Lowest Root Mean Square Error Research Articles

Deep Learning-Based Multipath DoAs Estimation Method for mmWave Massive MIMO Systems in Low SNR

To realize the direction-of-arrivals (DoAs) estimation without the prior information about the multipath number, a novel method using the deep learning is introduced for the millimeter (mmWave) massive multiple-input and multiple-output (MIMO) systems. In particular, the DoAs estimation is decomposed into three sub-problems, which are solved by the corresponding convolutional neural network (CNN), respectively. The estimation of multipath number is achieved by a multi-label classification model using the proposed CNN-I. Then, the proposed CNN-II is introduced for the DoA estimation of the line-of-sight (LOS) path. Based on the predicted multipath number obtained by the CNN-I, a regression model using the proposed CNN-III is applied for the DoAs estimation of non-line-of-sight (NLOS) paths. The proposed DoAs estimation method is implemented by learning the non-linear relationship between the sample covariance matrix of the received signal and the angles, thus reconstructing the mapping model. A series of results validate the superiority of the proposed DoAs estimation method in low signal-to-noise-ratio (SNR) regimes. The CNN-I is capable of achieving the good multipath number estimation performance across a range of SNRs. The classification model based on the proposed CNN-II and the regression model using the proposed CNN-III achieve the lower DoAs estimation root mean square error (RMSE) compared with some deep learning-based methods, estimation of signal parameters via rotational invariance techniques (ESPRIT) and Root-MUltiple SIgnal Classification (Root-MUSIC) methods. Remarkably, the proposed method using the CNN-II exhibits the good robustness in the DoA estimation of the LOS path. Furthermore, the low DoAs estimation RMSE is also achieved in the uniform planar array.

A Novel MBAS-RF Approach to Predict Mechanical Properties of Geopolymer-Based Compositions.

Using gels to replace a certain amount of cement in concrete is conducive to the green concrete industry, while testing the compressive strength (CS) of geopolymer concrete requires a substantial amount of substantial effort and expense. To solve the above issue, a hybrid machine learning model of a modified beetle antennae search (MBAS) algorithm and random forest (RF) algorithm was developed in this study to model the CS of geopolymer concrete, in which MBAS was employed to adjust the hyperparameters of the RF model. The performance of the MBAS was verified by the relationship between 10-fold cross-validation (10-fold CV) and root mean square error (RMSE) value, and the prediction performance of the MBAS and RF hybrid machine learning model was verified by evaluating the correlation coefficient (R) and RMSE values and comparing with other models. The results show that the MBAS can effectively tune the performance of the RF model; the hybrid machine learning model had high R values (training set R = 0.9162 and test set R = 0.9071) and low RMSE values (training set RMSE = 7.111 and test set RMSE = 7.4345) at the same time, which indicated that the prediction accuracy was high; NaOH molarity was confirmed as the most important parameter regarding the CS of geopolymer concrete, with the importance score of 3.7848, and grade 4/10 mm was confirmed as the least important parameter, with the importance score of 0.5667.

A unique Markov chain Monte Carlo method for forecasting wind power utilizing time series model

Concerns that impair human societies frequently include a heavy dependence on petroleum and coal and emissions of greenhouse gases. Thus, adopting renewable energy sources, such as wind power, has become a practical solution to this problem. Therefore, to carry out the research on wind velocity energy, a time-series structure is necessary. This study uses the Markov chain Monte Carlo approach and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to estimate short-term and long-term sustained winds. The significance of building a wind energy system is initially discussed, after which a wind velocity time-series framework based on a SARIMA is presented, followed by a short-term and Long-term wind speed projection. Furthermore, a methodology utilizing the Markov chain Monte Carlo method (MCMC) is suggested to establish a wind energy time-series analysis. This framework draws a Markov chain for time-series data on wind energy to maintain stochasticity and realize the probability transition matrix. Gibbs sampling is employed as well. The model's forecasting abilities were tested using the original database and various efficiency assessment measures, including Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) with efficiency of 13.09 and 1.03. In this study, a framework with the highest KGE and WI as well as the lowest RMSE and MAE was chosen. The findings demonstrate that the approach used in the operation provides outstanding predictability.

Novel 3D Force Sensors for a Cost-Effective 3D Force Plate for Biomechanical Analysis.

Three-dimensional force plates are important tools for biomechanics discovery and sports performance practice. However, currently, available 3D force plates lack portability and are often cost-prohibitive. To address this, a recently discovered 3D force sensor technology was used in the fabrication of a prototype force plate. Thirteen participants performed bodyweight and weighted lunges and squats on the prototype force plate and a standard 3D force plate positioned in series to compare forces measured by both force plates and validate the technology. For the lunges, there was excellent agreement between the experimental force plate and the standard force plate in the X-, Y-, and Z-axes (r = 0.950-0.999, p < 0.001). For the squats, there was excellent agreement between the force plates in the Z-axis (r = 0.996, p < 0.001). Across axes and movements, root mean square error (RMSE) ranged from 1.17% to 5.36% between force plates. Although the current prototype force plate is limited in sampling rate, the low RMSEs and extremely high agreement in peak forces provide confidence the novel force sensors have utility in constructing cost-effective and versatile use-case 3D force plates.

Renal function estimating equations performance during pregnancy and postpartum.

The objectives of this study were to evaluate the performance of renal function estimating equations compared to measured creatinine clearance (CrCl) during pregnancy and postpartum and to evaluate which body weight (pre-pregnancy weight (PPW), actual body weight (ABW), and ideal body weight (IBW)) provides the best performance. A retrospective study. Collections tookplace in the University of Washington clinical research unit. Women (n = 166) who completed ≥1 pharmacokinetic (PK) study with a 6-24 h measured CrCl during pregnancy and/or ≥3 months postpartum were included. CrCl was estimated utilizing estimated glomerular filtration rate (eGFR) and CrCl equations with common weight descriptors. Analyses included Bland-Altman, relative accuracies within 10% and 25%, and root mean squared error (RMSE). Overall performance was determined by summation of rank for evaluation parameters. During pregnancy, correlations between measured CrCl and estimated CrCl were between 0.5-0.8; equations with slopes closest to one were Modification of Diet in Renal Disease (MDRD2; PPW and ABW) and Cockcroft-Gault (CG) (PPW); and y-intercept closest to zero was Preeclampsia Glomerular Filtration Rate (PGFR). The lowest bias was seen with CG (ABW), and the highest accuracy within 25% was CG (ABW). CG (PPW) had the lowest RMSE. Postpartum, the best correlation was found with MDRD2 (PPW), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI (ABW)), and CKD-EPI 2021 (PPW). For slopes closest to one, MDRD2 (ABW) was best, whereas the equation with y-intercept closest to zero was CKD-EPI (ABW). CG (PPW) had the highest accuracy within 25%, and 100/serum creatinine (SCr) had the lowest bias. Based on overall performance, CG (PPW) was the best followed by CG (ABW) and PGFR during pregnancy and 100/SCr followed by CG (PPW) and CG (ABW) postpartum. The new CKD-EPI 2021 equation did not perform well during pregnancy. When 24-h CrCls are not available during pregnancy, CG (PPW or ABW) performed the best overall, whereas at 3 months postpartum, 100/SCr performed the best overall.

Effects of Climate on Stand-Level Biomass for Larch Plantations in Heilongjiang Province, Northeast China

Climate change affects forest resource availability, growing season length, and thus forest biomass accumulation. However, only a limited number of studies have been conducted on forest biomass management based on climate effects, particularly at the stand-level. Thus, an allometric biomass equation based on conventional and climate-based stand biomass models, was developed and compared for larch trees (Larix spp.). A total of 160 experimental plots of larch plantations have been collected in Heilongjiang Province, Northeast China. In this study, we developed four types of additive model systems for stand-level biomass: two types of the stand-level biomass basic models (M-1 and M-2) with stand variables (stand basal area (BA) and stand mean height (Hm)) as the predictors, and two types of the proposed stand-level biomass climate-based models (M-3 and M-4) with stand variables (BA and Hm) and climatic variables (mean annual temperature (MAT) and annual precipitation (AP)) as the predictors. Accordingly, this study evaluated the effects of climatic variables (MAT and AP) and stand variables (BA and Hm) on the model’s performance. Model fitting and validation results revealed that the climatic variables significantly improved the model performance of the fitted equation by increasing the coefficient of determination (R2) values and reducing the root mean square error (RMSE) values. A higher R2 and a lower RMSE were consistently generated by M-2 and M-4, whereas M-1 and M-3 consistently generated a lower R2 and a higher RMSE. We found that the proposed stand-level biomass climate-based model type 4 (M-4) performed better than the other models and slightly better than in previous studies of climate-sensitive models. This study provided an additional and beneficial method of analyzing climate effects on stand-level biomass estimation.

A global long-term, high-resolution satellite radar backscatter data record (1992–2022+): merging C-band ERS/ASCAT and Ku-band QSCAT

Abstract. Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness and has thus been used in a range of Earth science disciplines. However, there is no single global radar data set that has a relatively long wavelength and a decades-long time span. We here provide the first long-term (since 1992), high-resolution (∼8.9 km instead of the commonly used ∼25 km resolution) monthly satellite radar backscatter data set over global land areas, called the long-term, high-resolution scatterometer (LHScat) data set, by fusing signals from the European Remote Sensing satellite (ERS; 1992–2001; C-band; 5.3 GHz), Quick Scatterometer (QSCAT, 1999–2009; Ku-band; 13.4 GHz), and the Advanced SCATterometer (ASCAT; since 2007; C-band; 5.255 GHz). The 6-year data gap between C-band ERS and ASCAT was filled by modelling a substitute C-band signal during 1999–2009 from Ku-band QSCAT signals and climatic information. To this end, we first rescaled the signals from different sensors, pixel by pixel. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals by modelling the signal differences from climatic variables (i.e. monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, root mean square error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999–2001 and 2007–2009). We obtained high Pearson r values and low RMSE values at both the regional (r≥0.92, RMSE ≤ 0.11 dB, and rRMSE ≤ 0.38) and pixel levels (median r across pixels ≥ 0.64, median RMSE ≤ 0.34 dB, and median rRMSE ≤ 0.88), suggesting high accuracy for the data-merging procedure. The merged radar signals were then validated against the European Space Agency (ESA) ERS-2 data, which provide observations for a subset of global pixels until 2011, even after the failure of on-board gyroscopes in 2001. We found highly concordant monthly dynamics between the merged radar signals and the ESA ERS-2 signals, with regional Pearson r values ranging from 0.79 to 0.98. These results showed that our merged radar data have a consistent C-band signal dynamic. The LHScat data set (https://doi.org/10.6084/m9.figshare.20407857; Tao et al., 2023) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture with a high spatial resolution. The data set will be updated on a regular basis to include the latest images acquired by ASCAT and to include even higher spatial and temporal resolutions.

Intraocular Lens Power Calculations in Keratoconus Eyes Comparing Keratometry, Total Keratometry, and Newer Formulae

To compare the utility of keratometry vs total keratometry (TK) for intraocular lens power calculations in eyes with keratoconus (KCN) using KCN and non-KCN formulae. Retrospective cohort study. This study was conducted at 2 academic centers and included 87 eyes in 67 patients who underwent cataract surgery between 2019 and 2021. Biometry measurements were obtained using a swept-source optical coherence tomography biometer (IOL Master 700). Refractive prediction errors, including root mean square error (RMSE), were calculated for 13 formulae. These included 4 classical formulae (Haigis, Hoffer Q, Holladay 1 [H1], and SRK/T), 5 new formulae (NF) (Barrett Universal II [BU2], Cooke K6, EVO 2.0, Kane, and Pearl-DGS), 3 KCN formulae (BU2 KCN: M-PCA, BU2 KCN: P-PCA, and Kane KCN), and H1 with equivalent keratometry reading values (H1-EKR). Formulae were ranked by RMSE. Friedman analysis of variance with post hoc analysis and H-testing was used for statistical significance testing. KCN formulae had the lowest RMSEs in all eyes, and BU2 KCN:M-PCA performed the best among KCN formulae in all subgroups. In eyes with severe KCN, if TK values are unavailable, the BU2 KCN: P-PCA performed better than the top-ranked non-KCN formula (SRK/T). In eyes with nonsevere KCN, if TK values are unavailable, EVO 2.0 K was statistically superior to the next competitor (Kane K). H1-EKR had the highest RMSE. KCN formulae and TK are useful for intraocular lens power calculations in KCN eyes, especially in eyes with severe KCN. The BU2 KCN: M-PCA using TK values performed best for eyes with all severities of KCN. For eyes with nonsevere KCN, the EVO 2.0 TK or K can also be used.

An interactive web-based solar energy prediction system using machine learning techniques

Solar energy being one of the most inexpensive renewable energy sources, has shown to be a viable alternative to traditional fossil-fuel and wood-based electricity generation. For the purpose of creating a more trustworthy and successful energy planning strategy, accurate projections of sun irradiation, solar energy generation, and revenues are crucial. Hence, in this work we have proposed web-based optimal prediction system that estimates solar radiation based on location and meteorological data using Machine Learning techniques. Furthermore, an interactive dashboard solar digital map has been developed that enables real-time investigation of solar energy consumption, production, solar radiation, and investment potential for a specific county in California. The model's performance has been measured using Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Average Error (MAE), and Mean Absolute Percentage Error (MAPE) scores. Experimental results demonstrate that stacking model outperformed all the models with the lowest RMSE, MSE, and MAE.

CORRELATION TO PREDICT WAX APPEARANCE TEMPERATURE AND CHARACTERISATION OF CRUDE OIL

Wax precipitation in oil pipelines and industrial equipment is a severe concern in the petroleum sector as it cancause well bores to block and reduces the efficiency of oil/gas production and transportation processes. The solid particles (wax) will increase the pressure drop in the tubing and eventually cause plugging. Thus, predicting the Wax Appearance Temperature (WAT) is crucial, the temperature at which the first wax droplets are discovered. Gamma Distribution Function (GDF) parameters were used to develop a characterisation to determine the nature of hydrocarbon mixtures. The parameters of GDF were calculated using the characterisation developed in Python. The oil was characterised by using the calculated GDF parameters until C50+. Based on the estimated value of the parameters of GDF, the type of hydrocarbon mixtures were classified into heavy oil/biodegraded, asphaltenic, paraffinic, waxy, and light oils. The WAT of crude oils was predicted using data analysis techniques of regression analysis. Root Mean Square Error (RMSE) and R-squared were used to evaluate the model. Multiple regression was the best data analysis method to correlate WAT, giving the highest R-squared value with a lower RMSE than simple regression. This study will assist in predicting the nature of the crude oil. Correct prediction of crude oil’s nature will help manage flow assurance.Keywords: gamma distribution function, data analysis, flow assurance, wax precipitation, characterisation

Predicting the performance of radio over free space optics system using machine learning techniques

The efficiency and reliability of radio over free space optical communication may be significantly degraded by different weather conditions, thus leading to decrease and increase of signal to noise ratio (SNR) and bit error rate (BER) of the recovered signal respectively. Therefore, real time optical performance estimation is of utmost importance to enable the awareness of channel conditions and for achieving high quality of service. In this work, a terrestrial radio over free space optics (RoFSO) system has been investigated by employing 32-QAM and 64-QAM OFDM modulation schemes under rain, haze and clear weather conditions. Performance of the system is further estimated by employing artificial neural networks (ANN), k-nearest neighbours (KNN) and decision tree (DT) machine learning (ML) techniques with root mean square error (RMSE) and coefficient of determination (R2) as performance metrices. Atmospheric attenuation and different internal system parameters act as input features with BER and SNR of the received signal as the modelling targets. The best performing model has been found to be by applying an ANN approach with lower RMSE values for SNR and BER as 1.87 and 1.26 respectively. The value of R2 predicted by the model are 0.97139 and 0.97802 for SNR and BER data sets respectively.

Experimental and Artificial Neural Network (ANN) Modeling of Instream Vegetation Hydrodynamic Resistance

This study examines the impact of upstream structures on the bulk drag coefficient of vegetation through experimental means, which has not been previously conducted. An embankment model was placed upstream of the vegetation, both with and without a moat/depression. The results showed that the presence of an upstream structure reduced the bulk drag coefficient of vegetation as the structure shared the drag. When only the embankment was placed upstream, a maximum decrease of 11% in the bulk drag coefficient was observed. However, when both the embankment and moat models were placed upstream, a 20% decrease in the bulk drag coefficient was observed. Regression models and artificial neural network (ANN) models were developed to predict the bulk drag coefficient based on the variables affecting it. Five ANN models with different training functions were compared to find the best possible training function, with performance indicators such as coefficient of determination (R2), root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), sum of square error (SSE), mean absolute error (MAE), and Taylor’s diagrams used to evaluate the model performance. The ANN model with nine neurons in each hidden layer performed the best, achieving the highest R2 and NSE values and the lowest RMSE, SSE, and MAE values. Finally, the comparison between the regression model and the ANN model showed that the best ANN model outperformed the regression models, achieving R2 values of 0.99 and 0.98 for the training and validation subsets, respectively.

Neural network model for predicting the biomethane yield in an anaerobic digester using biomass composition profiles

The production of biomethane or renewable natural gas from anaerobic digestion (AD) of agriculture and agroindustry wastes has gained increasing interest towards decarbonisation of fuels and energy supply. Methane yield is a critical step for assessing biomethane projects. In this work, an artificial neural network (ANN) model was developed to predict the methane yield using a collected dataset of 340 experimental data. The input parameters to the network were biomass composition in terms of total solids (TS), volatile solids (VS), lipids, protein, and lignin. The root mean square error (RMSE) of the model was 0.031 L CH4/g VS, with a mean absolute percentage error of 9.2%. The performance of the ANN models was validated using additional mono- and co-digestion experimental datasets. ANN-prediction power was also compared to the outputs of five multiple linear regression models from the literature. Results clearly demonstrated that, compared to the conventional multiple regression models, the proposed ANN-based model has a superior predictive power with lower RMSE values and lower prediction error in most cases. As such, the model is useful for preliminary stages of process design and evaluation of AD-based bioenergy projects. The model has the advantage of requiring only the analysis for five composition values and it has been implemented in a Python function which can also facilitate its wide application.

Multistream 3-D Convolution Neural Network With Parameter Sharing for Human State Estimation

The large amount of data is one challenge in electroencephalogram (EEG) analysis, in which the channel Two Dimensional (2-D), time One Dimensional (1-D), and spectral (1-D) are generally considered. Convolutional neural networks (CNNs) have drawn much attention for automatic feature learning in various fields. Meanwhile, many studies have demonstrated integration from multiple sources and decisions could boost performance. However, CNN for EEG analysis usually involves millions of parameters, which easily leads to overfitting. A new model of a multistream Three Dimensional (3-D) CNN with parameter sharing is proposed for EEG. Two EEG data sets: 1) the lane-keeping task (LKT) data set and 2) sleep data set are applied. For the LKT data set, the proposed multistream 3-D CNN with parameter sharing model achieves 0.5486 root-mean-square error (RMSE), showing improvement by at least 2.77% compared to the other approaches. In the sleep data set, the error rate of the proposed model was 24.65%, showing at least 10.28% improvement in performance compared to the other methods. The lower RMSE and error rate show that the multistream 3-D CNN with parameter sharing model efficiently extracts significant features from EEG data. Moreover, the sharing mechanism even reduces the risk of overfitting and the number of parameters by comprehending common representations among multiple streams.

A Deep Learning Approach for Dengue Fever Prediction in Malaysia Using LSTM with Spatial Attention

This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.

Optimising the Investor's Portfolio through Modern Portfolio Theory: Empirical Evidence from Pakistan

Diversification is considered a way to lower investment risk by using a variety of investment avenues. The objective of this study is to compare the portfolio performance based on mean-variance optimisation with a naively diversified portfolio for textile spinning stocks in the Pakistan country. The data period of study spans from January 2015 to April 2022. The result shows that portfolios based on naïve diversification strategies outperformed the mean-variance portfolio optimisation in terms of risk and return for the textile sector in Pakistan. Of all the 9 variance-covariance estimators, the portfolio of sample and single index covariance technique is optimal for estimating variance-covariance matrices based on low RMSE (root mean square error) in Pakistan. This study extends the debate on mean-variance portfolio theory and naïve diversification strategy from the developed economy to the emerging equity market of Pakistan. This research contributes by providing the framework to the potential investors regarding their investments in the textile sector in Pakistan.

Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction

The ceramic waste powder (CWP) is generated in the ceramic industry during the cutting and polishing stages. It is harmful to the environment and needs a massive area for disposal. Therefore, an alternative way is required to reduce the environmental pollution and landfill caused by CWP. The aim of the study is to establish an Artificial Intelligence (AI) model for CWP concrete from the experimental results to save time and cost. Advancements in AI have made the estimation of concrete mechanical characteristics possible by employing Machine Learning (ML) approaches. In the current study, 60 concrete mixes with waste CWP are made as a partial replacement of cement by 10% and 20%. The plain concrete's ultrasonic pulse velocity (UPV) is taken as a reference. Furthermore, supervised ML techniques (i.e., Bagging, XG Boost, AdaBoost) and standalone (Decision tree) are employed to foresee the UPV of CWP concrete (CWPC). The prediction model's performance is evaluated using R2, Root Mean Square Error (RMSE) values, and Mean Absolute Error (MAE). The k-fold cross-validation is used to validate the performance of the prediction model. The XG Boost model, with an R2 value of 0.95, performed better compared to Bagging, AdaBoost, and DT models. Among all ensemble and individual models, the XG Boost model performs better with higher R2 and lower RMSE (0.081 km/s) and MAE (0.063 km/s) values. Therefore, the CWPC, as a construction material, would reduce land degradation and water pollution. In addition, applying ML techniques for estimating concrete characteristics would have reduced the consumption of efforts, resources, and time of researchers in the construction sector.

Improving the accuracy of bathymetry using the combined neural network and gravity wavelet decomposition method with altimetry derived gravity data

The wide range of bathymetry models can be estimated using the marine gravity information derived from satellite altimetry. However, due to nonlinear factors influences such as isostasy effects, the bathymetry estimated by gravity anomaly and vertical gravity gradient is not satisfactory. Therefore, to improve the accuracy of bathymetry estimation, a combined neural network and gravity information wavelet decomposition (CNNGWD) method is proposed based on wavelet decomposition and correlation analysis. Next, the bathymetry of the Manila Trench area is estimated using the CNNGWD method and multilayer neural network (MNN) method, respectively. Then, the shipborne sounding data and international bathymetric models such as ETOPO1 and GEBCO_2021 are separately used to evaluate the accuracy of the inversion models. The results show that the root mean square errors (RMSE) of the difference between the bathymetric model one (BM1) estimated by CNNGWD method and the shipborne sounding data is 59.90 m, the accuracy is improved by 12.45%, 64.70% and 28.68% compared with the bathymetric model two (BM2) which estimated by MNN, ETOPO1 and GEBCO, respectively. Finally, by analyzing the bathymetric accuracy shift with depth, the BM1 has lower RMSE at depths ranging from 1000 m to 3000 m. Furthermore, BM1 shows dominance in flat troughs and rugged ridge regions.

Empirical Pathloss Model Analysis of Television White Space in Lagos, Western Nigeria

To conduct feasible unlicensed communications in the television band, radio equipment must first identify transmission possibilities, or the proportion of the permitted spectrum for broadcasting services that is vacant at a given time in a particular location, which is referred to as Television White Space (TVWS). A drive test was conducted to determine the signal strength of three cell towers in three distinct areas of Lagos state (rural, suburban, and urban). The signal power parameters were measured in rural, urban, and suburban parts of Lagos in order to provide empirical values for the parameters that unlicensed radio devices can utilize to distinguish between vacant and occupied television channels in a real-world scenario. This research presents the results of field measurements in the UHF television band (470-860 MHz) conducted in Lagos. This work investigated and compared several propagation models (COST 231 model, Egli model, Okumura-Hata model, and Plain Earth model). The Standard Deviation Error (SDE), Root Mean Square Error (RMSE), and Mean Error (ME) analysis showed that the COST-231 Hata model is the optimal model for calculating path loss based on path loss exponents. This discovery led to the development of an improved model, the Oressuff TV model, which uses COST-231 Hata parameters to predict path loss in the 470–870 MHz spectrum for rural, urban, and suburban locations. From the result of Root Mean Square Error analysis, the proposed model predicted path loss in rural, urban, and suburban stations with low RMSE of 3.06dB, 3.08dB, and 1.19dB respectively. These figures indicate that the model optimization was successful, and that the Oressuff TV proposed model can estimate the path loss incurred by television signals with greater precision. Telecommunications firms may improve their service by utilizing the proposed model.

Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets

Abstract. Snow cover mapping algorithms utilizing multispectral satellite data at various spatial resolutions are available, each treating subpixel variation differently. Past evaluations of snow mapping accuracy typically relied on satellite data collected at a higher spatial resolution than the data in question. However, these optical data cannot characterize snow cover mapping performance under forest canopies or at the meter scale. Here, we use 3 m spatial resolution snow depth maps collected on 116 d by an aerial laser scanner to validate band ratio and spectral-mixture snow cover mapping algorithms. Such a comprehensive evaluation of sub-canopy snow mapping performance has not been undertaken previously. The following standard (produced operationally by an agency) products are evaluated: NASA gap-filled Moderate Resolution Imaging Spectroradiometer (MODIS) MOD10A1F, NASA gap-filled Visible Infrared Imaging Radiometer Suite (VIIRS) VNP10A1F, and United States Geological Survey (USGS) Landsat 8 Level-3 Fractional Snow Covered Area. Two spectral-unmixing approaches are also evaluated: Snow-Covered Area and Grain Size (SCAG) and Snow Property Inversion from Remote Sensing (SPIReS), both of which are gap-filled MODIS products and are also run on Landsat 8. We assess subpixel snow mapping performance while considering the fractional snow-covered area (fSCA), canopy cover, sensor zenith angle, and other variables within six global seasonal snow classes. Metrics are calculated at the pixel and basin scales, including the root-mean-square error (RMSE), bias, and F statistic (a detection measure). The newer MOD10A1F Version 61 and VNP10A1F Version 1 product biases (− 7.1 %, −9.5 %) improve significantly when linear equations developed for older products are applied (2.8 %, −2.7 %) to convert band ratios to fSCA. The F statistics are unchanged (94.4 %, 93.1 %) and the VNP10A1F RMSE improves (18.6 % to 15.7 %), while the MOD10A1F RMSE worsens (12.7 % to 13.7 %). Consistent with previous studies, spectral-unmixing approaches (SCAG, SPIReS) show lower biases (−0.1 %, −0.1 %) and RMSE (12.1 %, 12.0 %), with higher F statistics (95.6 %, 96.1 %) relative to the band ratio approaches for MODIS. Landsat 8 products are all spectral-mixture methods with low biases (−0.4 % to 0.3 %), low RMSE (11.4 % to 15.8 %), and high F statistics (97.3 % to 99.1 %). Spectral-unmixing methods can improve snow cover mapping at the global scale.