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32 articles for “root mean square (RMS)”
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Rainfall-runoff Modeling using HEC-HMS Hydrologic Model for Guder River Watershed, Blue Nile Basin, Ethiopia
Abstract: Rainfall-runoff modeling is important for a number of hydrologic applications including flood forecasting, water resource planning and management. This study presents the result of a watershed rainfall-runoff modeling for Guder river watershed having area of 6597 km 2 using Hydrologic Engineering Center-Hydrologic Modeling Systems (HEC-HMS). The watershed runoff is varying spatially and temporally due to manmade factors and natural factors on the watershed. These issue needs efficient water resource planning …
Published in Journal of Water Resource Engineering and Management · Vol. 8, Issue 2, 2021 · pp. 62–77 Read article
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Reduction of Ocular Artifacts in Single Channel EEG by EEMD-IMF Thresholding
Abstract: AbstractElectroencephalogram (EEG) is a widely used signal for analyzing the activities of the brain and usually contaminated with artifacts due to the movements of eye, heart, muscles and power line interference. Among these ocular activities creates significant artifacts and makes the analysis difficult. In this paper, ensemble empirical mode decomposition (EEMD) inspired by wavelet thresholding is used for the correction of ocular artifacts (OA) in EEG signals. Unlike the conventional …
Published in Recent Trends in Electronics Communication Systems · Vol. 5, Issue 1, 2018 · pp. 17–25 Read article
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Machine Learning-Based Structure–Property Quantification of Advanced Polymer Composites
Abstract: Advanced polymer composites are widely used in high-performance engineering due to their superior mechanical and multifunctional properties. Accurate structure–property quantification is essential for efficient material design and reducing experimental costs. Existing Machine Learning (ML) approaches often exhibit limited predictive generalization due to inadequate feature discrimination and suboptimal hyperparameter tuning. To address these limitations, the proposed method enhances the ability to capture the complex nonlinear interactions among composite structural descriptors. The …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Physics-Informed Machine Learning and Multiscale Modeling for Structure–Property Quantification of Polymer Composites
Abstract: The growing need for light-weight, high strength, and sustainable polymer composites has led to the development of smart methods that enable accurate structural-property quantification and material design. However, conventional methods have been predominantly data-based, thus ignoring physical constraints as well as multi-scale interactions involving fiber, matrix, interface, and process parameters, leading to lower accuracy and poor robustness and interpretability of the models. In this study, a Cat Swarm Optimization-Tuned Physics-Informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models
Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article
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Financial Forecast: Systematic Analysis Using Principal Component Analysis
Abstract: The most popular technique for data exploration and analysis across all scientific disciplines that can influence the classification model's accuracy is principal component analysis (PCA). In this study, the high dimensionality of the stock market is examined in order to forecast market movements using PCA in conjunction with linear regression. While reducing data redundancy, PCA can aid in enhancing the predictive performance of machine learning techniques. The New York Stock …
Published in E-Commerce for Future & Trends · Vol. 9, Issue 2, 2022 · pp. 18–24 Read article
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Energy and Exergy Analysis of Ammonia Water Operated Vapour Absorption Refrigeration System Using Artificial Neural Network Approach
Abstract: In this study, energy and exergy analysis have been performed of the ammonia-water operated vapour absorption refrigeration (VAR) system. Authors have trained artificial neural network (ANN) for predicting thermodynamic properties (specific enthalpy and specific entropy) of working fluid (ammonia-water). Authors have achieved coefficient of multiple determination (R2) value 0.9999 and the root mean square error (RMSE) value 0.00211 for input data set of saturated liquid and R2 value 0.999999 and …
Published in Journal of Refrigeration, Air conditioning, Heating and ventilation · Vol. 2, Issue 1, 2015 · pp. 7–20 Read article
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Extraction of High Resolution DEM from Cartosat-1 Stereo Imagery using Rational Math Model and Its Accuracy Assessment for a Part of Snow Covered NW-Himalaya
Abstract: This paper reports the experiments carried out for extraction of DEMs from Cartosat-1 stereo imagery using stereo correlation technique for Manali region. The focus of this paper was to assess the accuracy of extracted DEM with respect to DGPS observations, DEM generated from digitized contours of Survey of India (SoI), 1:50,000 scale topographic map(converted to ellipsoidal height using egm2008 geoid model) as well as well-known freely available DEMs ASTER GDEM …
Published in Journal of Remote Sensing & GIS · Vol. 4, Issue 2, 2013 · pp. 23–34 Read article
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Mathematical Modelling of Thin-layer Drying Process of Bamboo (Bambusa bambos) Shoots at Varying Temperature
Abstract: AbstractThe present study was carried out for understanding the drying mechanism in bamboo shoots and thereby helpful for selection and designing of appropriate drying system. Thin-layer drying characteristics of bamboo shoot slices were determined experimentally as a function of temperature using a laboratory tray dryer. Six mathematical models (Henderson and Pabis, Logarithms, Newton, Page, Two term, Wang and Sing) for describing the thin-layer drying behaviour of bamboo shoot slices were …
Published in Research & Reviews : Journal of Botany · Vol. 3, Issue 1, 2014 · pp. 1–9 Read article
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The Milklipidometric Model: A Mathematical Equation-based on Electric Conductivity to Predict Butterfat Concentrations in Bovine Milk
Abstract: The milklipidometric model is a mathematical equation to predict butterfat in raw milk and it is based on electric conductivity principle. The model differs from the existing equations in that it uses variables (electric conductivity, and volume fraction of whole milk ) which can be easily measured and it predicts fat at wide temperature, range of 5–60°C. Accurate results can be obtained even using commercial electrical conductivity meters, hence making …
Published in Research and Reviews : Journal of Dairy Science and Technology · Vol. 8, Issue 1, 2019 · pp. 10–21 Read article
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The Study of Temporal Variations in Ionosonde foF2 and GPS TEC at Multan and Islamabad during Low and High Solar Activity in Solar Cycle 24
Abstract: The study of temporal variations in critical frequency of the F2 layer (foF2) and total electron content (TEC) calculated from satellites of GPS (Global Positioning System) has been conducted at Multan (Geog. Lat: 30.16°N, long: 71.53°E) and Islamabad (33.66°N, 73.23°E) during low solar activity (LSA) year 2009 and relatively high solar activity (HSA) year 2012 of solar cycle (SC) 24. In general, it has been observed that the diurnal variations …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 7, Issue 2, 2018 · pp. 8–17 Read article
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The Comparison in Time Series Forecasting of Air Traffic Data by Autoregressive Integrated Moving Average Model, Radial Basis Function and Elman Recurrent Neural Networks
Abstract: Nowadays, nonlinear time series and artificial neural networks (ANN) models are used for forecasting in the field of business, agriculture and soon. Recent studies have shown, ANN have been successfully used for forecasting of financial and agriculture data series The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. ANN have more advantages that can approximate to …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 3, 2018 · pp. 75–90 Read article