Academic literature on the topic 'Root mean square error (RMSE)'
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Journal articles on the topic "Root mean square error (RMSE)"
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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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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.
Dissertations / Theses on the topic "Root mean square error (RMSE)"
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Thomas, Robin Rajan. "Optimisation of adaptive localisation techniques for cognitive radio." Diss., University of Pretoria, 2012. http://hdl.handle.net/2263/27076.
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Spectrum, environment and location awareness are key characteristics of cognitive radio (CR). Knowledge of a user’s location as well as the surrounding environment type may enhance various CR tasks, such as spectrum sensing, dynamic channel allocation and interference management. This dissertation deals with the optimisation of adaptive localisation techniques for CR. The first part entails the development and evaluation of an efficient bandwidth determination (BD) model, which is a key component of the cognitive positioning system. This bandwidth efficiency is achieved using the Cramer-Rao lower bound derivations for a single-input-multiple-output (SIMO) antenna scheme. The performances of the single-input-single-output (SISO) and SIMO BD models are compared using three different generalised environmental models, viz. rural, urban and suburban areas. In the case of all three scenarios, the results reveal a marked improvement in the bandwidth efficiency for a SIMO antenna positioning scheme, especially for the 1×3 urban case, where a 62% root mean square error (RMSE) improvement over the SISO system is observed. The second part of the dissertation involves the presentation of a multiband time-of arrival (TOA) positioning technique for CR. The RMSE positional accuracy is evaluated using a fixed and dynamic bandwidth availability model. In the case of the fixed bandwidth availability model, the multiband TOA positioning model is initially evaluated using the two-step maximum-likelihood (TSML) location estimation algorithm for a scenario where line-of-sight represents the dominant signal path. Thereafter, a more realistic dynamic bandwidth availability model has been proposed, which is based on data obtained from an ultra-high frequency spectrum occupancy measurement campaign. The RMSE performance is then verified using the non-linear least squares, linear least squares and TSML location estimation techniques, using five different bandwidths. The proposed multiband positioning model performs well in poor signal-to-noise ratio conditions (-10 dB to 0 dB) when compared to a single band TOA system. These results indicate the advantage of opportunistic TOA location estimation in a CR environment.Dissertation (MEng)--University of Pretoria, 2012.
Electrical, Electronic and Computer Engineering
unrestricted
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Septarina, Septarina. "Micro-Simulation of the Roundabout at Idrottsparken Using Aimsun : A Case Study of Idrottsparken Roundabout in Norrköping, Sweden." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79964.
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Microscopic traffic simulation is useful tool in analysing traffic and estimating the capacity and level of service of road networks. In this thesis, the four legged Idrottsparken roundabout in the city of Norrkoping in Sweden is analysed by using the microscopic traffic simulation package AIMSUN. For this purpose, data regarding traffic flow counts, travel times and queue lengths were collected for three consecutive weekdays during both the morning and afternoon peak periods. The data were then used in model building for simulation of traffic of the roundabout. The Root Mean Square Error (RMSE) method is used to get the optimal parameter value between queue length and travel time data and validation of travel time data are carried out to obtain the basic model which represents the existing condition of the system. Afterward, the results of the new models were evaluated and compared to the results of a SUMO model for the same scenario model. Based on calibrated and validated model, three alternative scenarios were simulated and analysed to improve efficiency of traffic network in the roundabout. The three scenarios includes: (1) add one free right turn in the north and east sections; (2) add one free right turn in the east and south sections; and (3) addition of one lane in roundabout. The analysis of these scenarios shows that the first and second scenario are only able to reduce the queue length and travel time in two or three legs, while the third scenario is not able to improve the performance of the roundabout. In this research, it can be concluded that the first scenario is considered as the best scenario compared to the second scenario and the third scenario. The comparison between AIMSUN and SUMO for the same scenario shows that the results have no significance differences. In calibration process, to get the optimal parameter values between the model measurements and the field measurements, both of AIMSUN and SUMO uses two significantly influencing parametersfor queue and travel time. AIMSUN package uses parameter of driver reaction time and the maximum acceleration, while SUMO package uses parameter of driver imperfection and also the driver rection time.
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Leksono, Catur Yudo, and Tina Andriyana. "Roundabout Microsimulation using SUMO : A Case Study in Idrottsparken RoundaboutNorrkӧping, Sweden." Thesis, Linköpings universitet, Kommunikations- och transportsystem, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79771.
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Idrottsparken roundabout in Norrkoping is located in the more dense part of the city.Congestion occurs in peak hours causing queue and extended travel time. This thesis aims to provide alternative model to reduce queue and travel time. Types ofobservation data are flow, length of queue, and travel time that are observed during peakhours in the morning and afternoon. Calibration process is done by minimising root meansquare error of queue, travel time, and combination both of them between observation andcalibrated model. SUMO version 0.14.0 is used to perform the microsimulation. There are two proposed alternatives, namely Scenario 1: the additional lane for right turnfrom East leg to North and from North leg to West and Scenario 2: restriction of heavy goodsvehicles passing Kungsgatan which is located in Northern leg of Idrottsparken roundaboutduring peak hours. For Scenario 1, the results from SUMO will be compared with AIMSUNin terms of queue and travel time. The result of microsimulation shows that parameters that have big influence in the calibrationprocess for SUMO are driver imperfection and driver’s reaction time, while for AIMSUN isdriver’s reaction time and maximum acceleration. From analysis found that the model of thecurrent situation at Idrottsparken can be represented by model simulation which usingcombination between root mean square error of queue and travel time in calibration andvalidation process. Moreover, scenario 2 is the best alternative for SUMO because itproduces the decrease of queue and travel time almost in all legs at morning and afternoonpeak hour without accompanied by increase significant value of them in the other legs. Thecomparison between SUMO and AIMSUN shows that, in general, the AIMSUN has higherchanges value in terms of queue and travel time due to the limited precision in SUMO forroundabout modelling.
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Thompson, Grant. "Effects of DEM resolution on GIS-based solar radiation model output: A comparison with the National Solar Radiation Database." University of Cincinnati / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1258663688.
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Šimoník, Petr. "Měřič odstupu signálu od šumu obrazových signálů." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2008. http://www.nusl.cz/ntk/nusl-217681.
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The diplomma thesis is dealing with possibilities of Signal to noise ratio measurement by method, which is based on direct measurement. It is chosen the most suitable method – signal and noise separation to two different parallel signal branches, where is measured signal strength in one branch and root mean square value in the other. The thesis is consisted of a concept of detail block scheme of Signal to noise ratio meter, which was designed in terms of theoretical knowledge. Particular functional blocks were circuit-designed, the active and passive parts were chosen and their function were described. There were made simulation and displayed input and output time flows. There is designed the whole connection of engineered Signal to noise ratio meter in the last part of my thesis. The double-sided board of printed circuit is contained too. It was created simple programme for supervisor micro-processor. Thereby were constructed complete bases for realization.
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Vestin, Albin, and Gustav Strandberg. "Evaluation of Target Tracking Using Multiple Sensors and Non-Causal Algorithms." Thesis, Linköpings universitet, Reglerteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160020.
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Today, the main research field for the automotive industry is to find solutions for active safety. In order to perceive the surrounding environment, tracking nearby traffic objects plays an important role. Validation of the tracking performance is often done in staged traffic scenarios, where additional sensors, mounted on the vehicles, are used to obtain their true positions and velocities. The difficulty of evaluating the tracking performance complicates its development. An alternative approach studied in this thesis, is to record sequences and use non-causal algorithms, such as smoothing, instead of filtering to estimate the true target states. With this method, validation data for online, causal, target tracking algorithms can be obtained for all traffic scenarios without the need of extra sensors. We investigate how non-causal algorithms affects the target tracking performance using multiple sensors and dynamic models of different complexity. This is done to evaluate real-time methods against estimates obtained from non-causal filtering. Two different measurement units, a monocular camera and a LIDAR sensor, and two dynamic models are evaluated and compared using both causal and non-causal methods. The system is tested in two single object scenarios where ground truth is available and in three multi object scenarios without ground truth. Results from the two single object scenarios shows that tracking using only a monocular camera performs poorly since it is unable to measure the distance to objects. Here, a complementary LIDAR sensor improves the tracking performance significantly. The dynamic models are shown to have a small impact on the tracking performance, while the non-causal application gives a distinct improvement when tracking objects at large distances. Since the sequence can be reversed, the non-causal estimates are propagated from more certain states when the target is closer to the ego vehicle. For multiple object tracking, we find that correct associations between measurements and tracks are crucial for improving the tracking performance with non-causal algorithms.
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Molapo, Mojalefa Aubrey. "Employing Bayesian Vector Auto-Regression (BVAR) method as an altenative technique for forecsating tax revenue in South Africa." Diss., 2017. http://hdl.handle.net/10500/25083.
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Simões, Carolina Ramos. "Estimação de funções densidade neutras face ao risco: uma aplicação a opções S&P 500." Master's thesis, 2017. http://hdl.handle.net/10316/86794.
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Dissertação de Mestrado em Métodos Quantitativos em Finanças apresentada à Faculdade de Ciências e TecnologiaCom esta dissertação pretende-se estudar e implementar vários métodos de estimação de funções densidade de probabilidade neutras face ao risco, identificando as perspectivas dos investidores quanto à evolução do mercado. Assim, este trabalho incide sobre preços teóricos de opções europeias, gerados pelo modelo de Black-Scholes, e sobre preços de mercado de opções do mesmo estilo sobre o índice S&P 500, transacionadas no Chicago Board Options Exchange (CBOE) e relativas ao dia 4 de janeiro de 2016 para diferentes maturidades.Inicialmente consideraram-se preços teóricos, gerados pelo modelo de Black-Scholes, e compararam-se, através da análise gráfica e do cálculo do root mean square error (RMSE), com os preços estimados por diferentes métodos. Os métodos utilizados neste estudo foram a mistura de duas distribuições lognormais, a expansão de Edgeworth e o método da volatilidade implícita proposto por Shimko. Seguidamente, estimaram-se os parâmetros relativos aos diferentes métodos e através deles produziram-se as funções densidade de probabilidade neutras face ao risco. Após esta estimação, efetuou-se a análise das perspectivas de mercado, utilizando opções sobre o índice S&P 500 para sete maturidades diferentes. Deste modo, compararam-se os preços das opções de mercado com os preços estimados pelo modelo de Black-Scholes, pela mistura de duas distribuições lognormais, pela expansão de Edgeworth e pelo método da volatilidade implícita proposto por Shimko. De seguida, estimaram-se os parâmetros dos diferentes métodos e, através destes, produziram-se as funções densidade de probabilidade neutras face ao risco. Por fim, analisaram-se algumas variáveis estatísticas das funções densidade de probabilidade obtidas, tais como a média, a variância, o coeficiente de assimetria e o excesso de curtose, de modo a retirar conclusões acerca das expectativas dos investidores em relação à evolução do mercado.
This dissertation intends to study and implement several methods of estimating risk-neutral probability density functions, identifying investors' perspectives on market evolution. Thus, this work focuses on the theoretical prices of European options, generated by the Black-Scholes model, and on market prices of similar options on the S& P 500 index, relative to January 4, 2016 for different maturities, which are traded on the Chicago Board Options Exchange (CBOE).Initially, theoretical prices, generated by the Black-Scholes model, were compared, using the graphical analysis and the calculation of the root mean square error (RMSE), with the prices estimated by different methods. The methods used in this study are a mixture of two lognormal distributions, an Edgeworth expansion and the implicit volatility method proposed by Shimko. Then, the parameters related to different methods were estimated and through them, risk-neutral probability density functions are produced. After this estimation, a market outlook analysis was performed using S&P 500 index options for seven different maturities. The market option prices were then compared with the prices estimated by the Black-Scholes model, the combination of two lognormal distributions, the Edgeworth expansion and the implicit volatility method proposed by Shimko. Afterwards, the parameters of the different methods are estimated and, through these parameters, risk-neutral probability density functions were produced. Finally, some statistical variables of the probability density functions obtained were analyzed, such as the mean, the variance, the asymmetry coefficient and the excess kurtosis, in order to draw conclusions about the investors' expectations regarding market development.
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PÁLENÍKOVÁ, Lenka. "Geodetické práce v investiční výstavbě." Master's thesis, 2009. http://www.nusl.cz/ntk/nusl-51158.
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The accuracy appraisal and its evaluation with what the results of geodetic work are being achieved will be shown in this graduation thesis. As well as the related legislation and economic point of view of the price setting in comparison to other geodetic companies. One of the major surveyed area is testing of the used instruments - confrontation of achieved values with the values stated by manufacturer, accuracy analysis of the used methods and allignment of independent measurements of equal magnitude.
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McCarthy, Christabel. "Investigating the use of dasymetric techniques for assessing employment containment in Melbourne, Australia." Master's thesis, 2012. http://hdl.handle.net/10362/8307.
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Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.This project studies employment containment in Melbourne, Australia. Employment containment is a measure of the proportion of people that work in a location close to their home. Recent urban planning policies in Melbourne have aimed to improve employment containment in the city’s suburbs. While there has been analysis of the rates at which people both live and work within broadly defined ‘local areas’, little work has been done to investigate employment containment using smaller and more uniform catchment areas as the unit of analysis. This research attempts such a finer scale analysis using dasymetric downscaling techniques. A regression modelling approach supported by land use data, alongside a binary dasymetric method, is used to develop fine scale estimates of employment distribution, while binary and populationdensity weighted methods are used to develop a fine scale estimate of working population distribution. For the employment distribution estimate, the Poisson model that distributed employment to employment-related land use classes produced the smallest error. However, the error produced by this model is still high. For the working population distribution estimate, the population-density weighted estimate is the more accurate of the approaches, and overall produced low error. For the employment containment analysis, a number of employment centres were randomly selected and an employment containment catchment has been derived from a 5 km2 commuting distance catchment. Commuting flows from an origin-destination matrix were areaweighted to estimate flows into the employment centre from the 5 km2 catchment. The method is found to be potentially useful; however inspecting the results of this employment containment calculation highlighted flaws in the current estimates that should be addressed before the measures can be used to further analyse employment containment in Melbourne. Improvements to this method would support urban strategic and transport planning analyses at a metropolitan-wide scale.
Book chapters on the topic "Root mean square error (RMSE)"
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Li, Jingyi, and Hong Chen. "Optimization and Prediction of Design Variables Driven by Building Energy Performance—A Case Study of Office Building in Wuhan." In Proceedings of the 2020 DigitalFUTURES, 229–42. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4400-6_22.
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AbstractThis research focuses on the energy performance of office building in Wuhan. The research explored and predicted the optimal solution of design variables by Multi-Island Genetic Algorithm (MIGA) and RBF Artificial neural networks (RBF-ANNs). Research analyzed the cluster centers of design variable by K-means cluster method. In the study, the RBF-ANNs model was established by 1,000 simulation cases. The RMSE (root mean square error) of the RBF-ANNs model in different energy aspects does not exceed 15%. Comparing to the reference case (the largest energy consumption case in the optimization), the 214 elite cases in RBF-ANNs model save at least 37.5% energy. By the cluster centers of the design variables in the elite cases, the study summarized the benchmark of 14 design variables and also suggested a building energy guidance for Wuhan office building design.
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Levinson, Norman. "The Wiener RMS (Root Mean Square) Error Criterion in Filter Design and Prediction." In Selected Papers of Norman Levinson, 163–80. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-5335-8_16.
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Akeh, Ugbah Paul, Steve Woolnough, and Olumide A. Olaniyan. "ECMWF Subseasonal to Seasonal Precipitation Forecast for Use as a Climate Adaptation Tool Over Nigeria." In African Handbook of Climate Change Adaptation, 1613–30. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_97.
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AbstractFarmers in most parts of Africa and Asia still practice subsistence farming which relies minly on seasonal rainfall for Agricultural production. A timely and accurate prediction of the rainfall onset, cessation, expected rainfall amount, and its intra-seasonal variability is very likely to reduce losses and risk of extreme weather as well as maximize agricultural output to ensure food security.Based on this, a study was carried out to evaluate the performance of the European Centre for Medium-range Weather Forecast (ECMWF) numerical Weather Prediction Model and its Subseasonal to Seasonal (S2S) precipitation forecast to ascertain its usefulness as a climate change adaptation tool over Nigeria. Observed daily and monthly CHIRPS reanalysis precipitation amount and the ECMWF subseasonal weekly precipitation forecast data for the period 1995–2015 was used. The forecast and observed precipitation were analyzed from May to September while El Nino and La Nina years were identified using the Oceanic Nino Index. Skill of the forecast was determined from standard metrics: Bias, Root Mean Square Error (RMSE), and Anomaly Correlation Coefficient (ACC).The Bias, RMSE, and ACC scores reveal that the ECMWF model is capable of predicting precipitation over Southern Nigeria, with the best skill at one week lead time and poorest skills at lead time of 4 weeks. Results also show that the model is more reliable during El Nino years than La-Nina. However, some improvement in the model by ECMWF can give better results and make this tool a more dependable tool for disaster risk preparedness, reduction and prevention of possible damages and losses from extreme rainfall during the wet season, thus enhancing climate change adaptation.
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Shekhar, Shashi, and Hui Xiong. "Root-Mean-Square Error." In Encyclopedia of GIS, 979. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1142.
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Pinsky, Mark. "Commentary on: [L 33] The Wiener RMS (Root Mean Square) Error Criterion in Filter Design and Prediction [L 34] A Heuristic Exposition of Wiener’s Mathematical Theory of Prediction and Filtering [L 69] Limiting Theorems for Galton-Watson Branching Process [L 70] Limiting Theorems for Age-Dependent Branching Process [L 81] (with H. P McKean, Jr.) Weighted Trigonometrical Approximation on R1 with Application to the Germ Field of a Stationary Gaussian Noise." In Selected Papers of Norman Levinson, 160–62. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-5335-8_15.
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Lakshmanaprabu, S. K., U. Sabura Banu, and N. Sivaramakrishnan. "Identification of the Fractional Order First Order Plus Dead Time Parameters of Two Interacting Conical Tank Process Using Bee Colony Optimization Technique Minimizing Root Mean Square Error." In Lecture Notes in Electrical Engineering, 771–82. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2119-7_75.
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Dhupia, Bhawna, and M. Usha Rani. "Assessment of Electric Consumption Forecast Using Machine Learning and Deep Learning Models for the Industrial Sector." In Advances in Wireless Technologies and Telecommunication, 206–18. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch016.
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Power demand forecasting is one of the fields which is gaining popularity for researchers. Although machine learning models are being used for prediction in various fields, they need to upgrade to increase accuracy and stability. With the rapid development of AI technology, deep learning (DL) is being recommended by many authors in their studies. The core objective of the chapter is to employ the smart meter's data for energy forecasting in the industrial sector. In this chapter, the author will be implementing popular power demand forecasting models from machine learning and compare the results of the best-fitted machine learning (ML) model with a deep learning model, long short-term memory based on RNN (LSTM-RNN). RNN model has vanishing gradient issue, which slows down the training in the early layers of the network. LSTM-RNN is the advanced model which take care of vanishing gradient problem. The performance evaluation metric to compare the superiority of the model will be R2, mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE).
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Ciulla, Carlo. "The Results of the Sub-Pixel Efficacy Region Based Bivariate Linear Interpolation Function." In Improved Signal and Image Interpolation in Biomedical Applications, 72–170. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-202-2.ch008.
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This is the first of the chapters of the book that present results obtained processing the MRI database with both classic and SRE-based interpolation paradigms. The focus of this chapter is on the bivariate linear interpolation function. An overview on the validation paradigm is given along with an explanation of the simulations that were conducted in order to validate the SRE-based bivariate liner interpolation function. Subsequently, results with real MRI are shown quantitatively through plots of the metric called RMSE Ratio which was employed to assess which one between classic and SRE-based interpolation furnishes lower root-mean-square-error (RMSE). Qualitative demonstration of the results with MRI is also given. The chapter also discusses and evaluates the case of interpolation error improvement when the novel re-sampling location within the pixel is placed on the x axis of the coordinate system (i.e. yr0 = 0) and consistently, both quantitative and qualitative results are presented through plots of the RSME Ratio and figures respectively.
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Wu, Yunfeng, and Rangaraj M. Rangayyan. "Noise Cancellation in ECG Signals with an Unbiased Adaptive Filter." In Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing, 348–66. IGI Global, 2011. http://dx.doi.org/10.4018/978-1-60960-553-7.ch022.
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The electrocardiographic (ECG) signal is a transthoracic manifestation of the electrical activity of the heart and is widely used in clinical applications. This chapter describes an unbiased linear adaptive filter (ULAF) to attenuate high-frequency random noise present in ECG signals. The ULAF does not contain a bias in its summation unit and the filter coefficients are normalized. During the adaptation process, the normalized coefficients are updated with the steepest-descent algorithm to achieve efficient filtering of noisy ECG signals. A total of 16 ECG signals were tested in the adaptive filtering experiments with the ULAF, the least-mean-square (LMS), and the recursive-least-squares (RLS) adaptive filters. The filtering performance was quantified in terms of the root-mean-squared error (RMSE), normalized correlation coefficient (NCC), and filtered noise entropy (FNE). A template derived from each ECG signal was used as the reference to compute the measures of filtering performance. The results indicated that the ULAF was able to provide noise-free ECG signals with an average RMSE of 0.0287, which was lower than the second-best RMSE obtained with the LMS filter. With respect to waveform fidelity, the ULAF provided the highest average NCC (0.9964) among the three filters studied. In addition, the ULAF effectively removed more noise, measured by FNE, in comparison with the LMS and RLS filters in most of the ECG signals tested. The issues of adaptive filter setting for noise reduction in ECG signals are discussed at the end of this chapter.
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Sasirekha K. and Thangavel K. "A Novel Biometric Image Enhancement Approach With the Hybridization of Undecimated Wavelet Transform and Deep Autoencoder." In Handbook of Research on Machine and Deep Learning Applications for Cyber Security, 245–69. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9611-0.ch012.
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For a long time, image enhancement techniques have been widely used to improve the image quality in many image processing applications. Recently, deep learning models have been applied to image enhancement problems with great success. In the domain of biometric, fingerprint and face play a vital role to authenticate a person in the right way. Hence, the enhancement of these images significantly improves the recognition rate. In this chapter, undecimated wavelet transform (UDWT) and deep autoencoder are hydridized to enhance the quality of images. Initially, the images are decomposed with Daubechies wavelet filter. Then, deep autoencoder is trained to minimize the error between reconstructed and actual input. The experiments have been conducted on real-time fingerprint and face images collected from 150 subjects, each with 10 orientations. The signal to noise ratio (SNR), peak signal to noise ratio (PSNR), mean square error (MSE), and root mean square error (RMSE) have been computed and compared. It was observed that the proposed model produced a biometric image with high quality.
Conference papers on the topic "Root mean square error (RMSE)"
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Effiong, Augustine James, Joseph Okon Etim, and Anietie Ndarake Okon. "Artificial Intelligence Model for Predicting Formation Damage in Oil and Gas Wells." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/207129-ms.
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Abstract An artificial neural network (ANN) was developed to predict skin, a formation damage parameter in oil and gas drilling, well completion and production operations. Four performance metrics: goodness of fit (R2), mean square error (MSE), root mean square error (RMSE), average absolute percentage relative error (AAPRE), was used to check the performance of the developed model. The results obtained indicate that the model had an overall MSE of 355.343, RMSE of 18.850, AAPRE of 4.090 and an R2 of 0.9978. All the predictions agreed with the measured result. The generalization capacity of the developed ANN model was assessed using 500 randomly generated datasets that were not part of the model training process. The results obtained indicate that the developed model predicted 97% of these new datasets with an MSE of 375.021, RMSE of 19.370, AAPRE of 6.090 and R2 of 0.9731, while Standing (1970) equation resulted in R2of −0.807, MSE of 9.34×1016, AAPRE of 3.10×106 and RMSE of 4.10×105. The relative importance analysis of the model input parameters showed that the flow rates (q), permeability (k), porosity (φ) and pressure drop (Δp) had a significant impact on the skin (S) values estimated from the downhole. Thus, the developed model if embedded in a downhole (sensing) tool that capture these basic or required reservoir parameters: pressure, flowrate, permeability, viscosity, and thickness, would eliminate the diagnostic approach of estimating skin factor in the petroleum industry.
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Bin Masood, Junaid, Sajid Hussain, Ali AlAlili, Sara Zaidan, and Ebrahim Al Hajri. "Detailed Dynamic Model of an Institutional Building in Hot and Humid Climate Conditions." In ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/es2017-3582.
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This paper is an ASHRAE Level 3 study of the energy audit process carried out in an institutional building, The Umm Shaif Building, of The Petroleum Institute, Abu Dhabi, UAE. It undertakes the study by collecting data and conditional surveys. The energy loss locations are highlighted through psychrometric and infrared camera analysis. The detailed dynamic model has been simulated using the EnergyPlus® simulation engine. The details of the building envelope, and fenestration, the occupancy schedules, the equipment energy consumption and HVAC details are presented. The detailed building model is used to allocate the energy usage and identify key energy consumers. The main results are reported using monthly total energy consumption. The validation and calibration are performed through different statistical metrics including Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Coefficient of Variance Root Mean Square Error (CVRMSE). Finally, energy conservation measures are suggested with the energy and cost savings.
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Satimehin, A. A., M. O. Oluwamukomi, V. N. Enujiugha, and M. Bello. "Drying characteristics and mathematical modelling of the drying kinetics of oyster mushroom (Pleurotus ostreatus)." In 21st International Drying Symposium. Valencia: Universitat Politècnica València, 2018. http://dx.doi.org/10.4995/ids2018.2018.7847.
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This study was conducted to determine the drying characteristics of oyster mushroom (Pleurotus ostreatus) at 50, 60 and 70 °C. Pleurotus ostreatus were cleaned and dried in a laboratory cabinet dryer. The drying data were fitted to six model equations namely Newton, Pabis and Henderson, Logarithmic, Two-term diffusion, Wang and Singh, as well as Modified Henderson and Pabis equations. The goodness of fit of the models were evaluated by means of the coefficient of determination (R2), root mean square error (RMSE) and reduced chi-square (χ2). The Logarithmic model best describes the drying data and could be used to predict its drying behaviour. Keywords: oyster mushroom; thin-layer drying; characteristics; modelling
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Kessler, Travis, Gregory Dorian, and J. Hunter Mack. "Application of a Rectified Linear Unit (ReLU) Based Artificial Neural Network to Cetane Number Predictions." In ASME 2017 Internal Combustion Engine Division Fall Technical Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/icef2017-3614.
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Due to the high cost and time required to synthesize alternative fuel candidates for comprehensive testing, an Artificial Neural Network (ANN) can be used to predict fuel properties, allowing researchers to preemptively screen desirable fuel candidates. However, the accuracy of an ANN is limited by its error, measured by the root mean square error (RMSE), standard deviation, and r-squared values derived from a given input database. The present work improves upon an existing model for predicting the Cetane Number (CN) by changing the neuron activation function of the ANN from sigmoid to rectified linear unit (ReLU). This change to the ANN’s architecture provides an increase in accuracy by reducing the RMSE by 21.4% (1.35 CN units), the average standard deviation across models by 28%, and increasing the r-squared value by 0.0492 across a wide range of molecular structures. Additionally, by using the ReLU activation function, input data is not required to be normalized, which reduces the likelihood of an inaccurate prediction on future fuel candidates which may have input parameters outside the range of normalization. Increasing the accuracy of the predictive ANN in this way will allow researchers to obtain more accurate fuel property predictions for promising fuel candidates.
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Jia, Bingyan, Danlin Hou, Liangzhu (Leon) Wang, and Ibrahim Galal Hassan. "Estimation of Room-Level Cooling Energy in Hot/Arid Climate by Machine Learning-Based Approaches." In ASME 2021 Verification and Validation Symposium. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/vvs2021-65272.
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Abstract Building energy models (BEM) are developed for understanding a building’s energy performance. A meta-model of the whole building energy analysis is often used for the BEM calibration and energy prediction. The literature review shows that studies with a focus on the development of room-level meta-models are missing. This study aims to address this research gap through a case study of a residential building with 138 apartments in Doha, Qatar. Five parameters, including cooling setpoint, number of occupants, lighting power density, equipment power density, and interior solar reflectance, are selected as input parameters to create ninety-six different scenarios. Three machine-learning models are used as meta-models to generalize the relationship between cooling energy and the model parameters, including Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks. The three meta-models’ prediction accuracies are evaluated by the Normalized Mean Bias Error (NMBE), Coefficient of Variation of the Root Mean Squared Error CV (RMSE), and R square (R2). The results show that the ANN model performs best. A new generic BEM is then established to validate the meta-model. The results indicate that the proposed meta-model is accurate and efficient in predicting the cooling energy in summer and transitional months for a building with a similar floor configuration.
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Sen, Anupam. "Data Mining and Principal Component Analysis on Coimbra Breast Cancer Dataset." In Intelligent Computing and Technologies Conference. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.115.5.
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Machine Learning (ML) techniques play an important role in the medical field. Early diagnosis is required to improve the treatment of carcinoma. During this analysis Breast Cancer Coimbra dataset (BCCD) with ten predictors are analyzed to classify carcinoma. In this paper method for feature selection and Machine learning algorithms are applied to the dataset from the UCI repository. WEKA (“Waikato Environment for Knowledge Analysis”) tool is used for machine learning techniques. In this paper Principal Component Analysis (PCA) is used for feature extraction. Different Machine Learning classification algorithms are applied through WEKA such as Glmnet, Gbm, ada Boosting, Adabag Boosting, C50, Cforest, DcSVM, fnn, Ksvm, Node Harvest compares the accuracy and also compare values such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Square Error (RMSE). Here the 10-fold cross validation method is used for training, testing and validation purposes.
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GÓRNICKI, Krzysztof, Agnieszka KALETA, and Aneta CHOIŃSKA. "EXTENSION OF WEIBULL MODEL FOR DESCRIBING OF DRIED APPLE REHYDRATION." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.026.
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Sample of Ligol variety apples (slices of 3 and 10 mm thickness and cubes of 10 mm thickness) were dried using following methods: natural convection (drying air velocity amounted to 0.01 m/s), forced convection (0.5 and 2 m/s), fluidized bed drying (6 m/s). The drying air temperature was kept at 50, 60, and 70C. The dried apples samples were rehydrated in distilled water at 20, 45, 70, and 95C. The Weibull model given for describing time dependence of the moisture content change was fitted to experimental data and model parameters were determined by multiple regression analysis. The variation of model parameters with characteristic particle dimension (L), drying air velocity (v), drying air temperature (td), and rehydration temperature (tr) described as multiplication type. By using these verification of parameters, extended Weibull model for describing combine effects of L, v, td, tr, and drying time was derived and the parameters of the model were also determined by multiple regression analysis. The accuracies of both models were measured using the determination coefficient (R2), mean bias error (MBE), root mean square error (RMSE), reduced chi-square (2), and t-statistic method. The Weibull model (R2=0.8319-0.9957, MBE=-0.0044-0.0110, RMSE=0.0189-0.1248, 2=0.0004-0.0180, and t-stat=0.0149-0.2875) and the extended Weibull model (R2=0.9130-0.9948, MBE=-0.0209-0.0377, RMSE=0.0230-0.0719, 2=0.0007-0.0057, and t-stat=0.0389-1.2214) described the rehydration characteristics of dried apple satisfactorily. The extended model by taking into account the effect of L, v, td, and tr on its parameters can be considered as more general one.
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Sheng, Shuangwen, and Robert X. Gao. "Architectural Effect on ANFIS for Machine Condition Assessment." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60071.
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This paper investigates the architectural effect on adaptive neuro-fuzzy inference system (ANFIS) for machine condition assessment. The study was motivated by ANFIS’s limitation in adapting its architecture to map the modeled input output relationship. Based on the grid input space partition method, two elements in defining an ANFIS architecture were studied: the type of the membership function (MF) and the MF number assigned to ANFIS inputs. A new modeling accuracy index was introduced to address the limitation of the traditional root mean square error (RMSE) in describing the effect of the MF type. The analysis showed that wide core membership functions enabled a smaller RMSE than narrow core membership functions for machine defect severity classification. It is further shown that selecting appropriate MF number is critical to ensuring accuracy of ANFIS, considering the overfitting problem. These results were experimentally investigated on a bearing test bed, where defect severity classification and dynamic load estimation were evaluated. The experiments agreed well with the theoretical analysis.
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Kakkar, Deepti, Ashish Prashar, Mehar Latif, Aitraiyee Konar, Kishan Kumar, and Amitabh Tripathi. "HATA Path Loss Model Optimization Using Particle Swarm Algorithm." In International Conference on Women Researchers in Electronics and Computing. AIJR Publisher, 2021. http://dx.doi.org/10.21467/proceedings.114.37.
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Fallacious path loss predictions before the placing of base station(BS) cause under evaluation of circulation areas which gives rise to unabating call drops & cross talks. The escalating demands of meeting overhaul needs of applications by users makes the consequence worst, which significantly influences competence of the cellular wireless system. Propagation model is a keystone of coverage planning. To slash cost, proper planning is needed in coverage of network in order to upgrade the quality of service. Now, K factor is taken into account in order to improve or enhance propagation model based on particles swarm optimization(PSO).The root mean square error(RMSE) between confirmed or verified measurements data & data we obtain from prediction model is used to test and validate the technique used. The values of the RMSE acquired by enhancedmodel and those attained by the standard Hata model are also juxtaposed. We reckoned that the model developed using PSO is better than the HATA model and is errorless.
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Abbasi, Ali A., and M. T. Ahmadian. "Prediction of Reaction Force on External Indenter in Cell Injection Experiment Using Support Vector Machine Technique." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-85026.
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Evaluation of the reaction force on a tool which is used for exertion of force on biomaterials such as biological cells or soft tissues has applications in virtual reality based medical simulators or haptic tools. In this study, two least square based support vector machine (SVM) models have been constructed to predict the indentation or reaction force on mouse oocyte and embryo cells in cell injection experiment. Inputs of these two models are geometrical parameters of indented cell, namely dimple radius (a), dimple depth (w) and radius of the semicircular curve (R). Experimental data for calibration and prediction of the models have been captured from literatures. The performance of the models has been evaluated using root mean square error (RMSE), correlation coefficient (r), relative error of prediction (REP), Nash-sutcliffe coefficient of efficiency (Ef) and accuracy factor (Af). Comparison of the prediction results of the SVM models with experimental datapoints shows that the proposed SVM models have the potential to be used for force prediction applications.
Reports on the topic "Root mean square error (RMSE)"
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Brodie, Katherine, Brittany Bruder, Richard Slocum, and Nicholas Spore. Simultaneous mapping of coastal topography and bathymetry from a lightweight multicamera UAS. Engineer Research and Development Center (U.S.), August 2021. http://dx.doi.org/10.21079/11681/41440.
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A low-cost multicamera Unmanned Aircraft System (UAS) is used to simultaneously estimate open-coast topography and bathymetry from a single longitudinal coastal flight. The UAS combines nadir and oblique imagery to create a wide field of view (FOV), which enables collection of mobile, long dwell timeseries of the littoral zone suitable for structure-from motion (SfM), and wave speed inversion algorithms. Resultant digital surface models (DSMs) compare well with terrestrial topographic lidar and bathymetric survey data at Duck, NC, USA, with root-mean-square error (RMSE)/bias of 0.26/–0.05 and 0.34/–0.05 m, respectively. Bathymetric data from another flight at Virginia Beach, VA, USA, demonstrates successful comparison (RMSE/bias of 0.17/0.06 m) in a secondary environment. UAS-derived engineering data products, total volume profiles and shoreline position, were congruent with those calculated from traditional topo-bathymetric surveys at Duck. Capturing both topography and bathymetry within a single flight, the presented multicamera system is more efficient than data acquisition with a single camera UAS; this advantage grows for longer stretches of coastline (10 km). Efficiency increases further with an on-board Global Navigation Satellite System–Inertial Navigation System (GNSS-INS) to eliminate ground control point (GCP) placement. The Appendix reprocesses the Virginia Beach flight with the GNSS–INS input and no GCPs.
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Bruder, Brittany L., Katherine L. Brodie, Tyler J. Hesser, Nicholas J. Spore, Matthew W. Farthing, and Alexander D. Renaud. guiBath y : A Graphical User Interface to Estimate Nearshore Bathymetry from Hovering Unmanned Aerial System Imagery. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39700.
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This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details guiBathy, a graphical user interface to estimate nearshore bathymetry from imagery collected via a hovering Unmanned Aerial System (UAS). guiBathy provides an end-to-end solution for non-subject-matter-experts to utilize commercia-off-the-shelf UAS to collect quantitative imagery of the nearshore by packaging robust photogrammetric and signal-processing algorithms into an easy-to-use software interface. This report begins by providing brief background on coastal imaging and the photogrammetry and bathymetric inversion algorithms guiBathy utilizes, as well as UAS data collection requirements. The report then describes guiBathy software specifications, features, and workflow. Example guiBathy applications conclude the report with UAS bathymetry measurements taken during the 2020 Atlantic Hurricane Season, which compare favorably (root mean square error = 0.44 to 0.72 m; bias = -0.35 to -0.11 m) with in situ survey measurements. guiBathy is a standalone executable software for Windows 10 platforms and will be freely available at www.github.com/erdc.
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Collins, Clarence O., and Tyler J. Hesser. altWIZ : A System for Satellite Radar Altimeter Evaluation of Modeled Wave Heights. Engineer Research and Development Center (U.S.), February 2021. http://dx.doi.org/10.21079/11681/39699.
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This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the design and implementation of a wave model evaluation system, altWIZ, which uses wave height observations from operational satellite radar altimeters. The altWIZ system utilizes two recently released altimeter databases: Ribal and Young (2019) and European Space Agency Sea State Climate Change Initiative v.1.1 level 2 (Dodet et al. 2020). The system facilitates model evaluation against 1 Hz1 altimeter data or a product created by averaging altimeter data in space and time around model grid points. The system allows, for the first time, quantitative analysis of spatial model errors within the U.S. Army Corps of Engineers (USACE) Wave Information Study (WIS) 30+ year hindcast for coastal United States. The system is demonstrated on the WIS 2017 Atlantic hindcast, using a 1/2° basin scale grid and a 1/4° regional grid of the East Coast. Consistent spatial patterns of increased bias and root-mean-square-error are exposed. Seasonal strengthening and weakening of these spatial patterns are found, related to the seasonal variation of wave energy. Some model errors correspond to areas known for high currents, and thus wave-current interaction. In conjunction with the model comparison, additional functions for pairing altimeter measurements with buoy data and storm tracks have been built. Appendices give information on the code access (Appendix I), organization and files (Appendix II), example usage (Appendix III), and demonstrating options (Appendix IV).