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Journal articles on the topic 'Root mean square error (RMSE)'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 t

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2

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 (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 MA

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3

Hodson, Timothy O. "Root-mean-square error (RMSE) or mean absolute error (MAE): when to use them or not." Geoscientific Model Development 15, no. 14 (2022): 5481–87. http://dx.doi.org/10.5194/gmd-15-5481-2022.

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Abstract. The root-mean-squared error (RMSE) and mean absolute error (MAE) are widely used metrics for evaluating models. Yet, there remains enduring confusion over their use, such that a standard practice is to present both, leaving it to the reader to decide which is more relevant. In a recent reprise to the 200-year debate over their use, Willmott and Matsuura (2005) and Chai and Draxler (2014) give arguments for favoring one metric or the other. However, this comparison can present a false dichotomy. Neither metric is inherently better: RMSE is optimal for normal (Gaussian) errors, and MAE

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Robeson, Scott M., and Cort J. Willmott. "Decomposition of the mean absolute error (MAE) into systematic and unsystematic components." PLOS ONE 18, no. 2 (2023): e0279774. http://dx.doi.org/10.1371/journal.pone.0279774.

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When evaluating the performance of quantitative models, dimensioned errors often are characterized by sums-of-squares measures such as the mean squared error (MSE) or its square root, the root mean squared error (RMSE). In terms of quantifying average error, however, absolute-value-based measures such as the mean absolute error (MAE) are more interpretable than MSE or RMSE. Part of that historical preference for sums-of-squares measures is that they are mathematically amenable to decomposition and one can then form ratios, such as those based on separating MSE into its systematic and unsystema

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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 (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 va

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Ivan, Eliansion, and Hindriyanto Dwi Purnomo. "FORECASTING PRICES OF FERTILIZER RAW MATERIALS USING LONG SHORT TERM MEMORY." Jurnal Teknik Informatika (Jutif) 3, no. 6 (2022): 1663–73. http://dx.doi.org/10.20884/1.jutif.2022.3.6.433.

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This study uses long short term memory (LSTM) modeling to predict time series data on the price of fertilizer raw materials, namely prilled urea, granular urea, ammonium sulphate((NH4)2SO4), ammonia (NH3), diammonium phosphate((NH4)2HPO4 ), phosphoric acid (H3PO4), phosphate rock (P2O5), NPK 16-16-16, potash, sulfur, and sulfuric acid (H2SO4). Predictions are made based on data that existed in the past using the long short term memory method, which is a derivative of the recurrent neural network. Carry out the evaluation process by looking at the root mean square error (RMSE) and mean absolute

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7

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 (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

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8

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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9

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 (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 pra

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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 (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 squar

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11

Kaliappan, S., R. Saravanakumar, Alagar Karthick, et al. "Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System." International Journal of Photoenergy 2021 (December 26, 2021): 1–8. http://dx.doi.org/10.1155/2021/7894849.

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The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. In this work, the artificial neural network (ANN) is used to predict the performance of this system by optimizing the important parameter of the feature selection. The Elman neural network (EN) algorithm, feed forward neural network (FN), and generalized regression neural network model (GRN) are investigat

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12

Andiani, Andiani, Yoel Simanjuntak, and Ninuk Wiliani. "Performance Assessment of ARIMA and LSTM Models in Prediction Using Root Mean Square Error (RMSE)." Journal of Applied Research In Computer Science and Information Systems 2, no. 1 (2024): 149–58. https://doi.org/10.61098/jarcis.v2i1.181.

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Cryptocurrency is a digital financial asset that serves as a medium of exchange, with its ownership guaranteed using decentralized cryptographic technology, and it has become a growing investment tool. Solana is one of the highly sought-after Cryptocurrencies by investors. The market price of Solana exhibits highly volatile movements, which are considered risky for investment purposes, as it offers both high potential profits and losses. In this regard, time series data prediction models are used to analyze and forecast the price movements of Solana. By comparing the performance of ARIMA and L

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13

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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14

Hendarwati, Emy Khairil, Piter Lepong, and Suyitno Suyitno. "Pemilihan Semivariogram Terbaik Berdasarkan Root Mean Square Error (RMSE) pada Data Spasial Eksplorasi Emas Awak Mas." GEOSAINS KUTAI BASIN 6, no. 1 (2023): 47. http://dx.doi.org/10.30872/geofisunmul.v6i1.1072.

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Semivariogram merupakan perangkat dasar geostatistik yang digunakan untuk memvisualisasi, memodelkan, dan menghitung autokorelasi spasial dari antar data dalam suatu variabel. Semivariogram dibedakan menjadi dua, yaitu semivariogram eksperimental dan semivariogram teoritis. Terdapat tiga jenis model semivariogram teoritis, yaitu model spherical, model eksponensial, dan model gaussian. Penelitian ini bertujuan untuk menentukan model semivariogram terbaik berdasarkan nilai RMSE terkecil. Data penelitian ini adalah data sekunder eksplorasi emas yang terdiri dari data drillhole sebanyak 101 data.

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15

Purva, Sharma, Saini Deepak, and Saxena Akash. "Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN." Bulletin of Electrical Engineering and Informatics 5, no. 3 (2016): 284–95. https://doi.org/10.11591/eei.v5i3.537.

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Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observe

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16

Durmus, Fatih, and Serap Karagol. "Lithium-Ion Battery Capacity Prediction with GA-Optimized CNN, RNN, and BP." Applied Sciences 14, no. 13 (2024): 5662. http://dx.doi.org/10.3390/app14135662.

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Over the last 20 years, lithium-ion batteries have become widely used in many fields due to their advantages such as ease of use and low cost. However, there are concerns about the lifetime and reliability of these batteries. These concerns can be addressed by obtaining accurate capacity and health information. This paper proposes a method to predict the capacity of lithium-ion batteries with high accuracy. Four key features were extracted from current and voltage data obtained during charge and discharge cycles. To enhance prediction accuracy, the Pearson correlation coefficient between these

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Yogafanny, Ekha, and Djoko Legono. "A COMPARATIVE STUDY OF MISSING RAINFALL DATA ANALYSIS USING THE METHODS OF INVERSED SQUARE DISTANCE AND ARITHMETIC MEAN." ASEAN Engineering Journal 12, no. 2 (2022): 69–74. http://dx.doi.org/10.11113/aej.v12.16974.

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In water resources planning and management, it is essential to have reliable rainfall data. In many cases, rainfall data under the guardian national/ local institution are incomplete. Some data are missing, both monthly and annually. The missing data may persist due to neither damage nor human error. This study aims to estimate the missing rainfall data using two methods, i.e., the inverse square distance and the arithmetic mean methods. The study compared the two mentioned methods using root mean square error (RMSE) and mean absolute error (MAE) and to determine the consistency of rainfall da

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18

Kamaruddin, Nor Kamariah, Muhammad Ammar Shafi, Gusman Nawanir, Nur Azia Hazida Mohamad Azmi, Aliya Syaffa Zakaria, and Zulfana Lidinillah. "Performance of Linear Programming Asymmetric Parameter Fuzzy Modelling Based on Statistical Error Measurement." Journal of Advanced Research Design 130, no. 1 (2025): 126–33. https://doi.org/10.37934/ard.130.1.126133.

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Modelling the relationship between a scalar answer and one or more explanatory factors using a linear technique is known as linear regression. The problem of using linear regression arises with the use of uncertain and imprecise data. Since the fuzzy set theory’s concept can deal with data not to a precise point value (uncertainty data), this study applied the fuzzy linear regression with asymmetric parameter (FLRWAP) to 1000 row of simulation data. Five independent variables with different combination of variable types were considered. Other than that, the performance of the models such as th

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19

Herranz-Matey, Ivan, and Luis Ruiz-Garcia. "New Agricultural Tractor Manufacturer’s Suggested Retail Price (MSRP) Model in Europe." Agriculture 14, no. 3 (2024): 342. http://dx.doi.org/10.3390/agriculture14030342.

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Research investigating models for assessing new tractor pricing is notably scarce, despite its fundamental importance in conducting comprehensive cost analyses. This study aims to identify a model that is both user-friendly and robust, evaluating both parametric and Machine Learning-optimized non-parametric models. Among parametric models, the second-order polynomial model demonstrated superior performance in terms of R-squared (R2) of 0.97469 and a Root Mean Square Error (RMSE) of 15,633. Conversely, Machine Learning-optimized Gaussian Processes Regressions exhibited the most favorable overal

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20

Usman, U., N. Garba, A.B Zoramawa, and H. Usman. "Assessing the Performance of Ordinary Least Square and Kernel Regression." Continental J. Applied Sciences 15, no. 1 (2020): 14–23. https://doi.org/10.5281/zenodo.3764305.

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The assessment of Ordinary Least Squares (OLS) and kernel regression on their predictive performance was studied. We used simulated data to assess the performance of estimators using small and large sample. However, the mean square error (MSE) and root mean square error (RMSE) was used to find out the most efficient among the estimated models. The results show that, when  the ordinary least square is more efficient than the kernel regression due to having the least MSE and RMSE in both distributions. Whereas for  the ordinary least square and the kernel regression have the same perfo

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Yunkanabilla, Syalsia Fatiha, Ahmad Faisol, and Franciscus Xaverius Ariwibisono. "SISTEM PERAMALAN STOK PENJUALAN OBAT PERTANIAN BERBASIS WEB DENGAN PENDEKATAN REGRESI LINIER." Jurnal Informatika Teknologi dan Sains (Jinteks) 7, no. 1 (2025): 63–71. https://doi.org/10.51401/jinteks.v7i1.5063.

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Toko obat pertanian menduduki peran penting terhadap sektor pertanian dalam mendukung kebutuhan obat pertanian. Saat ini usaha Toko Pertanian Borneo masih belum memiliki sistem untuk memprediksi stok obat pertanian. Sehingga toko pertanian saat ini menghadapi kondisi peningkatan permintaan obat yang belum dapat dipenuhi oleh toko pertanian. Penelitian yang akan dilakukan ini merupakan penelitian dengan menerapkan metode Regresi Linier Berganda untuk dapat memprediksi stok obat pertanian dengan RMSE (Root Mean Square Error) berdasarkan parameter yang digunakan dalam proses perhitungan adalah mu

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Nurhaida, Ida, Mochamad Sobiri, and Safitri Jaya. "Optimasi Prediksi Cryptocurrency Menggunakan Pendekatan Deep Learning." JSAI (Journal Scientific and Applied Informatics) 6, no. 2 (2023): 197–204. http://dx.doi.org/10.36085/jsai.v6i2.5288.

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Cryptocurrency adalah mata uang digital terdesentralisasi yang diatur oleh pemerintah pusat. Karena cryptocurrency sangat fluktuatif, analisis diperlukan sebelum menggunakan cryptocurrency untuk meminimalkan kerugian. Penelitian ini melakukan perbandingan antara model Long Short Term Memory (LSTM) dan algoritma optimasi seperti Adam dan Root Mean Square Propagation (RMSProp) untuk melakukan prediksi terhadap nilai cryptocurrency. Metode LSTM dioptimasi menggunakan Adam Optimizer dan dievaluasi berdasarkan Root Mean Square Error (RMSE). Dengan demikian diperoleh prediksi nilai RMSE sebesar 0.08

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23

Patel, Satish A., and Dharmendrasinh A. Baria. "SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION OF PHENYLEPHRINE HYDROCHLORIDE AND NAPHAZOLINE HYDROCHLORIDE IN EYE DROPS BY CHEMOMETRIC TECHNIQUES AND ARTIFICIAL NEURAL NETWORK." INDIAN DRUGS 58, no. 09 (2021): 38–46. http://dx.doi.org/10.53879/id.58.09.11710.

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Three multivariate calibration-prediction techniques, partial least squares (PLS), principal component regression (PCR) and artifi cial neural networks (ANN), have been applied without separation in the spectrophotometric multi-component analysis of phenylephrine hydrochloride and naphazoline hydrochloride. A set of 25 synthetic mixtures of phenylephrine hydrochloride and naphazoline hydrochloride has been evaluated to determine the predictability of PLS, PCR and ANN. The absorbance data matrix was obtained by measuring zero-order absorbances between 230-300 nm at intervals of 3 nm. The suitab

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Fahri, Amin, and Yudi Ramdhani. "Visualisasi Data dan Penerapan Machine Learning Menggunakan Decision Tree Untuk Keputusan Layanan Kesehatan COVID-19." Jurnal Tekno Kompak 17, no. 2 (2023): 50. http://dx.doi.org/10.33365/jtk.v17i2.2438.

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Pada Desember 2019, virus corona baru yang sekarang dinamai SARS-CoV-2, menyebabkan serangkaian penyakit pernapasan atipikal akut di Wuhan, Provinsi Hubei, China. Penyakit yang disebabkan oleh virus ini disebut COVID-19. Virus ini dapat menular antar manusia dan telah menyebabkan pandemi di seluruh dunia. Virus yang mendasari penyakit COVID-19, SARS-CoV-2, telah menyebabkan lebih dari 120 juta kasus yang dikonfirmasi dan 1,5 juta kematian sejak April 2022. Penelitian ini menggunakan algoritma Decision Tree untuk memprediksi COVID-19 dengan validasi parameter Cross Validation, Split Validation.

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Gede Adi, Wiguna Sudiartha, Oginawati Katharina, Sofyan Asep, et al. "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 e

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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 (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

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Aviantoro, Kevin, and Yulia Darnita. "IMPLEMENTASI WIENER, CONTRAST STRETCHING, SHARPENING FILTER PADA CITRA SEMANGKA MENGGUNAKAN MSE,RMSE, DAN PSNR." Djtechno: Jurnal Teknologi Informasi 5, no. 2 (2024): 195–205. http://dx.doi.org/10.46576/djtechno.v5i2.4613.

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Penelitian ini mengkaji tiga metode pemrosesan citra Wiener Filter, Contrast Stretching, dan Sharpening Filter untuk meningkatkan kualitas citra semangka. Evaluasi kinerja dilakukan menggunakan Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), dan Root Mean Square Error (RMSE). Wiener Filter efektif mengurangi noise, Contrast Stretching meningkatkan kontras, dan Sharpening Filter menonjolkan detail. MSE mengukur rata-rata kesalahan kuadrat antara citra asli dan citra yang diproses, dengan nilai < 1 menunjukkan kualitas bagus dan > 1 kualitas kurang bagus. PSNR mengukur rasio s

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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 (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

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Anderson, Kelly J., and John H. Kalivas. "Assessment of Pareto Calibration, Stability, and Wavelength Selection." Applied Spectroscopy 57, no. 3 (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 ar

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Zong, Siyu, Wei Li, Dawen Sun, Zhuoda Jia, and Zhengwei Yue. "Shoulder–Elbow Joint Angle Prediction Using COANN with Multi-Source Information Integration." Applied Sciences 15, no. 10 (2025): 5671. https://doi.org/10.3390/app15105671.

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To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. Experimental results demonstrate that the COANN achieves significant performance improvements: compared with RBF neural networks, it reduces the r

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Jadhav, Ashish Madhukar, Omkar Jadhav, and Poonam Ranpise. "Comparative Analysis of Predictive Models for Temperature Decay in Air Conditioned Space Influenced by Human Occupancy and Humidity." Journal of Scientific Research and Reports 31, no. 6 (2025): 853–69. https://doi.org/10.9734/jsrr/2025/v31i63179.

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Accurate prediction of indoor temperature is crucial for optimizing energy use and ensuring thermal comfort in air-conditioned environments. The study presents an empirical approach to model the cooling behaviour of a controlled room under varying conditions of air conditioner (AC) setpoint, occupancy, and humidity. Three predictive models linear, exponential (Newtonian cooling), and empirical were developed from the experimental data collected for the time taken for every 0.5 deg. C drop in room temperature. The empirical model, which incorporates humidity, occupancy, and room volume, demonst

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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 (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

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Ahmad, Ayaz, Furqan Farooq, Pawel Niewiadomski, et al. "Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm." Materials 14, no. 4 (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 (R

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Thomas, Oka Pratama, Sunarno Sunarno, Budhie Wijatna Agus, and Haryono Eko. "Grindulu fault cloud radon data for earthquake magnitude prediction using machine learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4572–82. https://doi.org/10.11591/ijai.v13.i4.pp4572-4582.

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The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grindulu fault, the research employs random forest (RF), extreme gradient boosting (XGB), neural network (NN), AdaBoost (AB), and support vector machine (SVM) methods. The study aims to refine earthquake magnitude prediction, utilizing real-time radon gas concentration measurements, crucial for disaster preparedness. The evaluation involve

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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 (2016): 182–200. http://dx.doi.org/10.53543/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 te

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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 (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 te

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Irhuma, Mohamed, Ahmad Alzubi, Tolga Öz, and Kolawole Iyiola. "Migrative armadillo optimization enabled a one-dimensional quantum convolutional neural network for supply chain demand forecasting." PLOS ONE 20, no. 3 (2025): e0318851. https://doi.org/10.1371/journal.pone.0318851.

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Demand forecasting is a quite challenging task, which is sensitive to several factors such as endogenous and exogenous parameters. In the context of supply chain management, demand forecasting aids to optimize the resources effectively. In recent years, numerous methods were developed for Supply Chain (SC) demand forecasting, which posed several limitations related to inadequate handling of dynamic time series patterns and data requirement problems. Thus, this research proposes a Migrative Armadillo Optimization-enabled one-dimensional Quantum convolutional neural network (MiA + 1D-QNN) for ef

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Ikram, Rana Muhammad Adnan, Xinyi Cao, Kulwinder Singh Parmar, Ozgur Kisi, Shamsuddin Shahid, and Mohammad Zounemat-Kermani. "Modeling Significant Wave Heights for Multiple Time Horizons Using Metaheuristic Regression Methods." Mathematics 11, no. 14 (2023): 3141. http://dx.doi.org/10.3390/math11143141.

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The study examines the applicability of six metaheuristic regression techniques—M5 model tree (M5RT), multivariate adaptive regression spline (MARS), principal component regression (PCR), random forest (RF), partial least square regression (PLSR) and Gaussian process regression (GPR)—for predicting short-term significant wave heights from one hour to one day ahead. Hourly data from two stations, Townsville and Brisbane Buoys, Queensland, Australia, and historical values were used as model inputs for the predictions. The methods were assessed based on root mean square error, mean absolute error

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พงษ์พานิช, บำรุงพงษ์, ศุภัคกุณ ชัยฤทธิ์, ทักษิณา สิทธิผล та วาสนา สุวรรณวิจิตร. "การพยากรณ์ปริมาณการส่งออกทุเรียนสดของไทยด้วยวิธีบอกซ์-เจนกินส์และวิธีปรับเรียบด้วยเส้นโค้งเลขชี้กำลังของวินเทอร์แบบบวก". Economics and Business Administration Journal Thaksin University 15, № 3 (2023): 1–16. http://dx.doi.org/10.55164/ecbajournal.v15i3.263235.

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การศึกษาครั้งนี้มีวัตถุประสงค์เพื่อสร้างและคัดเลือกตัวแบบพยากรณ์ที่เหมาะสมและเปรียบเทียบวิธีการพยากรณ์ปริมาณการส่งออกทุเรียนสดของไทย 2 วิธี ได้แก่ วิธีบอกซ์–เจนกินส์และวิธีปรับเรียบด้วยเส้นโค้งเลขชี้กำลังของวินเทอร์แบบบวก สำหรับการตรวจสอบความแม่นยำของการพยากรณ์พิจารณาจากค่าเฉลี่ยเปอร์เซ็นต์ความคลาดเคลื่อนสัมบูรณ์ (Mean Absolute Percentage Error: MAPE) และค่ารากที่สองของความคลาดเคลื่อนกำลังสอง (Root Mean Square Error: RMSE) โดยใช้ข้อมูลในการวิเคราะห์จากเว็บไซต์ของสำนักงานเศรษฐกิจการเกษตร ตั้งแต่เดือนมกราคม พ.ศ. 2554 ถึงเดือนธันวาคม พ.ศ. 2564 จำนวน 132 เดือน แบ่งข้อมูลออกเป็น 2 ชุด ชุดที่ 1 ตั้ง

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SETIYA, PARUL, AJEET SINGH NAIN, and ANURAG SATPATHI. "Comparative analysis of SMLR, ANN, Elastic net and LASSO based models for rice crop yield prediction in Uttarakhand." MAUSAM 75, no. 1 (2023): 191–96. http://dx.doi.org/10.54302/mausam.v75i1.3576.

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The study was aimed to develop the yield forecast model for rice crop yield. Four different techniques i.e. Stepwise Multiple Linear Regression (SMLR), Artificial Neural Network (ANN), Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (ELNET)were used to build the prediction models. Dataset of meteorological data and crop yield data of 15 years have been used to develop the forecast models. The developed models were also validated on the dataset of three years. The assessment of the developed models wasdone by using root mean square error (RMSE),normalized root mean squar

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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 (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 oxy

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Pratama, Thomas Oka, Sunarno Sunarno, Agus Budhie Wijatna, and Eko Haryono. "Cloud radon data for earthquake magnitude prediction using machine learning." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 4 (2024): 4572. http://dx.doi.org/10.11591/ijai.v13.i4.pp4572-4582.

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<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evalua

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Saglam, Mustafa, Catalina Spataru, and Omer Ali Karaman. "Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms." Energies 16, no. 11 (2023): 4499. http://dx.doi.org/10.3390/en16114499.

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Medium Neural Networks (MNN), Whale Optimization Algorithm (WAO), and Support Vector Machine (SVM) methods are frequently used in the literature for estimating electricity demand. The objective of this study was to make an estimation of the electricity demand for Turkey’s mainland with the use of mixed methods of MNN, WAO, and SVM. Imports, exports, gross domestic product (GDP), and population data are used based on input data from 1980 to 2019 for mainland Turkey, and the electricity demands up to 2040 are forecasted as an output value. The performance of methods was analyzed using statistica

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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 (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 neu

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Carlos, Hernandez, Lagos Dafne, Leal Paola, and Castillo Jaime. "Emergency patient forecasting with models based on support vector machines." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 3 (2024): 3129–40. https://doi.org/10.11591/ijai.v13.i3.pp3129-3140.

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Understanding the dynamic nature of the influx of patients is crucial for efficiently managing supplies, medical personnel, and infrastructure in an emergency room (ER). While overestimation can lead to resource wastage, underestimation can result in shortages and compromised service quality. This study addresses emergency patient forecast by means of implementing support vector machine (SVM) algorithms. Along four phases (analysis, design, development, and validation), more than 50,000 ER records were preprocessed and analyzed. Traditional error metrics such as mean absolute error (MAE), mean

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Zamzami, Balqis Dwian Fitri, Aisyah Tiara Pratiwi, Della Septiani, et al. "Algoritma Alternating Least Squares Untuk Mesin Rekomendasi Film." PROSIDING SEMINAR NASIONAL SAINS DATA 4, no. 1 (2024): 341–50. https://doi.org/10.33005/senada.v4i1.210.

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Abstract: The entertainment world is inseparable from the rapidly growing movie industry and is accompanied by huge data growth. The rapid growth of data has brought about a new era of information. These data are utilized to build innovative, efficient and more effective systems. This research implements a movie recommendation system using the Alternating Least Squares (ALS) algorithm from Apache Spark MLlib with the MovieLens 25M dataset. A collaborative filtering approach with matrix factorization is used to model user preferences and movie characteristics. The evaluation is done by calculat

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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 (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 bia

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Ahmed, Samir Badawi, Hajar Yusoff Siti, Mohammed Zyoud Alhareth, et al. "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 System (IJPEDS) 12, no. 2 (2021): 1114–30. https://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 ‘k’ and the Weibull scale factor ‘c’ were adopted. These suitability values were calculated using the following popular methods: method of moments (MM), standard deviation method (STDM), empiri

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Sudriyanto, Sudriyanto. "Optimizing Neural Networks Using Particle Swarm Optimization (PSO) Algorithm for Hypertension Disease Prediction." JEECOM Journal of Electrical Engineering and Computer 5, no. 2 (2023): 278–84. http://dx.doi.org/10.33650/jeecom.v5i2.6759.

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Penyakit hipertensi atau tekanan darah tinggi merupakan masalah kesehatan yang signifikan secara global. Prediksi yang akurat tentang risiko hipertensi dapat membantu dalam pencegahan, diagnosa, dan pengobatan dini. Dalam penelitian ini, kami mengusulkan penggunaan algoritma Particle Swarm Optimization (PSO) untuk mengoptimalkan jaringan saraf dalam prediksi penyakit hipertensi. Metode ini menggabungkan keunggulan jaringan saraf dalam pemodelan yang kompleks dengan kemampuan PSO dalam mencari solusi optimal. Proses optimisasi dilakukan dengan mengatur bobot dan biases dalam jaringan saraf meng

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Katipoğlu, Okan Mert. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques." Sustainability 15, no. 2 (2023): 1109. http://dx.doi.org/10.3390/su15021109.

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The prediction of hydrological droughts is vital for surface and ground waters, reservoir levels, hydroelectric power generation, agricultural production, forest fires, climate change, and the survival of living things. This study aimed to forecast 1-month lead-time hydrological droughts in the Yesilirmak basin. For this purpose, support vector regression, Gaussian process regression, regression tree, and ensemble tree models were used alone and in combination with a discrete wavelet transform. Streamflow drought index values were used to determine hydrological droughts. The data were divided

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