of season, and particularly, the effect of the aforementioned meteorological variables on the wool growth of two considerable sheep breeds in Greece, Karagouniko and Chios, opening up new horizons for their exploitation.    

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals Simple regression models can act as calibration-substitute to approximate transient storage parameters in streams

2019 ◽  
Vol 123 ◽  
pp. 201-209 ◽  
Author(s):  
P.V. Femeena ◽  
I. Chaubey ◽  
A. Aubeneau ◽  
S. McMillan ◽  
P.D. Wagner ◽  
...  
Keyword(s):  
Regression Models ◽  
Transient Storage ◽  
Simple Regression

scholarly journals A Note on the Flowering of Ajuga orientalis L. in Relation to Air Temperature in Mount Aenos (Cephalonia, Greece)

2018 ◽  
Vol 6 (3) ◽  
pp. 261-267 ◽  
Author(s):  
Aristidis Matsoukis ◽  
Athanasios Kamoutsis ◽  
Aikaterini Chronopoulou-Sereli
Keyword(s):  
Plant Species ◽  
Air Temperature ◽  
Landscape Design ◽  
Point Of View ◽  
Meteorological Variables ◽  
Phenological Stage ◽  
Aesthetic Value ◽  
Beginning Of Flowering ◽  
Start Dates

Ajuga orientalis L. is a widespread plant species in many countries, such as Greece, Italy and Turkey, with promising aesthetic value in the field and in landscape design, but nothing is known about its phenology, from a detailed, quantitatively, point of view, in relation to meteorological variables. Thus, under the aforementioned context, the purpose of our work is the elucidation of part of the phenology of this plant, especially concerning its flowering. To achieve this, the phenological stage ‘Beginning of flowering’, in terms of its start dates (julian days), was investigated in relation to average air temperature (T) of March in two areas, Roudi and Kaboulieri at north-northwest and south-southeast slopes, respectively, of Mount Aenos, Cephalonia, Greece, for three successive years (2014-2016). From the analysis of the T of March, it was confirmed that Kaboulieri area was significantly warmer (P<0.05) than Roudi area by 0.8 oC both in 2014 and 2015, with a significantly earlier appearance (P<0.05) of ‘Beginning of flowering’ of A. orientalis in Kaboulieri, ranging from 9.1 (2015) to 10.9 (2014) julian days. The findings of our study could be used for the planning of an efficient preservation program process of the aforementioned plant species in a vulnerable mountainous environment, such as the Mount Aenos environment, as well as for its further exploitation as a decorative plant.

scholarly journals Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?

2021 ◽  
Vol 18 (4) ◽  
pp. 280-296
Author(s):  
Abdel Razzaq Al Rababa’a ◽  
Zaid Saidat ◽  
Raed Hendawi
Keyword(s):  
Stock Market ◽  
Regression Models ◽  
Linear Models ◽  
Stock Exchange ◽  
Market Returns ◽  
Stock Market Returns ◽  
Statistical Measures ◽  
Amman Stock Exchange ◽  
Non Linear ◽  
Ann Models

Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).

scholarly journals Meteorological factors responsible for the growth and development of sugarcane at two locations in Rio Grande do Sul, Brazil

2021 ◽  
Vol 51 (10) ◽  
Author(s):  
Fábio Miguel Knapp ◽  
Jaqueline Sgarbossa ◽  
Claiton Nardini ◽  
Denise Schmidt ◽  
Liliane Bárbara Tibolla ◽  
...  
Keyword(s):  
Air Temperature ◽  
Rio Grande ◽  
Morphological Characters ◽  
Temperature Tolerance ◽  
Stem Diameter ◽  
Meteorological Variables ◽  
Rio Grande Do Sul ◽  
Stem Height ◽  
Thermal Sum ◽  
Minimum Air Temperature

ABSTRACT: This study determined the meteorological variable that most contribute to the productivity of sugarcane stalks in the northwest and central regions of Rio Grande do Sul. The following sugarcane genotypes were used: UFSM XIKA FW, UFSM LUCI FW, UFSM PRETA FW, UFSM DINA FW, UFSM MARI FW, and IAC87-3396. The UFSM cultivars originate from a mutation process in the breeding program conducted at the Federal University of Santa Maria, Frederico Westphalen campus, and have low temperature tolerance. The productivity-associated morphological characters included in the models were average stem diameter, average stem number per meter of furrow, and average stem height. The following meteorological variables were used: minimum air temperature, precipitation, incident solar radiation, and accumulated thermal sum. Pearson’s correlation, canonical correlations, and Stepwise regression were performed between morphological characters and meteorological variables: minimum air temperature had the greatest influence on sugarcane productivity in the studied regions, and accumulated thermal sum showed the highest correlation and contributed most to stem diameter and average stem height. Thus, the models indicated that the growth of sugarcane is positively associated with the accumulated thermal sum, and sugarcane can be cultivated at the studied regions.

Effect of input variables on rainfall-runoff modeling using a deep learning method

2021 ◽  
Author(s):  
Kazuki yokoo ◽  
Kei ishida ◽  
Takeyoshi nagasato ◽  
Ali Ercan
Keyword(s):  
Deep Learning ◽  
Air Temperature ◽  
Meteorological Variables ◽  
Learning Method ◽  
Rainfall Runoff ◽  
Model Accuracy ◽  
Runoff Modeling ◽  
Input Variables ◽  
Rainfall Runoff Model ◽  
Runoff Model

&lt;p&gt;In recent years, deep learning has been applied to various issues in natural science, including hydrology. These application results show its high applicability. There are some studies that performed rainfall-runoff modeling by means of a deep learning method, LSTM (Long Short-Term Memory). LSTM is a kind of RNN (Recurrent Neural Networks) that is suitable for modeling time series data with long-term dependence. These studies showed the capability of LSTM for rainfall-runoff modeling. However, there are few studies that investigate the effects of input variables on the estimation accuracy. Therefore, this study, investigated the effects of the selection of input variables on the accuracy of a rainfall-runoff model by means of LSTM. As the study watershed, this study selected a snow-dominated watershed, the Ishikari River basin, which is in the Hokkaido region of Japan. The flow discharge was obtained at a gauging station near the outlet of the river as the target data. For the input data to the model, Meteorological variables were obtained from an atmospheric reanalysis dataset, ERA5, in addition to the gridded precipitation dataset. The selected meteorological variables were air temperature, evaporation, longwave radiation, shortwave radiation, and mean sea level pressure. Then, the rainfall-runoff model was trained with several combinations of the input variables. After the training, the model accuracy was compared among the combinations. The use of meteorological variables in addition to precipitation and air temperature as input improved the model accuracy. In some cases, however, the model accuracy was worsened by using more variables as input. The results indicate the importance to select adequate variables as input for rainfall-runoff modeling by LSTM.&lt;/p&gt;

scholarly journals The sensitivity of modeled snow accumulation and melt to precipitation phase methods across a climatic gradient

2019 ◽  
Vol 23 (9) ◽  
pp. 3765-3786 ◽  
Author(s):  
Keith S. Jennings ◽  
Noah P. Molotch
Keyword(s):  
Logistic Regression ◽  
Air Temperature ◽  
Regression Models ◽  
Binary Logistic Regression ◽  
Snow Accumulation ◽  
Phase Method ◽  
Logistic Regression Models ◽  
Temperature Thresholds ◽  
Snow Cover Duration ◽  
Precipitation Phase

Abstract. A critical component of hydrologic modeling in cold and temperate regions is partitioning precipitation into snow and rain, yet little is known about how uncertainty in precipitation phase propagates into variability in simulated snow accumulation and melt. Given the wide variety of methods for distinguishing between snow and rain, it is imperative to evaluate the sensitivity of snowpack model output to precipitation phase determination methods, especially considering the potential of snow-to-rain shifts associated with climate warming to fundamentally change the hydrology of snow-dominated areas. To address these needs we quantified the sensitivity of simulated snow accumulation and melt to rain–snow partitioning methods at sites in the western United States using the SNOWPACK model without the canopy module activated. The methods in this study included different permutations of air, wet bulb and dew point temperature thresholds, air temperature ranges, and binary logistic regression models. Compared to observations of snow depth and snow water equivalent (SWE), the binary logistic regression models produced the lowest mean biases, while high and low air temperature thresholds tended to overpredict and underpredict snow accumulation, respectively. Relative differences between the minimum and maximum annual snowfall fractions predicted by the different methods sometimes exceeded 100 % at elevations less than 2000 m in the Oregon Cascades and California's Sierra Nevada. This led to ranges in annual peak SWE typically greater than 200 mm, exceeding 400 mm in certain years. At the warmer sites, ranges in snowmelt timing predicted by the different methods were generally larger than 2 weeks, while ranges in snow cover duration approached 1 month and greater. Conversely, the three coldest sites in this work were relatively insensitive to the choice of a precipitation phase method, with average ranges in annual snowfall fraction, peak SWE, snowmelt timing, and snow cover duration of less than 18 %, 62 mm, 10 d, and 15 d, respectively. Average ranges in snowmelt rate were typically less than 4 mm d−1 and exhibited a small relationship to seasonal climate. Overall, sites with a greater proportion of precipitation falling at air temperatures between 0 and 4 ∘C exhibited the greatest sensitivity to method selection, suggesting that the identification and use of an optimal precipitation phase method is most important at the warmer fringes of the seasonal snow zone.

Sign in / Sign up

Export Citation Format

Share Document