scholarly journals River ice and water temperature prediction on the Danube

2021 ◽  
Vol 70 (3) ◽  
pp. 201-214
Author(s):  
Zoltán Árpád Liptay ◽  
◽  
Szabolcs Czigány ◽  
Ervin Pirkhoffer ◽  
◽  
...  
Keyword(s):  
Water Temperature ◽  
Prediction Accuracy ◽  
Ice Formation ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Sensible Heat ◽  
Weighted Mean ◽  
Temperature Prediction ◽  
Temperature Calculation

This paper presents a modification of the theory of weighted mean temperatures for rivers. Rodhe, B. (1952) assumed the dominance of sensible heat transfer on ice formation. We aimed to improve the method for the evaluation of ice and water temperature based on a relatively low number of inputs. We further developed the model by introducing the effect of pre-existing ice, hence increasing the accuracy of the model on the timing of ice disappearance. Prediction accuracy of ±1 day was reached for the timing of the appearance of ice. Additional outputs have also been added to the model, including the termination of ice and the prediction of water temperature. The temperature calculation had a coefficient of determination of 95 percent, and a root mean square error of 1.33 °C during the calibration period without the use of observed water temperatures. The validation was carried out in a forecasting situation, and the results were compared to the energy balance.

scholarly journals Multi-State Load Demand Forecasting Using Hybridized Support Vector Regression Integrated with Optimal Design of Off-Grid Energy Systems—A Metaheuristic Approach

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1166
Author(s):  
Bashir Musa ◽  
Nasser Yimen ◽  
Sani Isah Abba ◽  
Humphrey Hugh Adun ◽  
Mustafa Dagbasi
Keyword(s):  
Support Vector Regression ◽  
Mean Square Error ◽  
Prediction Accuracy ◽  
Demand Forecasting ◽  
Load Forecasting ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Support Vector ◽  
Mean Square ◽  
Hybrid Renewable Energy System

The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

scholarly journals COMPARAÇÃO ENTRE MEDIDAS EVAPOTRANSPIROMÉTRICAS E METODOLOGIA DA FAO, NA DETERMINAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA

Irriga ◽  
1999 ◽  
Vol 4 (1) ◽  
pp. 31-42 ◽  
Author(s):  
Marco Antonio Lunardi ◽  
Dalva Martinelli Cury Lunardi ◽  
Nariaqui Cavaguti
Keyword(s):  
Standard Error ◽  
Reference Evapotranspiration ◽  
Potential Evapotranspiration ◽  
Moving Average ◽  
Sensible Heat Flux ◽  
Coefficient Of Determination ◽  
Sensible Heat ◽  
Class A ◽  
Class A Pan

COMPARAÇÃO ENTRE MEDIDAS EVAPOTRANSPIROMÉTRICAS E METODOLOGIA DA FAO, NA DETERMINAÇÃO DA EVAPOTRANSPIRAÇÃO DE REFERÊNCIA   Marco Antônio LunardiFaculdade de Ciências Agronômicas - UNESP - Campus de Botucatu - Botucatu, SP  CEP 18603-110Dalva Martinelli Cury LunardiDepartamento de Ciências Ambientais - Faculdade de Ciências Agronômicas - UNESP Campus de Botucatu – Botucatu, SP – CEP 18603-110Nariaqui CavagutiDepartamento de Engenharia e Tecnologia Civil - Faculdade de Engenharia Civil UNESP - Campus de Bauru – Bauru, SP – CEP 17033-360   1 RESUMO   Este estudo foi conduzido na área experimental do Departamento de Ciências Ambientais da Faculdade de  Ciências Agronômicas - UNESP, Botucatu -  S.P. (latitude 22o 51’ S, longitude 48o 26’ W, altitude 786 m), no período de 01 de outubro de 1994 a 30 de setembro de 1995.As estimativas diárias da evapotranspiração potencial (ETo), através dos métodos de Penman-Monteith, Penman-FAO, Radiação Solar FAO e Tanque Classe A, foram comparadas com os valores de ETo medidos com 5 (cinco) evapotranspirômetros de lençol freático de nível constante, mantidos a 60 cm de profundidade, em intervalos de 1, 3, 5 e  10 dias nas 04 estações do ano.Os resultados indicaram que esses equipamentos são adequados para a medida da evapotranspiração de referência, não só pela facilidade operacional, mas também por sua precisão. O emprego dos métodos demonstrou ser dependente da estação do ano, da redução do erro padrão de estimativa e do aumento do coeficiente de determinação, como função do intervalo de  tempo  considerado.Todos os métodos indicaram sensibilidade, sempre que a energia  foi utilizada essencialmente na forma de calor sensível, superestimando a evapotranspi- ração nas condições experimentais.O intervalo de 5 a 10 dias apresentou os menores valores de erro padrão e os maiores valores do coeficiente  de determinação, sendo a diferença entre os métodos atribuída aos dados climáticos considerados por cada um.Finalmente os resultados indicaram que os métodos de Penman-Monteith e Penman-FAO determinam com maior precisão a evapotranspiração.   UNITERMOS: evapotranspirômetro, evapotranspiração de referência, métodos de estimativa.   LUNARDI, M.A., LUNARDI, D.M.C., CAVAGUTI, N. COMPARISON BETWEEN EVAPOTRANSPIROMETRIC MEASUREMENT AND FAO METHODOLOGIES IN DETERMINATION OF THE POTENTIAL EVAPOTRANSPIRATION   2 ABSTRACT   In the present work daily potential evapotranspiration values was calculated for Botucatu - S.P. (22o 51’ S, 48o 26’ W, 786m) using Penman Monteith, FAO Penman, FAO Solar Radiation and Class A Pan methods between october, 1984 and september, 1995.The estimating methods was compared with grass reference evapotranspiration obtained with five evapotranspirometers of constant ground water at 60 cm depth, using a moving average of 1 - 10 days on spring, summer, autumn and winter.The study indicates that the equipaments are appropriate for the  evaluation of reference evapotranspiration not only by the operational facilities  but also by the accuracy.The results indicate that the methods applicability is a function of the season of the year, reduction of standard error and  increasing of determination coefficient in the considered intervals.Several estimating methods  showed sensitive even when litlle energy was  utilized in sensible heat flux and overestimated the evapotranspiration in the experimental condition.The 5 -10  days moving average can reduce the standard error of estimate and increase the coefficient of determination significantly between estimated and measured reference evapotranspiration for several estimating methods.Finally, the results indicated that FAO Penman and Penman - Monteith methods are the most realible indication of evapotranspiration.   KEYWORDS: Evapotranspirometer, reference evapotranpiration, estimative methods.

scholarly journals Validation of the procedure: Quantification of the degradation index of Photovoltaic Grid Connection Systems

2021 ◽  
Vol 2 (5) ◽  
pp. 8-13
Author(s):  
Proenza Y. Roger ◽  
Camejo C. José Emilio ◽  
Ramos H. Rubén
Keyword(s):  
Mean Absolute Error ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Degradation Index ◽  
Statistical Parameters ◽  
Grid Connection ◽  
The Mean

The results obtained from the validation of the procedure ‟Quantification of the degradation index of Photovoltaic Grid Connection Systems” are presented, using statistical parameters, which corroborate its accuracy, achieving a coefficient of determination of 0.9896, a percentage of the root of the mean square of the error RMSPE = 1.498% and a percentage of the mean absolute error MAPE = 1.15%, evidencing the precision of the procedure.

PREDICTION OF THE WEIGHT OF QUAIL EGGS THROUGH SHAPE FEATURES AND SPECTRAL INDICES

2019 ◽  
pp. 125-132 ◽  
Author(s):  
Mariya Georgieva-Nikolova ◽  
Zlatin Zlatev
Keyword(s):  
Mean Square Error ◽  
Japanese Quail ◽  
Coefficient Of Determination ◽  
Transmission Spectra ◽  
Mean Square ◽  
Spectral Indices ◽  
Shape Features ◽  
Indirect Determination ◽  
Internal Properties

In the present work, a method for indirect determination of the weight of Japanese quail eggs is proposed, taking into account changes in their internal properties. Visual data and transmission spectra is used. Shape features and spectral indices are selected and applied. It has been found that egg weight M can be predicted by the volume V of eggs and the spectral index GLI, M=f(V,GLI). The resulting model has a coefficient of determination R2=0,89, low error values, up to 3%. Mean square error MSE=0,03 and root mean square error RMSE=0,2. The results obtained can be used to indirect determination the weight of Japanese quail eggs when incubated, packaged.

Indikatoren schulischen Wohlbefindens bei gesunden und chronisch kranken Kindern: Psychometrische Prüfung und Validierung adaptierter FEESS-Skalen

2020 ◽  
Vol 34 (3-4) ◽  
pp. 201-219
Author(s):  
Kathleen Schnick-Vollmer ◽  
Christiane Diefenbach ◽  
Christine Gräf ◽  
Dorle Hoffmann ◽  
Isabell Hoffmann ◽  
...  
Keyword(s):  
Item Response ◽  
Mean Square ◽  
Weighted Mean ◽  
Externe Validität ◽  
Chronisch Kranke

Zusammenfassung. Das schulbezogene Wohlbefinden (SBWB) ist eine wichtige Voraussetzung für schulischen Erfolg. Trotzdem existieren – insbesondere mit Blick auf die Erfassung des SBWB von Erstklässlern – im deutschsprachigen Raum nur vereinzelt Studien. Dies lässt sich möglicherweise durch das Fehlen geeigneter Instrumente begründen. Dies gilt auch und insbesondere dann, wenn der Gesundheitszustand der Kinder berücksichtigt werden soll. Das Ziel der vorliegenden Arbeit besteht in der Validierung des adaptierten Fragebogens zur Erfassung von emotionalen und sozialen Schulerfahrungen (FEESS 1 – 2; Rauer & Schuck, 2004 ) mit Fokus auf die Eignung des Instruments für chronisch kranke und gesunde Kinder. Dafür wird zunächst das Konstrukt Wohlbefinden (WB) resp. SBWB definiert und in einschlägige Theorien – die Selbstbestimmungstheorie nach Deci und Ryan (1985) und das Erwartung-mal-Wert-Modell nach Wigfield und Eccles (2000) – eingebettet. Die Bedeutung der verwendeten FEESS-Skalen und ihr Zusammenhang zum schulischen Erfolg werden aufgezeigt. 1491 Kinder wurden zu ihrer Lernfreude (LF), sozialen Integration (SI) und zu ihrem schulbezogenen Fähigkeitsselbstkonzept (SK) befragt. Die Erfassung des Gesundheitszustands wurde über Elternfragebögen und Schuleingangsuntersuchungen eruiert. Zudem wurden die Eltern zur gesundheitsbezogenen Lebensqualität (LQ) ihrer Kinder mit Hilfe eines Fragebogens zur Erfassung der Lebensqualität von Kindern (KINDL; Bullinger, Mackensen & Kirchberger, 1994 ) befragt. Die psychometrische Qualität der adaptierten FEESS-Skalen wurde für beide Gruppen (erkrankt / gesund) auf Skalen- und Itemebene untersucht. Hierzu kamen sowohl klassische Verfahren als auch Verfahren der Item-Response-Theorie zum Einsatz. Die Ergebnisse untermauern die Validität des Konstruktes SBWB und stützen die Annahme der Dreidimensionalität (LF, SI, SK). Alle drei Skalen zeigen eine zufriedenstellende bis sehr gute Reliabilität. Die Items zeigen sehr gute MNSQ-Werte (weighted mean-square; gewichtete Abweichungsquadrate) und geeignete Trennschärfen. Die externe Validität, für deren Berechnung der Zusammenhang zwischen den Angaben der Kinder und den Angaben der Eltern zur gesundheitsbezogenen LQ untersucht wurde, konnte noch nicht ausreichend nachgewiesen werden. Bis auf diese Einschränkung kann mit Hilfe der adaptierten FEESS-Skalen im nächsten Schritt das SBWB von gesunden und erkrankten Kindern verglichen werden, um mögliche Chancenungleichheiten auszugleichen.

scholarly journals Modeling Population Spatial-Temporal Distribution Using Taxis Origin and Destination Data

2021 ◽  
Vol 13 (7) ◽  
pp. 3727
Author(s):  
Fatema Rahimi ◽  
Abolghasem Sadeghi-Niaraki ◽  
Mostafa Ghodousi ◽  
Soo-Mi Choi
Keyword(s):  
Regression Analysis ◽  
Mean Squared Error ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Coefficient Of Determination ◽  
Temporal Modeling ◽  
Location Data ◽  
Time Period ◽  
Proposed Model

During dangerous circumstances, knowledge about population distribution is essential for urban infrastructure architecture, policy-making, and urban planning with the best Spatial-temporal resolution. The spatial-temporal modeling of the population distribution of the case study was investigated in the present study. In this regard, the number of generated trips and absorbed trips using the taxis pick-up and drop-off location data was calculated first, and the census population was then allocated to each neighborhood. Finally, the Spatial-temporal distribution of the population was calculated using the developed model. In order to evaluate the model, a regression analysis between the census population and the predicted population for the time period between 21:00 to 23:00 was used. Based on the calculation of the number of generated and the absorbed trips, it showed a different spatial distribution for different hours in one day. The spatial pattern of the population distribution during the day was different from the population distribution during the night. The coefficient of determination of the regression analysis for the model (R2) was 0.9998, and the mean squared error was 10.78. The regression analysis showed that the model works well for the nighttime population at the neighborhood level, so the proposed model will be suitable for the day time population.

scholarly journals Histamine Control in Raw and Processed Tuna: A Rapid Tool Based on NIR Spectroscopy

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 885
Author(s):  
Sergio Ghidini ◽  
Luca Maria Chiesa ◽  
Sara Panseri ◽  
Maria Olga Varrà ◽  
Adriana Ianieri ◽  
...  
Keyword(s):  
Root Mean Square ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Tuna Fish ◽  
Mean Square Errors

The present study was designed to investigate whether near infrared (NIR) spectroscopy with minimal sample processing could be a suitable technique to rapidly measure histamine levels in raw and processed tuna fish. Calibration models based on orthogonal partial least square regression (OPLSR) were built to predict histamine in the range 10–1000 mg kg−1 using the 1000–2500 nm NIR spectra of artificially-contaminated fish. The two models were then validated using a new set of naturally contaminated samples in which histamine content was determined by conventional high-performance liquid chromatography (HPLC) analysis. As for calibration results, coefficient of determination (r2) > 0.98, root mean square of estimation (RMSEE) ≤ 5 mg kg−1 and root mean square of cross-validation (RMSECV) ≤ 6 mg kg−1 were achieved. Both models were optimal also in the validation stage, showing r2 values > 0.97, root mean square errors of prediction (RMSEP) ≤ 10 mg kg−1 and relative range error (RER) ≥ 25, with better results showed by the model for processed fish. The promising results achieved suggest NIR spectroscopy as an implemental analytical solution in fish industries and markets to effectively determine histamine amounts.

scholarly journals Sustainability Performance Assessment Using Self-Organizing Maps (SOM) and Classification and Ensembles of Regression Trees (CART)

2021 ◽  
Vol 13 (7) ◽  
pp. 3870
Author(s):  
Mehrbakhsh Nilashi ◽  
Shahla Asadi ◽  
Rabab Ali Abumalloh ◽  
Sarminah Samad ◽  
Fahad Ghabban ◽  
...  
Keyword(s):  
Performance Assessment ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Sustainability Assessment ◽  
Regression Trees ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Sustainability Performance ◽  
Learning Techniques ◽  
Self Organizing

This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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