scholarly journals Drying Characteristics of Abelmoschus esculentus Thin Piece on Drying in a Fabricated Tunnel Dryer

2019 ◽  
pp. 1-5
Author(s):  
Dinesh Kumar ◽  
L. P. Singh ◽  
A. K. Singh
Keyword(s):  
Experimental Data ◽  
Root Mean Square Error ◽  
Abelmoschus Esculentus ◽  
Mean Bias Error ◽  
Bias Error ◽  
Drying Rate ◽  
Mean Square ◽  
Drying Time ◽  
Drying Characteristics ◽  
Tunnel Dryer

For the storage of crops, various drying processes are used. It should be synthesized on the basis of drying time, product quality such as colour texture and the taste of the product when uses. To study the drying characteristics of Abelmoschus esculentus (bhindi) thin piece was performed for a temperature range of 38°C and 88°C and velocity of air is fixed at 1.1 m/s in the fabricated tunnel. For the investigation of drying, characteristics experiment was performed, the result was found, the drying rate was falling. The sample studied at 38°C was much good in colour texture and aroma than sample studied at 58°C to 88°C. The experimental data were used on different models proposed, by equating the determination coefficient , Mean Bias Error (MBE), decreased  and Root Mean Square Error (RMSE) measured along with investigated moisture ratio.

scholarly journals Drying of MalaysianCapsicum annuumL. (Red Chili) Dried by Open and Solar Drying

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Ahmad Fudholi ◽  
Mohd Yusof Othman ◽  
Mohd Hafidz Ruslan ◽  
Kamaruzzaman Sopian
Keyword(s):  
Root Mean Square ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Solar Drying ◽  
Mean Square ◽  
Drying Time ◽  
Sun Drying ◽  
65 H ◽  
Mean Square Errors

This study evaluated the performance of solar drying in the Malaysian red chili (Capsicum annuumL.). Red chilies were dried down from approximately 80% (wb) to 10% (wb) moisture content within 33 h. The drying process was conducted during the day, and it was compared with 65 h of open sun drying. Solar drying yielded a 49% saving in drying time compared with open sun drying. At the average solar radiation of 420 W/m2and air flow rate of 0.07 kg/s, the collector, drying system, and pickup demonstrated efficiency rates of approximately 28%, 13%, and 45%, respectively. Evaporative capacity ranged from 0.13 to 2.36 kg/h, with an average of 0.97 kg/h. The specific moisture extraction rate (SMER) of 0.19 kg/kWh was obtained. Moreover, the drying kinetics ofC. annuumL. were investigated. A nonlinear regression procedure was used to fit three drying models. These models were compared with experimental data on red chilies dried by open sun drying and those dried by solar drying. The fit quality of the models was evaluated using their coefficient of determination (R2), mean bias error, and root-mean-square error values. The Page model resulted in the highestR2and the lowest mean bias and root-mean-square errors.

scholarly journals REDES NEURAIS ARTIFICIAIS, REGRESSÃO E MÉTODOS EMPÍRICOS PARA A MODELAGEM DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA NA CIDADE DE INHAMBANE, MOÇAMBIQUE

Irriga ◽  
2019 ◽  
Vol 24 (4) ◽  
pp. 802-816
Author(s):  
BARTOLOMEU FÉLIX TANGUNE ◽  
Rodrigo Máximo Sánchez Román
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Reference Evapotranspiration ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Empirical Methods ◽  
E Mail

REDES NEURAIS ARTIFICIAIS, REGRESSÃO E MÉTODOS EMPÍRICOS PARA A MODELAGEM DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA NA CIDADE DE INHAMBANE, MOÇAMBIQUE   BARTOLOMEU FÉLIX TANGUNE1 E RODRIGO MÁXIMO SÁNCHEZ ROMÁN2   1 Departamento de Engenharia Rural, Escola Superior de Desenvolvimento Rural, Universidade Eduardo Mondlane, Vilankulo, Inhambane, Moçambique. E-mail: [email protected]. 2 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Fazenda Experimental Lageado, Avenida Universitária, nº 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP. Brasil. E-mail: [email protected]     1 RESUMO   Estimativa precisa da evapotranspiração de referência (ETo) é importante para dimensionar e fazer manejo de sistemas de irrigação. Métodos de estimativa da ETo (11 métodos empíricos, 10 modelos de regressão múltipla: RLM e 10 redes neurais artificias: RNAs) foram avaliados em relação ao método padrão de Penman Monteith FAO 56, utilizando os seguintes índices: MBE (Mean Bias Error), RMSE (Root Mean Square Error) e R2, sendo RMSE utilizado como critério principal de seleção dos métodos. A significância dos métodos foi avaliada com base no teste t utilizando dados de 1985 a 2009. Os dados meteorológicos utilizados (temperatura máxima: Tmax, temperatura mínima: Tmin e temperatura média: T, umidade relativa, velocidade do vento e insolação) são da estação meteorológica convencional da cidade de Inhambane, Moçambique. Os resultados mostraram que o modelo RLM4 apresentou melhor desempenho (MBE = 0,01 mm.d-1; RMSE = 0,15 mm.d-1; R2 = 0,99). Na falta da radiação solar global, os modelos RLM6 (MBE = -0,01 mm.d-1; RMSE = 0,23 mm.d-1; R2 = 0,97) e RLM10 (MBE = 0,01 mm.d-1; RMSE = 0,23 mm.d-1; R2 = 0,97) podem ser utilizados e exigem a medição da T, Tmax e Tmin, respectivamente. Esses modelos não foram estatisticamente diferentes do método padrão.   Palavras-chave: evapotranspiração, regressão múltipla, redes neurais.     TANGUNE, B. F.; SÁNCHEZ-ROMÁN, R. M. ARTIFICIAL NEURAL NETWORKS, REGRESSION AND EMPIRICAL METHODS FOR REFERENCE EVAPOTRANSPIRATION MODELING IN INHAMBANE CITY, MOZAMBIQUE     2 ABSTRACT   Precise estimation of reference evapotranspiration (ETo) is important for designing and managing irrigation systems. Methods of ETo estimation (11 empirical methods, 10 multiple regression models: RLM and 10 artificial neural networks: RNAs) were evaluated against Penman Monteith FAO 56 method using the following indexes: MBE (Mean Bias Error), RMSE (Root Mean Square Error) and R2, and RMSE was used as the main criterion of method selection. The significance of the methods was evaluated on the basis of the t test using data from 1985 to 2009. The meteorological data used (maximum temperature: Tmax, minimum temperature: Tmin and average temperature: T, relative air humidity, wind speed and solar brightness), from 1985 to 2009, are from the conventional meteorological station of the city of Inhambane, Mozambique. The results showed that the RLM4 model presented better performance (MBE = 0.01 mm.d-1; RMSE = 0.15 mm.d-1; R2 = 0.99). In the absence of global solar radiation, RLM6 (MBE = -0.01 mm.d-1; RMSE = 0.23 mm.d-1; R2 = 0.97) and RLM10 (MBE = 0.01 mm. d-1; RMSE = 0.23 mm.d-1; R2 = 0.97) can be used, which require measurement of T, and Tmax and Tmin, respectively. These models were not statistically different from the standard method.   Keywords: evapotranspiration, multiple regression, neural networks.

scholarly journals Comparison of evapotranspiration models

MAUSAM ◽  
2022 ◽  
Vol 53 (2) ◽  
pp. 119-126
Author(s):  
R. K. MALL ◽  
B. R. D. GUPTA
Keyword(s):  
Root Mean Square Error ◽  
Model Performance ◽  
Uttar Pradesh ◽  
The Other ◽  
Mean Bias Error ◽  
Wheat Crop ◽  
Bias Error ◽  
Mean Square ◽  
Better Than ◽  
Computing Models

Actual evapotranspiration of wheat crop during different year from 1978-79 to 1992-93 was measured daily in Varanasi, Uttar Pradesh using lysimeter. In this study three evapotranspiration computing models namely Doorenbos and Pruitt, Thornthwaite and Soil Plant Atmosphere Water (SPAW) have been used. Comparisons of these three methods show that the SPAW model is better than the other two methods for evapotraspiration estimation. In the present study the MBE (Mean-Bias-Error), RMSE (Root Mean Square Error) and t-statistic have also been obtained for better evaluations of a model performance.

scholarly journals Solar radiation forecasting by using deep neural networks in Eski̇şehi̇r

2021 ◽  
pp. 159-169
Author(s):  
Mohammed QASEM
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Clean Energy ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

According to the World Economic Outlook (WEO), the global demand for energy is presum- ably going to be increased due to growing the world’s population up during the upcoming two decades. As a result of that, apprehensions about environmental effects, which appear as a re- sult of greenhouse gases are grown and cleaner energy technologies are developed. This clearly shows that extended growth of the worldwide market share of clean energy. Solar energy is considered as one of the fundamental types of renewable energy. For this reason, the need for a predictive model that effectively observes solar energy conversion with high performance becomes urgent. In this paper, classic empirical, artificial neural network (ANN), deep neural network (DNN), and time series models are applied, and their results are compared to each other to find the most accurate model for daily global solar radiation (DGSR) estimation. In addition, four regression models have been developed and applied for DGSR estimation. The obtained results are evaluated and compared by the root mean square error (RMSE), rela- tive root mean square error (rRMSE), mean absolute error (MAE), mean bias error (MBE), t-statistic, and coefficient of determination (R2). Finally, simulation results provided that the best result is found by the DNN model.

scholarly journals Using supervised learning to develop BaRAD, a 40-year monthly bias-adjusted global gridded radiation dataset

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
T. C. Chakraborty ◽  
Xuhui Lee
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Diffuse Radiation ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Shortwave Radiation ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square

AbstractDiffuse solar radiation is an important, but understudied, component of the Earth’s surface radiation budget, with most global climate models not archiving this variable and a dearth of ground-based observations. Here, we describe the development of a global 40-year (1980–2019) monthly database of total shortwave radiation, including its diffuse and direct beam components, called BaRAD (Bias-adjusted RADiation dataset). The dataset is based on a random forest algorithm trained using Global Energy Balance Archive (GEBA) observations and applied to the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) dataset at the native MERRA-2 resolution (0.5° by 0.625°). The dataset preserves seasonal, latitudinal, and long-term trends in the MERRA-2 data, but with reduced biases than MERRA-2. The mean bias error is close to 0 (root mean square error = 10.1 W m−2) for diffuse radiation and −0.2 W m−2 (root mean square error = 19.2 W m−2) for the total incoming shortwave radiation at the surface. Studies on atmosphere-biosphere interactions, especially those on the diffuse radiation fertilization effect, can benefit from this dataset.

scholarly journals A High-Resolution Topographic Correction Method for Clear-Sky Solar Irradiance Derived with a Numerical Weather Prediction Model

2011 ◽  
Vol 50 (12) ◽  
pp. 2460-2472 ◽  
Author(s):  
José A. Ruiz-Arias ◽  
David Pozo-Vázquez ◽  
Vicente Lara-Fanego ◽  
Francisco J. Santos-Alamillos ◽  
J. Tovar-Pescador
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Numerical Weather Prediction ◽  
Solar Irradiance ◽  
Weather Prediction ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Numerical Weather

AbstractRugged terrain is a source of variability in the incoming solar radiation field, but the influence of terrain is still not properly included by most current numerical weather prediction (NWP) models. In this work, a downscaling postprocessing method for NWP-model solar irradiance through terrain effects is presented. It allows one to decrease the estimation bias caused by terrain shading and sky-view reduction, and to account for elevation variability, surface orientation, and surface albedo. The method has been applied to a case study in southern Spain using the Weather Research and Forecasting (WRF) mesoscale model with a spatial resolution of 30 arc s, resulting in disaggregated maps of 3 arc s. The validation was based on a radiometric network made of eight stations located in the Natural Park of Sierra Mágina over an area of roughly 30 × 35 km2 and 12 carefully selected cloudless days during a year. Three of the stations were equipped with tilted pyranometers. Their inclination and aspect were visually adjusted to the inclination and aspect of the local terrain and then carefully measured. For horizontal surface, the downscaled irradiance has proven to reduce the root-mean-square error of the WRF model by 20% to about 25 W m−2 in winter and autumn and 60 W m−2 in spring and summer. For tilted surface, downscaling to different spatial resolutions resulted in the best performance for 9 arc s, with root-mean-square error of 45% (57 W m−2) and a mean bias error close to zero.

scholarly journals Performance Analysis of Clear Sky Global Horizontal Irradiance Models: Simple Models Adapted for Local Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Nicholas Kwarikunda ◽  
Zivayi Chiguvare
Keyword(s):  
Solar Energy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Measured Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Mean Square ◽  
Clear Sky

Evaluation of the maximum solar energy potential of a given area for possible deployment of solar energy technologies requires assessment of clear sky solar irradiance for the region under consideration. Such localized assessment is critical for optimal sizing of the technology to be deployed in order to realize the anticipated output. As the measurements are not always available where they are needed, models may be used to estimate them. In this study, three different models were adapted for the geographical location of the area under study and used to estimate clear sky global horizontal irradiance (GHI) at three locations in the subtropical desert climate of Namibia. The three models, selected on the basis of input requirements, were used to compute clear sky GHI at Kokerboom, Arandis, and Auas. The models were validated and evaluated for performance using irradiance data measured at each of the sites for a period of three years by computing statistical parameters such as mean bias error (MBE), root mean square error (RMSE), and the coefficient of determination (R2), normalized MBE, and normalized RMSE. Comparative results between modelled and measured data showed that the models fit well the measured data, with normalized root mean square error values in the range 4–8%, while the R2 value was above 98% for the three models. The adapted models can thus be used to compute clear sky GHI at these study areas as well as in other regions with similar climatic conditions.

scholarly journals The Analysis of Dynamic O/D Adjustment for Bicycle Traffic Demand Estimation with AIMSUN Simulation Model: A Case Study of Nakhon Sawan Municipality in Thailand

2018 ◽  
Vol 12 (1) ◽  
pp. 352-365 ◽  
Author(s):  
Karn Chalermwongphan ◽  
Prapatpong Upala
Keyword(s):  
Simulation Model ◽  
Root Mean Square ◽  
Goodness Of Fit ◽  
Simulation Software ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Goodness Of Fit Test ◽  
Traffic Demand

Aim: This research aimed to present the process of estimating bicycle traffic demand in order to design bike routes that meet the daily transportation needs of the people in Nakhon Sawan Municipality. Methods: The primary and secondary traffic data were collected to develop a virtual traffic simulation model with the use of the AIMSUN simulation software. The model validation method was carried out to adjust the origin and destination survey data (O/D matrix) by running dynamic O/D adjustment. The 99 replication scenarios were statistically examined and assessed using the goodness-of-fit test. The 9 measures, which were examined, included: 1) Root Mean Square Error (RMSE), 2) Root Mean Square Percentage Error (RMSPE%), 3) Mean Absolute Deviation (MAD), 4) Mean Bias Error (MBE), 5) Mean Percentage Error (MPE%), 6) Mean Absolute Percentage Error (MAPE%), 7) Coefficient of Determination (R2), 8) GEH Statistic (GEH), and 9) Thiel’s U Statistic (Theil’s U). Results: The resulting statistical values were used to determine the acceptable ranges according to the acceptable indicators of each factor. Conclusion: It was found that there were only 8 scenarios that met the evaluation criteria. The selection and ranking process was consequently carried out using the multi-factor scoring method, which could eliminate errors that might arise from applying only one goodness-of-fit test measure.

scholarly journals A Comparison of Models for Estimating Solar Radiation from Sunshine Duration in Croatia

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Tihomir Betti ◽  
Ivana Zulim ◽  
Slavica Brkić ◽  
Blanka Tuka
Keyword(s):  
Mean Absolute Error ◽  
Sunshine Duration ◽  
Model Performance ◽  
Absolute Error ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Bias Error ◽  
Mean Square ◽  
Using Data ◽  
Statistical Indicators

The performance of seventeen sunshine-duration-based models has been assessed using data from seven meteorological stations in Croatia. Conventional statistical indicators are used as numerical indicators of the model performance: mean absolute percentage error (MAPE), mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE). The ranking of the models was done using the combination of all these parameters, all having equal weights. The Rietveld model was found to perform the best overall, followed by Soler and Dogniaux-Lemoine monthly dependent models. For three best-performing models, new adjusted coefficients are calculated, and they are validated using separate dataset. Only the Dogniaux-Lemoine model performed better with adjusted coefficients, but across all analysed locations, the adjusted models showed improvement in reduced maximum percentage error.

The thin-layer drying characteristics of sewage sludge by the appropriate foaming pretreatment

2014 ◽  
Vol 69 (9) ◽  
pp. 1859-1866 ◽  
Author(s):  
Hui-Ling Wang ◽  
Zhao-Hui Yang ◽  
Jing Huang ◽  
Li-Ke Wang ◽  
Cheng-Liu Gou ◽  
...  
Keyword(s):  
Sample Thickness ◽  
Coefficient Of Determination ◽  
Drying Rate ◽  
Drying Time ◽  
Drying Characteristics ◽  
Thin Layer Drying ◽  
Dewatered Sludge ◽  
Drying Curves ◽  
Sludge Drying ◽  
The Impact

As dewatered sludge is highly viscous and sticky, the combination of foaming pretreatment and drying process seems to be an alternative method to improve the drying performance of dewatered sludge. In this study, CaO addition followed by mechanical whipping was employed for foaming the dewatered sludge. It was found that the foams were stable and the diameters of bubbles mainly ranged from 0.1 to 0.3 mm. The drying experiments were carried out in a drying oven in the convective mode. The results indicated that foamed sludge at 0.70 g/cm3 had the best drying performance at each level of temperature, which could save 35–45% drying time to reach 20% moisture content compared with the non-foamed sludge. The drying rate of foamed sludge at 0.70 g/cm3 was improved with the increasing of drying temperature. The impact of sample thickness on drying rate was not obvious when the sample thickness increased from 2 to 8 mm. Different mathematical models were used for the simulation of foamed sludge drying curves. The Wang and Singh model represented the drying characteristics better than other models with coefficient of determination values over 0.99.

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