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1
Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)?" Geoscientific Model Development Discussions 7, no. 1 (February 28, 2014): 1525–34. http://dx.doi.org/10.5194/gmdd-7-1525-2014.
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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error and thus the MAE would be a better metric for that purpose. Their paper has been widely cited and may have influenced many researchers in choosing MAE when presenting their model evaluation statistics. However, we contend that the proposed avoidance of RMSE and the use of MAE is not the solution to the problem. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric.
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Chai, T., and R. R. Draxler. "Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature." Geoscientific Model Development 7, no. 3 (June 30, 2014): 1247–50. http://dx.doi.org/10.5194/gmd-7-1247-2014.
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Abstract. Both the root mean square error (RMSE) and the mean absolute error (MAE) are regularly employed in model evaluation studies. Willmott and Matsuura (2005) have suggested that the RMSE is not a good indicator of average model performance and might be a misleading indicator of average error, and thus the MAE would be a better metric for that purpose. While some concerns over using RMSE raised by Willmott and Matsuura (2005) and Willmott et al. (2009) are valid, the proposed avoidance of RMSE in favor of MAE is not the solution. Citing the aforementioned papers, many researchers chose MAE over RMSE to present their model evaluation statistics when presenting or adding the RMSE measures could be more beneficial. In this technical note, we demonstrate that the RMSE is not ambiguous in its meaning, contrary to what was claimed by Willmott et al. (2009). The RMSE is more appropriate to represent model performance than the MAE when the error distribution is expected to be Gaussian. In addition, we show that the RMSE satisfies the triangle inequality requirement for a distance metric, whereas Willmott et al. (2009) indicated that the sums-of-squares-based statistics do not satisfy this rule. In the end, we discussed some circumstances where using the RMSE will be more beneficial. However, we do not contend that the RMSE is superior over the MAE. Instead, a combination of metrics, including but certainly not limited to RMSEs and MAEs, are often required to assess model performance.
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Ren, Tao, Xiaoqing Kang, Wen Sun, and Hong Song. "Study of Dynamometer Cards Identification Based on Root-Mean-Square Error Algorithm." International Journal of Pattern Recognition and Artificial Intelligence 32, no. 02 (November 12, 2017): 1850004. http://dx.doi.org/10.1142/s0218001418500040.
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The surface dynamometer cards are important working condition data of sucker-rod pumping system. It has a very important practical significance for the analysis of transmission system and the diagnosis of oil production condition of sucker-rod pumping system. The pump dynamometer cards are important reference for the diagnosis of oil production condition, and its key technology is the identification of pump dynamometer cards. A new similar pattern recognition algorithm based on root-mean-square error (RMSE) is proposed, a theoretical model of the similarity matching algorithm based on RMSE is established, and the algorithm is studied and analyzed. The three-dimensional vibration mathematical models for the surface dynamometer cards are created, by which the surface dynamometer cards can be transformed to the pump dynamometer cards. The accuracy, reliability and stability between the algorithm of RMSE similarity matching and the classical algorithms of similarity pattern matching are studied. The research shows that the resistance to the graphics deformation of RMSE algorithm is the highest among all algorithms. The application of RMSE algorithm and classic similarity matching algorithms to the identification of real pump dynamometer cards and the fault diagnosis of oil wells indicates that the RMSE algorithm has very high identification reliability and accuracy. The remarkable feature of the RMSE algorithm is that it has very high identification accuracy for small difference, while the classical similarity matching algorithms do not have this feature.
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Karno, Adhitio Satyo Bayangkari. "Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory)." Journal of Informatic and Information Security 1, no. 1 (May 29, 2020): 1–8. http://dx.doi.org/10.31599/jiforty.v1i1.133.
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Abstract
This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE value will be easier to obtain. From the research of BBRI's stock data prediction, a good RMSE value was obtained (RMSE = 227.470333244533).
Keywords: long short-term memory, machine learning, epoch, root mean square error, mean square error.
Abstrak
Penelitian ini bertujuan untuk mengukur ketelitian dalam memprediksi data time series menggunakan metode mesin belajar LSTM (Long Short-Term Memory), serta menentukan banyaknya epoch yang diperlukan untuk menghasilkan nilai RMSE (Root Mean Square Error) yang kecil. Hasil dari penelitian ini adalah tingkat variasi yang tinggi nilai rmse terhdap jumlah epoch yang diperlukan dalam proses pengolahan data. Variasi ini cukup menyulitkan untuk memperoleh nilai epoch yang tepat. Dengan melakukan iterasi dari proses LSTM terhadap jumlah epoch yang berbeda (di visualisasikan dalam grafik), maka jumlah epoch dengan nilai RMSE minimal akan lebih mudah diperoleh. Dari penelitan prediksi data saham BBRI diperoleh nilai RMSE yang cukup baik yaitu 227,470333244533.
Kata kunci: long short-term memory, machine learning, epoch, root mean square error, mean square error.
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Ganji, Homayoon, and Takamitsu Kajisa. "Error propagation approach for estimating root mean square error of the reference evapotranspiration when estimated with alternative data." Journal of Agricultural Engineering 50, no. 3 (September 10, 2019): 120–26. http://dx.doi.org/10.4081/jae.2019.909.
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Estimation of reference evapotranspiration (ET0) with the Food and Agricultural Organisation (FAO) Penman-Monteith model requires temperature, relative humidity, solar radiation, and wind speed data. The lack of availability of the complete data set at some meteorological stations is a severe restriction for the application of this model. To overcome this problem, ET0 can be calculated using alternative data, which can be obtained via procedures proposed in FAO paper No.56. To confirm the validity of reference evapotranspiration calculated using alternative data (ET0(Alt)), the root mean square error (RMSE) needs to be estimated; lower values of RMSE indicate better validity. However, RMSE does not explain the mechanism of error formation in a model equation; explaining the mechanism of error formation is useful for future model improvement. Furthermore, for calculating RMSE, ET0 calculations based on both complete and alternative data are necessary. An error propagation approach was introduced in this study both for estimating RMSE and for explaining the mechanism of error formation by using data from a 30-year period from 48 different locations in Japan. From the results, RMSE was confirmed to be proportional to the value produced by the error propagation approach (ΔET0). Therefore, the error propagation approach is applicable to estimating the RMSE of ET0(Alt) in the range of 12%. Furthermore, the error of ET0(Alt) is not only related to the variables’ uncertainty but also to the combination of the variables in the equation.
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Wang, Weijie, and Yanmin Lu. "Analysis of the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) in Assessing Rounding Model." IOP Conference Series: Materials Science and Engineering 324 (March 2018): 012049. http://dx.doi.org/10.1088/1757-899x/324/1/012049.
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Gede Adi, Wiguna Sudiartha, Oginawati Katharina, Sofyan Asep, Ardiwinata, Kurnia Asep, Sukarjo, Handayani Cici, and Sulaeman. "One-Dimensional Pollutant Transport Modelling of Cadmium (Cd), Chromium (Cr) and Lead (Pb) in Saguling Reservoir." E3S Web of Conferences 148 (2020): 07009. http://dx.doi.org/10.1051/e3sconf/202014807009.
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The existing conditions of the Saguling Reservoir are reported to have suffered severe heavy metal pollution due to the presence of wastewater inputs from various types of industries flowing into Citarum River and then accumulating in the Saguling Reservoir. From the results of calibration tests of heavy metal models on water using the Root Mean Square Error (RMSE) analysis and Relative Error (RE) analysis, obtained dispersion coefficients on Cadmium, Chromium, and Lead metals sequentially 1 m2 / second (with RMSE 0,00515 and 34% relative error); 1 m2 / second (with RMSE 0.00595 and relative error 26%); and 2.5 m2 / second (with RMSE 0.028205 and relative error 41.25%) which shows that the model has good capability to simulate the concentration of heavy metals approaching the actual data both in the dry and wet seasons. From the results of the verification test models of concentration of cadmium, lead and chromium in sediments using the Root Mean Square Error (RMSE) analysis and Relative Error (RE) analysis, obtained sequentially 18.53 and 77%; 10.43 and 47.15%; 2.789 and 33%. Error values in sediment concentrations are quite large because of the difficulty of making assumptions that are close to natural conditions.
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Willmott, CJ, and K. Matsuura. "Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance." Climate Research 30 (2005): 79–82. http://dx.doi.org/10.3354/cr030079.
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Zamhuri Fuadi, Azam, Irsyad Nashirul Haq, and Edi Leksono. "Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 3 (June 19, 2021): 466–73. http://dx.doi.org/10.29207/resti.v5i3.2947.
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Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimal parameter values C 1e6 and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14.
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Fortin, V., M. Abaza, F. Anctil, and R. Turcotte. "Why Should Ensemble Spread Match the RMSE of the Ensemble Mean?" Journal of Hydrometeorology 15, no. 4 (July 30, 2014): 1708–13. http://dx.doi.org/10.1175/jhm-d-14-0008.1.
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Abstract When evaluating the reliability of an ensemble prediction system, it is common to compare the root-mean-square error of the ensemble mean to the average ensemble spread. While this is indeed good practice, two different and inconsistent methodologies have been used over the last few years in the meteorology and hydrology literature to compute the average ensemble spread. In some cases, the square root of average ensemble variance is used, and in other cases, the average of ensemble standard deviation is computed instead. The second option is incorrect. To avoid the perpetuation of practices that are not supported by probability theory, the correct equation for computing the average ensemble spread is obtained and the impact of using the wrong equation is illustrated.
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Xu, H., R. De Jong, S. Gameda, and B. Qian. "Development and evaluation of a Canadian agricultural ecodistrict climate database." Canadian Journal of Soil Science 90, no. 2 (May 1, 2010): 373–85. http://dx.doi.org/10.4141/cjss09064.
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Spatially representative climate data are required input in various agricultural and environmental modelling studies. An agricultural ecodistrict climate database for Canada was developed from climate station data using a spatial interpolation procedure. This database includes daily maximum and minimum air temperatures, precipitation and incoming global solar radiation, which are necessary inputs for many agricultural modelling studies. The spatial interpolation procedure combines inverse distance squared weighting with the nearest neighbour approach. Cross-validation was performed to evaluate the accuracy of the interpolation procedure. In addition to some common error measurements, such as mean biased error and root mean square error, empirical probability distributions and accurate rates of precipitation occurrence were also examined. Results show that the magnitude of errors for this database was similar to those in other studies that used similar or different interpolation procedures. The average root mean square error (RMSE) was 1.7°C, 2.2°C and 3.8 mm for daily maximum and minimum temperature, and precipitation, respectively. The RMSE for solar radiation varied from 16 to 19% of the climate normal during April through September and from 21 to 28% of the climate normal during the remainder of the year.Key words: Maximum and minimum temperature, precipitation, solar radiation, ecodistrict, interpolation, cross validation
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Anderson, Kelly J., and John H. Kalivas. "Assessment of Pareto Calibration, Stability, and Wavelength Selection." Applied Spectroscopy 57, no. 3 (March 2003): 309–16. http://dx.doi.org/10.1366/000370203321558227.
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Recent work has shown that ridge regression (RR) is Pareto to partial least squares (PLS) and principal component regression (PCR) when the variance indicator Euclidian norm of the regression coefficients, ‖p̂‖, is plotted against the bias indicator root mean square error of calibration (RMSEC). Simplex optimization demonstrates that RR is Pareto for several other spectral data sets when ‖p̂‖ is used with RMSEC and the root mean square error of evaluation (RMSEE) as optimization criteria. From this investigation, it was observed that while RR is Pareto optimal, PLS and PCR harmonious models are near equivalent to harmonious RR models. Additionally, it was found that RR is Pareto robust, i.e., models formed at one temperature were then used to predict samples at another temperature. Wavelength selection is commonly performed to improve analysis results such that bias indicators RMSEC, RMSEE, root mean square error of validation, or root mean square error of cross-validation decrease using a subset of wavelengths. Just as critical to an analysis of selected wavelengths is an assessment of variance. Using wavelengths deemed optimal in a previous study, this paper reports on the variance/bias tradeoff. An approach that forms the Pareto model with a Pareto wavelength subset is suggested.
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Ahmad, Ayaz, Furqan Farooq, Pawel Niewiadomski, Krzysztof Ostrowski, Arslan Akbar, Fahid Aslam, and Rayed Alyousef. "Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm." Materials 14, no. 4 (February 8, 2021): 794. http://dx.doi.org/10.3390/ma14040794.
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Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.
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Al Mahrouq, Yousef A. "Effect of Item Difficulty and Sample Size on the Accuracy of Equating by Using Item Response Theory." Journal of Educational and Psychological Studies [JEPS] 10, no. 1 (January 1, 2016): 182. http://dx.doi.org/10.24200/jeps.vol10iss1pp182-200.
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This study explored the effect of item difficulty and sample size on the accuracy of equating by using item response theory. This study used simulation data. The equating method was evaluated using an equating criterion (SEE, RMSE). Standard error of equating between the criterion scores and equated scores, and root mean square error of equating (RMSE) were used as measures to compare the method to the criterion equating. The results indicated that the large sample size reduces the standard error of the equating and reduces residuals. The results also showed that different difficulty models tend to produce smaller standard errors and the values of RMSE. The similar difficulty models tend to produce decreasing standard errors and the values of RMSE.
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Suleman, Salman, and Roys Pakaya. "PREDIKSI HASIL PRODUKSI IKAN TUNA MENGGUNAKAN ALGORITMA NEURAL NETWORK BERBASIS FORWARD SELECTION." Jurnal Technopreneur (JTech) 6, no. 1 (May 24, 2018): 1. http://dx.doi.org/10.30869/jtech.v6i1.159.
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Potensi perikanan dan kelautan merupakan modal dasar pembangunan Provinsi Gorontalo. Berdasarkan persentase rata-rata hasil produksi ikan tuna yang meningkat setiap tahunnya maka perlu dilakukan prediksi hasil produksi ikan tuna dalam rangka peningkatan sektor produksi perikanan kelautan. Metode prediksi times series mimiliki tingkat error data yang rendah dan baik yaitu Neural Network. Metode pelatihan yang dapat digunakan dalam memperbaiki bobot jaringan syaraf tiruan adalah backpropagation. Akan tetapi terdapat beberapa kelemahan diantaranya masalah waktu pelatihan yang lama dalam mencapai konvergen, dan masalah pada overfitting.Untuk itu dalam mengatasi masalah pada metode Neural Nerwork pada penelitian ini ditambahkan metode seleksi fitur yang akan digunakan yaitu forward selection dengan harapan mampu menghasilkan tingkat akurasi yang lebih baik. Dengan penggunaan fitur selection dalam jaringan syaraf tiruan mampu mempercepat waktu kalkulasi dan mendapatkan hasil prediksi yang akurat, sehingga dapat digunakan dalam memprediksi hasil produksi ikan tuna.Dengan membandingkan hasil Root Mean Square Error (RMSE) menggunakan Neural Network yaitu 0,096, sedangkan dengan menggunakan metode Neural Network berbasis Forward Selection didapatkan hasil Root Mean Square Error (RMSE) yaitu 0,080. Hal ini membuktikan bahwa dengan menerapkan pola yang didapatkan dari metode Neural Network Berbasis Forward Selection dalam melakukan prediksi mampu mengurangi nilai Root Mean Square Error (RMSE) dengan hasil selisih yaitu sebesar 0,016.
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Basalekou, M., C. Pappas, Y. Kotseridis, P. A. Tarantilis, E. Kontaxakis, and S. Kallithraka. "Red Wine Age Estimation by the Alteration of Its Color Parameters: Fourier Transform Infrared Spectroscopy as a Tool to Monitor Wine Maturation Time." Journal of Analytical Methods in Chemistry 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5767613.
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Color, phenolic content, and chemical age values of red wines made from Cretan grape varieties (Kotsifali, Mandilari) were evaluated over nine months of maturation in different containers for two vintages. The wines differed greatly on their anthocyanin profiles. Mid-IR spectra were also recorded with the use of a Fourier Transform Infrared Spectrophotometer in ZnSe disk mode. Analysis of Variance was used to explore the parameter’s dependency on time. Determination models were developed for the chemical age indexes using Partial Least Squares (PLS) (TQ Analyst software) considering the spectral region 1830–1500 cm−1. The correlation coefficients (r) for chemical age index i were 0.86 for Kotsifali (Root Mean Square Error of Calibration (RMSEC) = 0.067, Root Mean Square Error of Prediction (RMSEP) = 0,115, and Root Mean Square Error of Validation (RMSECV) = 0.164) and 0.90 for Mandilari (RMSEC = 0.050, RMSEP = 0.040, and RMSECV = 0.089). For chemical age index ii the correlation coefficients (r) were 0.86 and 0.97 for Kotsifali (RMSEC 0.044, RMSEP = 0.087, and RMSECV = 0.214) and Mandilari (RMSEC = 0.024, RMSEP = 0.033, and RMSECV = 0.078), respectively. The proposed method is simpler, less time consuming, and more economical and does not require chemical reagents.
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Xin, Chen, Xueqing Shi, Dongsheng Wang, Chong Yang, Qian Li, and Hongbin Liu. "Multi-grained cascade forest for effluent quality prediction of papermaking wastewater treatment processes." Water Science and Technology 81, no. 5 (March 1, 2020): 1090–98. http://dx.doi.org/10.2166/wst.2020.206.
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Abstract The real time estimation of effluent indices of papermaking wastewater is vital to environmental conservation. Ensemble methods have significant advantages over conventional single models in terms of prediction accuracy. As an ensemble method, multi-grained cascade forest (gcForest) is implemented for the prediction of wastewater indices. Compared with the conventional modeling methods including partial least squares, support vector regression, and artificial neural networks, the gcForest model shows prediction superiority for effluent suspended solid (SSeff) and effluent chemical oxygen demand (CODeff). In terms of SSeff, gcForest achieves the highest correlation coefficient with a value of 0.86 and the lowest root-mean-square error (RMSE) value of 0.41. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 46.05% to 50.60%. In terms of CODeff, gcForest achieves the highest correlation coefficient with a value of 0.83 and the lowest root-mean-square error value of 4.05. In comparison with the conventional models, the RMSE value using gcForest is reduced by approximately 10.60% to 18.51%.
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Kim, Cho Hwe, and Young Chul Kim. "Application of Artificial Neural Network Over Nickel-Based Catalyst for Combined Steam-Carbon Dioxide of Methane Reforming (CSDRM)." Journal of Nanoscience and Nanotechnology 20, no. 9 (September 1, 2020): 5716–19. http://dx.doi.org/10.1166/jnn.2020.17627.
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The application of artificial neural network (ANN) for modeling, combined steam-carbon dioxide reforming of methane over nickel-based catalysts, was investigated. The artificial neural network model consisted of a 3-layer feed forward network, with hyperbolic tangent function. The number of hidden neurons is optimized by minimization of mean square error and maximization of R2 (R square, coefficient of determination) and set of 8 neurons. With feed ratio, flow rate, and temperature as independent variables, methane, carbon dioxide conversion, and H2/CO ratio, were measured using artificial neural network. Coefficient of determination (R2) values of 0.9997, 0.9962, and 0.9985 obtained, and MAE (Mean Absolute Error), MSE (Mean Squared Error), RMSE (Root Mean Squared Error), and MAPE (Mean Absolute Percentage Error) showed low value. This study indicates ANN can successfully model a highly nonlinear process and function.
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Sangalugeme, Chuki, Philbert Luhunga, Agness Kijazi, and Hamza Kabelwa. "Validation of Operational WAVEWATCH III Wave Model Against Satellite Altimetry Data Over South West Indian Ocean Off-Coast of Tanzania." Applied Physics Research 10, no. 4 (July 13, 2018): 55. http://dx.doi.org/10.5539/apr.v10n4p55.
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The WAVEWATCH III model is a third generation wave model and is commonly used for wave forecasting over different oceans. In this study, the performance of WAVEWATCH III to simulate Ocean wave characteristics (wavelengths, and wave heights (amplitudes)) over the western Indian Ocean in the Coast of East African countries was validated against satellite observation data. Simulated significant wave heights (SWH) and wavelengths over the South West Indian Ocean domain during the month of June 2014 was compared with satellite observation. Statistical measures of model performance that includes bias, Mean Error (ME), Root Mean Square Error (RMSE), Standard Deviation of error (SDE) and Correlation Coefficient (r) are used. It is found that in June 2014, when the WAVEWATCH III model was forced by wind data from the Global Forecasting System (GFS), simulated the wave heights over the Coast of East African countries with biases, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (r) and Standard Deviation of error (SDE) in the range of -0.25 to -0.39 m, 0.71 to 3.38 m, 0.84 to 1.84 m, 0.55 to 0.76 and 0.38 to 0.44 respectively. While, when the model was forced by wind data from the European Centre for Medium Range Weather Foresting (ECMWF) simulated wave height with biases, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (r) and Standard Deviation of error (SDE) in the range of -0.034 to 0.008 m, 0.0006 to 0.049 m, 0.026 to 0.22 m, 0.76 to 0.89 and 0.31 to 0.41 respectively. This implies that the WAVEWATCH III model performs better in simulating wave characteristics over the South West of Indian Ocean when forced by the boundary condition from ECMWF than from GFS.
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Huang, Hongxia, Yuanyuan Lv, Xiaoyi Sun, Shuangshuang Fu, Xuefang Lou, and Zengmei Liu. "Rapid determination of tannin in Danshen and Guanxinning injections using UV spectrophotometry for quality control." Journal of Innovative Optical Health Sciences 11, no. 06 (November 2018): 1850034. http://dx.doi.org/10.1142/s1793545818500347.
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A technique for the determination of tannin content in traditional Chinese medicine injections (TCMI) was developed based on ultraviolet (UV) spectroscopy. Chemometrics were used to construct a mathematical model of absorption spectrum and tannin reference content of Danshen and Guanxinning injections, and the model was verified and applied. The results showed that the established UV-based spectral partial least squares regression (PLS) tannin content model performed well with a correlation coefficient ([Formula: see text]) of 0.952, root mean square error of calibration (RMSEC) of 0.476[Formula: see text][Formula: see text]g/ml, root mean square error of validation (RMSEV) of 1.171[Formula: see text][Formula: see text]g/ml, and root mean square error of prediction (RMSEP) of 0.465[Formula: see text][Formula: see text]g/ml. Pattern recognition models using linear discriminant analysis (LDA) and [Formula: see text] nearest neighbor ([Formula: see text]-NN) classifiers based on UV spectrum could successfully classify different types of injections and different manufacturers. The established method to measure tannin content based on UV spectroscopy is simple, rapid and reliable and provides technical support for quality control of tannin in Chinese medicine injections.
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Zhang, Wei, Hang Song, Jing Lu, Wen Liu, Lirong Nie, and Shun Yao. "Online NIR Analysis and Prediction Model for Synthesis Process of Ethyl 2-Chloropropionate." International Journal of Analytical Chemistry 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/145315.
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Online near-infrared spectroscopy was used as a process analysis technique in the synthesis of 2-chloropropionate for the first time. Then, the partial least squares regression (PLSR) quantitative model of the product solution concentration was established and optimized. Correlation coefficient (R2) of partial least squares regression (PLSR) calibration model was 0.9944, and the root mean square error of correction (RMSEC) was 0.018105 mol/L. These values of PLSR and RMSEC could prove that the quantitative calibration model had good performance. Moreover, the root mean square error of prediction (RMSEP) of validation set was 0.036429 mol/L. The results were very similar to those of offline gas chromatographic analysis, which could prove the method was valid.
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Badawi, Ahmed Samir, Siti Hajar Yusoff, Alhareth Mohammed Zyoud, Sheroz Khan, Aisha Hashim, Yılmaz Uyaroğlu, and Mahmoud Ismail. "Data bank: nine numerical methods for determining the parameters of weibull for wind energy generation tested by five statistical tools." International Journal of Power Electronics and Drive Systems (IJPEDS) 12, no. 2 (June 1, 2021): 1114. http://dx.doi.org/10.11591/ijpeds.v12.i2.pp1114-1130.
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This study aims to determine the potential of wind energy in the mediterranean coastal plain of Palestine. The parameters of the Weibull distribution were calculated on basis of wind speed data. Accordingly, two approaches were employed: analysis of a set of actual time series data and theoretical Weibull probability function. In this analysis, the parameters Weibull shape factor ‘<em>k</em>’ and the Weibull scale factor ‘<em>c</em>’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empirical method (EM), maximum likelihood method (MLM), modified maximum likelihood method (MMLM), second modified maximum likelihood method (SMMLM), graphical method (GM), least mean square method (LSM) and energy pattern factor method (EPF). The performance of these numerical methods was tested by root mean square error (RMSE), index of agreement (IA), Chi-square test (X<sup>2</sup>), mean absolute percentage error (MAPE) and relative root mean square error (RRMSE) to estimate the percentage of error. Among the prediction techniques. The EPF exhibited the greatest accuracy performance followed by MM and MLM, whereas the SMMLM exhibited the worst performance. The RMSE achieved the best prediction accuracy, whereas the RRMSE attained the worst prediction accuracy.
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Kotivuori, Eetu, Matti Maltamo, Lauri Korhonen, Jacob L. Strunk, and Petteri Packalen. "Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches." Forestry: An International Journal of Forest Research 94, no. 4 (March 24, 2021): 576–87. http://dx.doi.org/10.1093/forestry/cpab007.
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Abstract In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error ($\overline{\mathrm{RMSE}}$) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation ($\overline{\mathrm{RMSE}}$: 17.6 percent, 17.4 percent), followed by ABA ($\overline{\mathrm{RMSE}}$: 19.3 percent, 18.2 percent) and ITD ($\overline{\mathrm{RMSE}}$: 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level ($\overline{\mathrm{RMSE}}$ 15.5 and 15.3 percent), with poorer ITD performance ($\overline{\mathrm{RMSE}}$ 19.4 percent).
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Shafi, Muhammad Ammar, Mohd Saifullah Rusiman, Kavikumar Jacob, Nor Shamsidah Amir Hamzah, Norziha Che Him, and Nazeera Mohamad. "Prediction in a Hybrid of Fuzzy Linear Regression with Symmetric Parameter Model and Fuzzy C-Means Method Using Simulation Data." International Journal of Engineering & Technology 7, no. 4.30 (November 30, 2018): 419. http://dx.doi.org/10.14419/ijet.v7i4.30.22351.
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The objective of fuzzy linear regression model (FLRM) to predict the dependent variable and independent variables in vague phenomenon. In this study, several models such as fuzzy linear regression model (FLRM), fuzzy linear regression with symmetric parameter (FLWSP) and a hybrid model have been applied to be evaluated by 1000 rows in 1 simulation data. Moreover, the hybrid method was applied between fuzzy linear regression with symmetric parameter (FLRWSP) and fuzzy c-mean (FCM) method to get the effective prediction in a new model and best result in this study. To improve the accuracy of evaluating and predicting, this study employ two measurement error of cross validation statistical technique which are mean square error (MSE) and root mean square error (RMSE). The simulation result suggests that comparison among models using two measurement errors should be to determine the best results. Finally, this study notes that the new hybrid model of FLRWSP and FCM is verified to be a good model with the least value of MSE and RMSE measurement errors.
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Hussain, Lal, Sharjil Saeed, Adnan Idris, Imtiaz Ahmed Awan, Saeed Arif Shah, Abdul Majid, Bilal Ahmed, and Quratul-Ain Chaudhary. "Regression analysis for detecting epileptic seizure with different feature extracting strategies." Biomedical Engineering / Biomedizinische Technik 64, no. 6 (December 18, 2019): 619–42. http://dx.doi.org/10.1515/bmt-2018-0012.
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Abstract Due to the excitability of neurons in the brain, a neurological disorder is produced known as epilepsy. The brain activity of patients suffering from epilepsy is monitored through electroencephalography (EEG). The multivariate nature of features from time domain, frequency domain, complexity and wavelet entropy based, and the statistical features were extracted from healthy and epileptic subjects using the Bonn University database and seizure and non-seizure intervals using the CHB MIT database. The robust machine learning regression methods based on regression, support vector regression (SVR), regression tree (RT), ensemble regression, Gaussian process regression (GPR) were employed for detecting and predicting epileptic seizures. Performance was measured in terms of root mean square error (RMSE), squared error, mean square error (MSE) and mean absolute error (MAE). Moreover, detailed optimization was performed using a RT to predict the selected features from each feature category. A deeper analysis was conducted on features and tree regression methods where optimal RMSE and MSE results were obtained. The best optimal performance was obtained using the ensemble boosted regression tree (BRT) and exponential GPR with an RMSE of 0.47, an MSE (0.22), an R Square (RS) (0.25) and an MAE (0.30) using the Bonn University database and support vector machine (SVM) fine Gaussian with RMSE (0.63634), RS (0.03), MSE (0.40493) and MAE (0.31744); squared exponential GPR and rational quadratic GPR with an RMSE of 0.63841, an RS (0.03), an MSE (0.40757) and an MAE (0.3472) was obtained using the CHB MIT database. A further deeper analysis for the prediction of selected features was performed on an RT to compute the optimal feasible point, observed and estimated function values, function evaluation time, objective function evaluation time and overall elapsed time.
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Gajdzik, Bożena. "Forecasts of Size of Steel Production in Poland until 2022." Multidisciplinary Aspects of Production Engineering 1, no. 1 (September 1, 2018): 499–505. http://dx.doi.org/10.2478/mape-2018-0063.
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Abstract The article presents the results of forecasting the volume (size) of steel production in Poland based on selected adaptation models. The data used in forecasting were the annual size of steel production in the period from 2000 to 2017. Data on the size of steel production in Poland were obtained from reports of both the Polish Steel Association in Katowice (Poland) and the World Steel Association. The accuracy of predictions was determined by the values of real deviation of forecasted variable from forecasts (extinguished - ex post) using square root calculated from mean square error of apparent forecasts, ie RMSE - Root Mean Square Error and mean value of relative error of expired forecasts Ψ. Forecasts can be used in making decisions in metallurgical enterprises for building production scenarios.
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Qiu, Hui, and Shuanggen Jin. "Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry." Remote Sensing 12, no. 3 (January 21, 2020): 356. http://dx.doi.org/10.3390/rs12030356.
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Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error ( RMSE ) of 3.56 m and, a correlation coefficient ( R ) of 0.95.
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Aijaz, Iflah, and Parul Agarwal. "A Study on Time Series Forecasting using Hybridization of Time Series Models and Neural Networks." Recent Advances in Computer Science and Communications 13, no. 5 (November 5, 2020): 827–32. http://dx.doi.org/10.2174/1573401315666190619112842.
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Introduction: Auto-Regressive Integrated Moving Average (ARIMA) and Artificial Neural Networks (ANN) are leading linear and non-linear models in Machine learning respectively for time series forecasting. Objective: This survey paper presents a review of recent advances in the area of Machine Learning techniques and artificial intelligence used for forecasting different events. Methods: This paper presents an extensive survey of work done in the field of Machine Learning where hybrid models for are compared to the basic models for forecasting on the basis of error parameters like Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Normalized Root Mean Square Error (NRMSE). Results: Table 1 summarizes important papers discussed in this paper on the basis of some parameters which explain the efficiency of hybrid models or when the model is used in isolation. Conclusion: The hybrid model has realized accurate results as compared when the models were used in isolation yet some research papers argue that hybrids cannot always outperform individual models.
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Seasholtz, M. B., D. D. Archibald, A. Lorber, and B. R. Kowalski. "Quantitative Analysis of Liquid Fuel Mixtures with the Use of Fourier Transform Near-IR Raman Spectroscopy." Applied Spectroscopy 43, no. 6 (August 1989): 1067–72. http://dx.doi.org/10.1366/0003702894203985.
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Near-infrared Fourier transform (near-IR FT) Raman spectroscopy was used to predict mass percentages of liquid fuel mixtures without the use of an internal standard. The 29 mixtures making up the calibration set were composed of varying mass percentages of unleaded gasoline, superunleaded gasoline, and diesel. Predictions were made with the use of classical least-squares with the pure components. Root mean square error (RMSE) values for all samples were 13.5, 13.8, and 5.2% (absolute error) for unleaded, superunleaded, and diesel, respectively. Using an estimate of the pure spectra determined by calibration gave RMSE values of 13.3% for unleaded, 12.2% for superunleaded, and 5.0% for diesel. A partial least-squares (PLS) model was able to partially compensate for matrix effects. Using different portions of the Raman spectra reduced the RMSE to 5.7% for unleaded, 5.2% for superunleaded, and 1.3% for diesel.
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Górnicki, Krzysztof, Radosław Winiczenko, Agnieszka Kaleta, and Aneta Choińska. "Evaluation of models for the dew point temperature determination." Technical Sciences 3, no. 20 (May 4, 2017): 241–57. http://dx.doi.org/10.31648/ts.5425.
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The accuracy of the available from the literature models for the dew point temperature determination was compared. The proposal of the modelling using artificial neural networks was also given. The experimental data were taken from the psychrometric tables. The accuracies of the models were measured using the mean bias error MBE, root mean square error RMSE, correlation coefficient R, and reduced chi-square χ2. Model M3, especially with constants A=237, B=7.5, gave the best results in determining the dew point temperature (MBE: -0.0229 – 0.0038 K, RMSE: 0.1259 – 0.1286 K, R=0.9999, χ2: 0.0159 – 0.0166 K2). Model M1 with constants A=243.5, B=17.67 and A=243.3, B=17.269 can be also considered as appropriate (MBE=-0.0062 and -0.0078 K, RMSE=0.1277 and 0.1261 K, R=0.9999, χ2=0.0163 and 0.0159 K2). Proposed ANN model gave the good results in determining the dew point temperature (MBE=-0.0038 K, RMSE=0.1373 K, R=0.9999, χ2=0.0189 K2).
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Nguyen, Vu, Binh Pham, Ba Vu, Indra Prakash, Sudan Jha, Himan Shahabi, Ataollah Shirzadi, et al. "Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling." Forests 10, no. 2 (February 12, 2019): 157. http://dx.doi.org/10.3390/f10020157.
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This paper presents novel hybrid machine learning models, namely Adaptive Neuro Fuzzy Inference System optimized by Particle Swarm Optimization (PSOANFIS), Artificial Neural Networks optimized by Particle Swarm Optimization (PSOANN), and Best First Decision Trees based Rotation Forest (RFBFDT), for landslide spatial prediction. Landslide modeling of the study area of Van Chan district, Yen Bai province (Vietnam) was carried out with the help of a spatial database of the area, considering past landslides and 12 landslide conditioning factors. The proposed models were validated using different methods such as Area under the Receiver Operating Characteristics (ROC) curve (AUC), Mean Square Error (MSE), Root Mean Square Error (RMSE). Results indicate that the RFBFDT (AUC = 0.826, MSE = 0.189, and RMSE = 0.434) is the best method in comparison to other hybrid models, namely PSOANFIS (AUC = 0.76, MSE = 0.225, and RMSE = 0.474) and PSOANN (AUC = 0.72, MSE = 0.312, and RMSE = 0.558). Thus, it is reasonably concluded that the RFBFDT is a promising hybrid machine learning approach for landslide susceptibility modeling.
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Wang, Sijia, Yunhao Chen, Mingguo Wang, Yifei Zhao, and Jing Li. "SPA-Based Methods for the Quantitative Estimation of the Soil Salt Content in Saline-Alkali Land from Field Spectroscopy Data: A Case Study from the Yellow River Irrigation Regions." Remote Sensing 11, no. 8 (April 23, 2019): 967. http://dx.doi.org/10.3390/rs11080967.
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The problem of soil salinization has always been a global problem involving resource, environmental, and ecological issues, and is closely related to the sustainable development of the social economy. Remote sensing provides an effective technical means for soil salinity identification and quantification research. This study focused on the estimation of the soil salt content in saline-alkali soils and applied the Successive Projections Algorithm (SPA) method to the estimation model; twelve spectral forms were applied in the estimation model of the spectra and soil salt content. Regression modeling was performed using the Partial Least Squares Regression (PLSR) method. Proximal-field spectral measurements data and soil samples were collected in the Yellow River Irrigation regions of Shizuishan City. A total of 60 samples were collected. The results showed that application of the SPA method improved the modeled determination coefficient (R2) and the ratio of performance to deviation (RPD), and reduced the modeled root mean square error (RMSE) and the percentage root mean square error (RMSE%); the maximum value of R2 increased by 0.22, the maximum value of RPD increased by 0.97, the maximum value of the RMSE decreased by 0.098 and the maximum value of the RMSE% decreased by 8.52%. The SPA–PLSR model, based on the first derivative of reflectivity (FD), the FD–SPA–PLSR model, showed the best results, with an R2 value of 0.89, an RPD value of 2.72, an RMSE value of 0.177, and RMSE% value of 11.81%. The results of this study demonstrated the applicability of the SPA method in the estimation of soil salinity, by using field spectroscopy data. The study provided a reference for a subsequent study of the hyperspectral estimation of soil salinity, and the proximal sensing data from a low distance, in this study, could provide detailed data for use in future remote sensing studies.
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Ma, Yue, Guo-Zheng Zhang, and Sedjoah Aye-Ayire Rita-Cindy. "Quantification of Water, Protein and Soluble Sugar in Mulberry Leaves Using a Handheld Near-Infrared Spectrometer and Multivariate Analysis." Molecules 24, no. 24 (December 4, 2019): 4439. http://dx.doi.org/10.3390/molecules24244439.
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Mulberry (Morus alba L.) leaves are not only used as the main feed for silkworms (Bombyx mori) but also as an added feed for livestock and poultry. In order to rapidly select high-quality mulberry leaves, a hand-held near-infrared (NIR) spectrometer combined with partial least squares (PLS) regression and wavelength optimization methods were used to establish a predictive model for the quantitative determination of water content in fresh mulberry leaves, as well as crude protein and soluble sugar in dried mulberry leaves. For the water content in fresh mulberry leaves, the R-square of the calibration set ( R C 2 ), R-square of the cross-validation set ( R C V 2 ) and R-square of the prediction set ( R P 2 ) are 0.93, 0.90 and 0.91, respectively, the corresponding root mean square error of calibration set (RMSEC), root mean square error of cross-validation set (RMSECV) and root mean square error of prediction set (RMSEP) are 0.96%, 1.13%, and 1.18%, respectively. The R C 2 , R C V 2 and R P 2 of the crude protein prediction model are 0.91, 0.83 and 0.92, respectively, and the corresponding RMSEC, RMSECV and RMSEP are 0.71%, 0.97% and 0.61%, respectively. The soluble sugar prediction model has R C 2 , R C V 2 , and R P 2 of 0.64, 0.51, and 0.71, respectively, and the corresponding RMSEC, RMSECV, and RMSEP are 2.33%, 2.73%, and 2.36%, respectively. Therefore, the use of handheld NIR spectrometers combined with wavelength optimization can fastly detect the water content in fresh mulberry leaves and crude protein in dried mulberry leaves. However, it is a slightly lower predictive performance for soluble sugar in mulberry leaves.
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Khan, Zulfiqar Ahmad, Amin Ullah, Waseem Ullah, Seungmin Rho, Miyoung Lee, and Sung Wook Baik. "Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy." Applied Sciences 10, no. 23 (December 2, 2020): 8634. http://dx.doi.org/10.3390/app10238634.
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Smart grid technology based on renewable energy and energy storage systems are attracting considerable attention towards energy crises. Accurate and reliable model for electricity prediction is considered a key factor for a suitable energy management policy. Currently, electricity consumption is rapidly increasing due to the rise in human population and technology development. Therefore, in this study, we established a two-step methodology for residential building load prediction, which comprises two stages: in the first stage, the raw data of electricity consumption are refined for effective training; and the second step includes a hybrid model with the integration of convolutional neural network (CNN) and multilayer bidirectional gated recurrent unit (MB-GRU). The CNN layers are incorporated into the model as a feature extractor, while MB-GRU learns the sequences between electricity consumption data. The proposed model is evaluated using the root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE) metrics. Finally, our model is assessed over benchmark datasets that exhibited an extensive drop in the error rate in comparison to other techniques. The results indicated that the proposed model reduced errors over the individual household electricity consumption prediction (IHEPC) dataset (i.e., RMSE (5%), MSE (4%), and MAE (4%)), and for the appliances load prediction (AEP) dataset (i.e., RMSE (2%), and MAE (1%)).
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Xaba, Diteboho, Ntebogang Dinah Moroke, Johnson Arkaah, and Charlemagne Pooe. "A Comparative Study Of Stock Price Forecasting Using Nonlinear Models." Risk Governance and Control: Financial Markets and Institutions 7, no. 2 (2017): 7–17. http://dx.doi.org/10.22495/rgcv7i2art1.
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This study compared the in-sample forecasting accuracy of three forecasting nonlinear models namely: the Smooth Transition Regression (STR) model, the Threshold Autoregressive (TAR) model and the Markov-switching Autoregressive (MS-AR) model. Nonlinearity tests were used to confirm the validity of the assumptions of the study. The study used model selection criteria, SBC to select the optimal lag order and for the selection of appropriate models. The Mean Square Error (MSE), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) served as the error measures in evaluating the forecasting ability of the models. The MS-AR models proved to perform well with lower error measures as compared to LSTR and TAR models in most cases.
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Młyński, Dariusz, Andrzej Wałęga, Andrea Petroselli, Flavia Tauro, and Marta Cebulska. "Estimating Maximum Daily Precipitation in the Upper Vistula Basin, Poland." Atmosphere 10, no. 2 (January 23, 2019): 43. http://dx.doi.org/10.3390/atmos10020043.
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The aim of this study was to determine the best probability distributions for calculating the maximum annual daily precipitation with the specific probability of exceedance (Pmaxp%). The novelty of this study lies in using the peak-weighted root mean square error (PWRMSE), the root mean square error (RMSE), and the coefficient of determination (R2) for assessing the fit of empirical and theoretical distributions. The input data included maximum daily precipitation records collected in the years 1971–2014 at 51 rainfall stations from the Upper Vistula Basin, Southern Poland. The value of Pmaxp% was determined based on the following probability distributions of random variables: Pearson’s type III (PIII), Weibull’s (W), log-normal, generalized extreme value (GEV), and Gumbel’s (G). Our outcomes showed a lack of significant trends in the observation series of the investigated random variables for a majority of the rainfall stations in the Upper Vistula Basin. We found that the peak-weighted root mean square error (PWRMSE) method, a commonly used metric for quality assessment of rainfall-runoff models, is useful for identifying the statistical distributions of the best fit. In fact, our findings demonstrated the consistency of this approach with the RMSE goodness-of-fit metrics. We also identified the GEV distribution as recommended for calculating the maximum daily precipitation with the specific probability of exceedance in the catchments of the Upper Vistula Basin.
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Pei, Yanling, Zhisheng Wu, Xinyuan Shi, Xiaoning Pan, Yanfang Peng, and Yanjiang Qiao. "NIR assignment of isopsoralen by 2D-COS technology and model application in Yunkang Oral Liquid." Journal of Innovative Optical Health Sciences 08, no. 06 (October 27, 2015): 1550023. http://dx.doi.org/10.1142/s1793545815500236.
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Near infrared (NIR) assignment of Isopsoralen was performed using deuterated chloroform solvent and two-dimensional correlation spectroscopy (2D-COS) technology. Yunkang Oral Liquid was applied to study Isopsoralen, the characteristic bands by spectral assignment as well as the bands by interval partial least squares (iPLS) and synergy interval partial least squares (siPLS) were used to establish partial least squares (PLS) model. The coefficient of determination in calibration [Formula: see text] were 0.9987, 0.9970 and 0.9982. The coefficient of determination in cross validation [Formula: see text] were 0.9985, 0.9921 and 0.9982. The coefficient of determination in prediction [Formula: see text] were 0.9987, 0.9955 and 0.9988. The root mean square error of calibration (RMSEC) were 0.27, 0.40 and 0.31 ppm. The root mean square error of cross validation (RMSECV) were 0.30, 0.67 and 0.32 ppm. The root mean square error of prediction (RMSEP) were 0.23, 0.43 and 0.22 ppm. The residual predictive deviation (RPD) were 31.00, 16.58 and 32.41. It turned out that the characteristic bands by spectral assignment had the same results with the chemometrics methods in PLS model. It provided guidance for NIR spectral assignment of chemical compositions in Chinese Materia Medica (CMM).
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Ebtehaj, Isa, and Hossein Bonakdari. "Comparison of genetic algorithm and imperialist competitive algorithms in predicting bed load transport in clean pipe." Water Science and Technology 70, no. 10 (October 25, 2014): 1695–701. http://dx.doi.org/10.2166/wst.2014.434.
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The existence of sediments in wastewater greatly affects the performance of the sewer and wastewater transmission systems. Increased sedimentation in wastewater collection systems causes problems such as reduced transmission capacity and early combined sewer overflow. The article reviews the performance of the genetic algorithm (GA) and imperialist competitive algorithm (ICA) in minimizing the target function (mean square error of observed and predicted Froude number). To study the impact of bed load transport parameters, using four non-dimensional groups, six different models have been presented. Moreover, the roulette wheel selection method is used to select the parents. The ICA with root mean square error (RMSE) = 0.007, mean absolute percentage error (MAPE) = 3.5% show better results than GA (RMSE = 0.007, MAPE = 5.6%) for the selected model. All six models return better results than the GA. Also, the results of these two algorithms were compared with multi-layer perceptron and existing equations.
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Bengtsson, Lisa K., Linus Magnusson, and Erland Källén. "Independent Estimations of the Asymptotic Variability in an Ensemble Forecast System." Monthly Weather Review 136, no. 11 (November 1, 2008): 4105–12. http://dx.doi.org/10.1175/2008mwr2526.1.
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Abstract One desirable property within an ensemble forecast system is to have a one-to-one ratio between the root-mean-square error (rmse) of the ensemble mean and the standard deviation of the ensemble (spread). The ensemble spread and forecast error within the ECMWF ensemble prediction system has been extrapolated beyond 10 forecast days using a simple model for error growth. The behavior of the ensemble spread and the rmse at the time of the deterministic predictability are compared with derived relations of rmse at the infinite forecast length and the characteristic variability of the atmosphere in the limit of deterministic predictability. Utilizing this methodology suggests that the forecast model and the atmosphere do not have the same variability, which raises the question of how to obtain a perfect ensemble.
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DALIMUNTHE, DESY YULIANA. "FIT OF STATISTICAL FORECASTING MODEL BERDASARKAN VARIABEL ANGKA KEMISKINAN DI PROVINSI KEPULAUAN BANGKA BELITUNG." E-Jurnal Matematika 9, no. 2 (May 26, 2020): 117. http://dx.doi.org/10.24843/mtk.2020.v09.i02.p288.
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Poverty is one of the main problems in economic development and is considered to be a variable to measure the success of the economic development of a region. This study is limited to the analysis and determination of the best forecasting statistical model for the variable poverty rate in the Bangka Belitung Islands Province area based on R Square, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) assessments. This study uses the Exponential Smoothing forecasting method which emphasizes the procedure of continuous improvement of the latest observation objects which hopefully can provide the appropriate results. In general, the double exponential smoothing model from Holt's is the best projection model compared to other exponential smoothing models for projecting poverty data in the Bangka Belitung Islands Province with historical data for 2002-2018 with an increase in projections in 2019 of 0.37 % with Upper Criteria Limit (UCL) of 1.07% and Lower Criteria Limit (LCL) of -0.33% with a value of R Square of 0.627 which means that the independent variable can explain the variance of the dependent variable of 62.7% of this model, and the value of RMSE is 0.328 and MAPE is 22.162. The results of this model when compared to other models have relatively larger R Squared values ??and smaller RMSE and MAPE values.
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Keawbunsong, Pitak, Sarun Duangsuwan, Pichaya Supanakoon, and Sathaporn Promwong. "Quantitative Measurement of Path Loss Model Adaptation Using the Least Squares Method in an Urban DVB-T2 System." International Journal of Antennas and Propagation 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/7219618.
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The aim of this paper was to propose quantitative measurement of path loss model adaptation in urban radio propagation for a second-generation, terrestrial digital video broadcasting standard (DVB-T2) system. The measurement data was analyzed using data processing based on the least squares (LS) method to verify the probabilistic quantitation of realistic data measurement such as mean error (ME), root mean square error (RMSE), and standard deviation of error (SD), as well as relative error (RE). To distinguish the experimental evaluation, the researchers compared between the conventional Hata path loss model and the proposed model. The result showed that path loss based on the proposed model was more accurate in predicting the quantitative measurement of propagation data properly.
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Chhabra, Mohit, and Rajneesh Kumar. "Comparison of Different Edge Detection Techniques to Improve Quality of Medical Images." Journal of Computational and Theoretical Nanoscience 17, no. 6 (June 1, 2020): 2496–507. http://dx.doi.org/10.1166/jctn.2020.8921.
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In modern era the major challenge will betodetect diseases from Medical Images. To curb this challenge, different efficient image feature extraction techniques was required in medical fields. Today Medical field industry today deals with millions of images of different disease of brain heart, lungs. So in this paper, we had presented a comparison among different feature extraction technique like Canny, Laplacian of Gaussian, Sobel, Prewit on large number of images of lung disease. The objective of our research work was to find best extraction techniques based on various image quality parameters such as Mean absolute error (MAE), Root mean square error (RMSE), mean square error (MSE), Signal to noise ratio (SNR).
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Mugume, Isaac, Charles Basalirwa, Daniel Waiswa, Joachim Reuder, Michel d. S. Mesquita, Sulin Tao, and Triphonia J. Ngailo. "Comparison of Parametric and Nonparametric Methods for Analyzing the Bias of a Numerical Model." Modelling and Simulation in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7530759.
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Numerical models are presently applied in many fields for simulation and prediction, operation, or research. The output from these models normally has both systematic and random errors. The study compared January 2015 temperature data for Uganda as simulated using the Weather Research and Forecast model with actual observed station temperature data to analyze the bias using parametric (the root mean square error (RMSE), the mean absolute error (MAE), mean error (ME), skewness, and the bias easy estimate (BES)) and nonparametric (the sign test, STM) methods. The RMSE normally overestimates the error compared to MAE. The RMSE and MAE are not sensitive to direction of bias. The ME gives both direction and magnitude of bias but can be distorted by extreme values while the BES is insensitive to extreme values. The STM is robust for giving the direction of bias; it is not sensitive to extreme values but it does not give the magnitude of bias. The graphical tools (such as time series and cumulative curves) show the performance of the model with time. It is recommended to integrate parametric and nonparametric methods along with graphical methods for a comprehensive analysis of bias of a numerical model.
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Sagar, Pinki, Prinima Gupta, and Rohit Tanwar. "A novel prediction algorithm for multivariate data sets." Decision Making: Applications in Management and Engineering 4, no. 2 (October 15, 2021): 225–40. http://dx.doi.org/10.31181/dmame210402215s.
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Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.
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Fu, Zhengqing, Guolin Liu, Lanlan Guo, Weike Liu, and Hua Guo. "Nonlinear Least Square Based on Control Direction by Dual Method and Its Application." Mathematical Problems in Engineering 2016 (2016): 1–5. http://dx.doi.org/10.1155/2016/7094157.
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A direction controlled nonlinear least square (NLS) estimation algorithm using the primal-dual method is proposed. The least square model is transformed into the primal-dual model; then direction of iteration can be controlled by duality. The iterative algorithm is designed. The Hilbert morbid matrix is processed by the new model and the least square estimate and ridge estimate. The main research method is to combine qualitative analysis and quantitative analysis. The deviation between estimated values and the true value and the estimated residuals fluctuation of different methods are used for qualitative analysis. The root mean square error (RMSE) is used for quantitative analysis. The results of experiment show that the model has the smallest residual error and the minimum root mean square error. The new estimate model has effectiveness and high precision. The genuine data of Jining area in unwrapping experiments are used and the comparison with other classical unwrapping algorithms is made, so better results in precision aspects can be achieved through the proposed algorithm.
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Udompant, Kannapat, Ricardo Ospina, Yong-Joo Kim, and Noboru Noguchi. "Utilization of Quasi-Zenith Satellite System for Navigation of a Robot Combine Harvester." Agronomy 11, no. 3 (March 5, 2021): 483. http://dx.doi.org/10.3390/agronomy11030483.
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The purpose of this study is to evaluate the performance of a robot combine harvester by comparing the Centimeter Level Augmentation Service (CLAS) and the Multi-Global Navigation Satellite System (GNSS) Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) from the Quasi-Zenith Satellite System (QZSS) by using the Real Time Kinematic (RTK) positioning technique as a reference. The first section of this study evaluates the availability and the precision under static conditions by measuring the activation time, the reconnection time, and obtaining a Twice Distance Root Mean Square (2DRMS) of 0.04 m and 0.10 m, a Circular Error Probability (CEP) of 0.03 m and 0.08 m, and a Root Mean Square Error (RMSE) of 0.57 m and 0.54 m for the CLAS and MADOCA, respectively. The second section evaluates the accuracy under dynamic conditions by using a GNSS navigation-based combine harvester running in an experimental field. The results show that the RMSE of the lateral deviation is between 0.04 m and 0.69 m for MADOCA and between 0.03 m and 0.31 m for CLAS; which suggest that the CLAS positioning augmentation system can be utilized for the robot combine harvester if the user considers these accuracy and dynamic characteristics.
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DWIVEDI, VIJAY KUMAR, PRIYANKA SHARMA, and DIPAK KUMAR. "MODELING AND MULTI-OBJECTIVE OPTIMIZATION FOR APSed THERMAL BARRIER COATINGS ON IN718." Surface Review and Letters 28, no. 06 (March 13, 2021): 2150040. http://dx.doi.org/10.1142/s0218625x21500402.
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Experimental results based on [Formula: see text] orthogonal arrays (OAs) of Air Plasma Spraying (APS) parameters for deposition of thermal barrier coatings (TBCs) onto bond coated Inconel 718 (IN718) superalloys were used for mathematical models, using artificial neural network (ANN), for hardness and roughness. Thereafter, it was optimized from ANN-coupling Genetic Algorithm (GA). The developed ANN-based models showed optimal level of responses as 6.61 and 1209.8, for roughness and hardness, respectively. The average percentage prediction error (APPE)/mean error percentage (MEP) and root mean square error (RMSE) for the roughness were found as 0.07834% and 0.00754%, respectively, while these APPE/MEP and RMSE for hardness were found as 0.456% and 0.00765%, respectively.
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Maiwald, Christian, Stefan Grau, Inga Krauss, Marlene Mauch, Detlef Axmann, and Thomas Horstmann. "Reproducibility of Plantar Pressure Distribution Data in Barefoot Running." Journal of Applied Biomechanics 24, no. 1 (February 2008): 14–23. http://dx.doi.org/10.1123/jab.24.1.14.
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The aim of this study was to provide detailed information on rationales, calculations, and results of common methods used to quantify reproducibility in plantar pressure variables. Recreational runners (N = 95) performed multiple barefoot running trials in a laboratory setup, and pressure variables were analyzed in nine distinct subareas of the foot. Reproducibility was assessed by calculating intraclass correlation coefficients (ICC) and the root mean square error (RMSE). Intraclass correlation coefficients ranged from 0.58 to 0.99, depending on the respective variable and type of ICC. Root mean square errors ranged between 2.3 and 3.1% for relative force–time integrals, between 0.07 and 0.23 for maximum force (Fmax), and between 107 and 278 kPa for maximum pressure (Pmax), depending on the subarea of the foot. Force–time integral variables demonstrated the best within-subject reproducibility. Rear-foot data suffered from slightly increased measurement error and reduced reproducibility compared with the forefoot.
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Bai, Yan, Hai Yan Gong, Chun Fang Zuo, Jing Wei Lei, Xiao Yan Duan, and Cai Xia Xie. "Quantitative Model for Diosgenin in Dioscorea zingiberensis C.H.Wright by NIRS Combined with TQ Software." Advanced Materials Research 807-809 (September 2013): 2085–91. http://dx.doi.org/10.4028/www.scientific.net/amr.807-809.2085.
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To determine the Diosgenin in Dioscorea zingiberensis C.H.Wright by near-infraed spectroscopy (NIRS) combined with TQ software. The near-infrared sprectra and HPLC values of the Diosgenin in Dioscorea zingiberensis C.H.Wright from different areas were collected, and the quantitative calibration model was established with TQ software. And then the prediction samples were anylized by the model. The correlation coefficients (R2), the root-mean-square error of calibration (RMSEC) and the root-mean-square error of cross-validation (RMSECV) of the quantitative calibration model for diosgenin were 0.96459, 0.0999 and 0.30041 respectively; the correlation coefficients of prediction (r2) and the root-mean-square error of prediction (RMSEP) were 0.9634 and 0.128. The method is fast, convenient, non-polluted and accurate. The correction model could be used to predict the diosgenin in Dioscorea zingiberensis C.H.Wright.
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Omoze, E. L., and F. O. Edeko. "Statistical tuning of cost 231 Hata model in deployed 1800mhz GSM networks for a rural environment." Nigerian Journal of Technology 39, no. 4 (March 24, 2021): 1216–22. http://dx.doi.org/10.4314/njt.v39i4.30.
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Radio propagation planning requires the use of propagation models in planning cell size as well as frequency assignment. This paper presents a comparative study of path loss predicted using COST 231 Hata model and ECC-33 model on received signal strength data collected from three deployed GSM networks at 1800MHz in Nigerian Institute for Oil Palm Research environment (NIFOR), Edo State, Nigeria. Based on the Mean Prediction Error (MPE) and Root Mean Square Error (RMSE) values obtained from the comparison, the COST 231 Hata model was tuned using the least square approach. The result obtained after tuning shows that for Network A; MPE andRMSE values reduces to 1.17 dB and 5.5dB. For Network B, MPE and RMSE values reduces to 2.26 dB and 7.16dB. While, for Network C; MPE and RMSE values reduces to 6.21 dB and 10.78dB. The results obtained show that the tuned COST 231 Hata model can be used for radio planning in the study environment as well as other environment with similar terrain profile.
Keywords: Propagation model, Path loss, COST 231 Hata model, ECC-33 model, Least square tuning approach, MPE and RMSE