Statistical Error Analysis Research Articles

Cosmology using long gamma-ray bursts: statistical analysis of errors in calibrated data

We investigate direction dependence and non-Gaussian features in high-z cosmological data using Δχ2 and Δχ statistics and the Kolmogorov–Smirnov test. These techniques are applied on a set of calibrated long gamma-ray bursts (GRBs) and its combination with recent Type Ia supernovae data (Union2). Our statistical analysis shows a weak but consistent direction dependence in both the data sets. The analysis also indicates a non-Gaussian nature of errors in both data sets.

Constitutive Modeling of 2024 Aluminum Alloy Based on the Johnson–Cook Model

In this paper, hot compression behavior of Al2024 in the temperatures range of 573–723 K and strain rate range of 0.001–0.6 s−1 was studied based on standard tests. The prediction of flow stress was performed using constitutive equations based on the basic and modified Johnson–Cook model, and the accuracy of the proposed models was estimated by statistical error analysis method. Based on the experimental results, flow stress got changed significantly with changes in the strain rate and temperature. Since the basic model could not examine the correlated effects of the parameters, it had inadequate exactness to estimate the flow stress especially at high temperatures. During calculation, the constants in the modified model, effects of hardening and softening behavior were included in addition to considering the correlated effects of the parameters, so the accuracy of the modified model was increased significantly.

Estimating the mass change of mountain glacier using a novel method based on InSAR observations

Melting glaciers have a direct contribution to sea level rise, runoff and glacier lake outburst flood disasters. Therefore, accurate estimations of glacier mass balances with high spatial (large region) and temporal (annual, seasonal) resolutions are very important. However, the estimation of glacial mass balances in mountainous regions is usually hampered by remoteness and the lack of high-precision topographic data for most mountain ranges. This study presents a novel method for estimating mass changes in mountain glaciers using InSAR data. We utilised observations of glacier surface deformation to derive changes in thickness, and then calculated the mass change. This method can accurately estimate short-term mass changes. We apply this method to the Koxkar glacier in the Tien Shan Mountain Range in China and successfully estimate the seasonal mass change. Using theoretical error and statistical error analysis, we determined that the accuracy of this method is much better than other geodetic methods. We analyse the spatial characteristics of the mass changes in the ablation zone for the first time. The results show considerable spatial and seasonal variability, which were mainly from ablation in the summer, with the greatest amount of glacial ablation reaching up to − 3872 mm water equivalent (w.e.), and from accumulation in the winter.

Evaluation of a high-resolution wave hindcast model SWAN for the West Mediterranean basin

This study aims to present an evaluation and implementation of a high-resolution SWAN wind wave hindcast model forced by the CFSR wind fields in the west Mediterranean basin, taking into account the recent developments in wave modelling as the new source terms package ST6. For this purpose, the SWAN model was calibrated based on one-year wave observations of Azeffoune buoy (Algerian coast) and validated against eleven wave buoys measurements through the West Mediterranean basin. For the calibration process, we focused on the whitecapping dissipation coefficient Cds and on the exponential wind wave growth and whitecapping dissipation source terms. The statistical error analysis of the calibration results led to conclude that the SWAN model calibration corrected the underestimation of the significant wave height hindcasts in the default mode and improved its accuracy in the West Mediterranean basin. The exponential wind wave growth of Komen et al (1984) and the whitecapping dissipation source terms of Janssen (1991) with Cds = 1.0 have been thus recommended for the western Mediterranean basin. The comparison of the simulation results obtained using this calibrated parameters against eleven measurement buoys showed a high performance of the calibrated SWAN model with an average scatter index of 30% for the significant wave heights and 19% for the mean wave period. This calibrated SWAN model will constitute a practical wave hindcast model with high spatial resolution (˜3 km) and high accuracy in the Algerian basin, which will allow us to proceed to a finer mesh size using the SWAN nested grid system in this area.

Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization: Application to thermal enhanced oil recovery processes

Relative permeability is an indispensable parameter that is involved in many sensitive tasks related to reservoir studies. Due to this fact, there is increasing interest about methods that can accurately capture the temperature effect on relative permeability. As the existing empirical methods suffer from inaccuracies and the experimental methods are expensive and time consuming, developing accurate, rapid, and inexpensive model is inevitable to determine temperature-dependent oil/water relative permeability. In this paper, we propose various intelligent approaches to predict the effect of temperature on relative permeability in the oil/water systems (Kro and Krw). These approaches are based on two machine-learning methods: least square support vector machine (LSSVM) and radial basis neural network (RBFNN), coupled with four metaheuristic algorithms including particle swarm optimization (PSO), genetic algorithm (GA), differential evolution (DE) and grey wolf optimization (GWO). A large database including 1223 experimental points collected from published literature and some Algerian fields is employed to establish and test the models with the following inputs: absolute permeability, water saturation, reservoir temperature, water viscosity, and oil viscosity. The obtained results revealed that the proposed models can predict the temperature-based oil/water relative permeability with very satisfactory accuracy. Furthermore, graphical and statistical error analyses illustrated that RBFNN-GWO model outperforms the existing correlations and the other established hybridization-based models in predicting both Kro and Krw, where RBFNN-GWO showed overall determination coefficients (R2) of 0.9997 and 0.9996, average absolute relative deviations (AARDs%) of 3.4056 and 5.8663, and root mean squared errors (RMSEs) of 0.0073 and 0.0048 for Kro and Krw, respectively. Finally, outliers detection using the Leverage approach confirmed that the proposed RBFNN-GWO model is statistically valid, where only 1.69% and 1.39% of the Kroand Krw data points, respectively, may be regarded as doubtful data.

Opinion Polls and Statistics Conflict—Time for a Change?

Generally, in the 2016 United States Presidential Election, Mr. Trump was underestimated by 6.9% and greater than the margin of error. Against this background, the paper discusses the shortcomings of the existing methods and supports the view that a new polling method is needed when related to “opinions”. Graphs show that the maximum error did not occur at the expected value, nor did the data align with a normal statistical bell-shaped distribution. Vulnerabilities exist with combining fact-based statistical analysis with feeling-based opinions. Basic statistics equations do not cover feeling- based factors, i.e. biases, truthfulness, competency, nonresponse rates, etc. A comprehensive pollster accuracy study showed that the most widely used pollsters had significant biases favoring Democrats over Republicans. The 2016 polling failures illustrate deficiencies in the existing approach supporting the view that a new methodology is needed such as Statistical Error Analysis, On-Line Methodology, and others.

WAM and WAVEWATCH-III intercomparison studies in the North Indian Ocean using Oceansat-2 Scatterometer winds

This paper presents the intercomparison of wave hindcasts using the third-generation models WAM and WAVEWATCH-III for the North Indian Ocean over 1° × 1° (latitude × longitude) grid resolutions, which reveals the first assessment of their relative performance through intercomparison of the model results. Hindcast wave parameters such as significant wave height, mean wave period, and swell wave height obtained from the simulations using Oceansat-2 scatterometer winds are analyzed to understand the quality and variability associated with the individual model outputs in the Indian Ocean. WAM and WAVEWATCH-III intercomparison studies are carried out for four different cases (January and June 2010, and January and June 2011). A comparative study of the relative performances of these two models is evaluated through extensive and robust statistical error analysis. Based on both qualitative and quantitative assessment of the model results, this study clearly indicates that both WAM and WAVEWATCH-III performed well in the common model domain using Oceansat-2 scatterometer winds, and they can be confidently used for long-term hindcasting in the North Indian Ocean, which will be very useful for most of the user community dealing with various coastal/offshore activities. The study also suggests that it would be preferable to consider available long-term wave measurements both in deep and coastal waters of the North Indian Ocean to validate and intercompare WAM and WAVEWATCH-III further.

Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems

Abstract Exact determination of pressure-volume-temperature (PVT) properties of the reservoir oils is necessary for reservoir calculations, reservoir performance prediction, and the design of optimal production conditions. The objective of this study is to develop intelligent and reliable models based on multilayer perceptron (MLP) and radial basis function (RBF) neural networks for estimating the solution gas–oil ratio as a function of bubble point pressure, reservoir temperature, oil gravity (API), and gas specific gravity. These models were developed and tested using a total of 710 experimental data sets representing the samples of crude oil from various geographical locations around the world. Performance of the developed MLP and RBF models were evaluated and investigated against a number of well-known empirical correlations using statistical and graphical error analyses. The results indicated that the proposed models outperform the considered empirical correlations, providing a strong agreement between predicted and experimental values, However, the developed RBF exhibited higher accuracy and efficiency compared to the proposed MLP model.

Displacement Monitoring and Health Evaluation of Two Bridges Using Sentinel-1 SAR Images

Displacement monitoring of large bridges is an important source of information concerning their health state. In this paper, a procedure based on satellite Persistent Scatterer Interferometry (PSI) data is presented to assess bridge health. The proposed approach periodically assesses the displacements of a bridge in order to detect abnormal displacements at any position of the bridge. To demonstrate its performances, the displacement characteristics of two bridges, the Nanjing-Dashengguan High-speed Railway Bridge (NDHRB, 1272 m long) and the Nanjing-Yangtze River Bridge (NYRB, 1576-m long), are studied. For this purpose, two independent Sentinel-1 SAR datasets were used, covering a two-year period with 75 and 66 images, respectively, providing very similar results. During the observed period, the two bridges underwent no actual displacements: thermal dilation displacements were dominant. For NDHRB, the total thermal dilation parameter from the PSI analysis was computed using the two different datasets; the difference of the two computations was 0.09 mm/°C, which, assuming a temperature variation of 30 °C, corresponds to a discrepancy of 2.7 mm over the total bridge length. From the total thermal dilation parameters, the coefficients of thermal expansion (CTE) were calculated, which were 11.26 × 10−6/°C and 11.19 × 10−6/°C, respectively. These values match the bridge metal properties. For NYRB, the estimated CTE was 10.46 × 10−6/°C, which also matches the bridge metal properties (11.26 × 10−6/°C). Based on a statistical analysis of the PSI topographic errors of NDHRB, pixels on the bridge deck were selected, and displacement models covering the entire NDHRB were established using the two track datasets; the model was validated on the six piers with an absolute mean error of 0.25 mm/°C. Finally, the health state of NDHRB was evaluated with four more images using the estimated models, and no abnormal displacements were found.

Ergonomic interventions for preventing musculoskeletal disorders in dental care practitioners.

Dentistry is a profession with a high prevalence of work-related musculoskeletal disorders (WMSD) among practitioners, with symptoms often starting as early in the career as the student phase. Ergonomic interventions in physical, cognitive, and organisational domains have been suggested to prevent their occurrence, but evidence of their effects remains unclear. To assess the effect of ergonomic interventions for the prevention of work-related musculoskeletal disorders among dental care practitioners. We searched CENTRAL, MEDLINE PubMed, Embase, PsycINFO ProQuest, NIOSHTIC, NIOSHTIC-2, HSELINE, CISDOC (OSH-UPDATE), ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry Platform (ICTRP) Search Portal to August 2018, without language or date restrictions. We included randomised controlled trials (RCTs), quasi-RCTs, and cluster RCTs, in which participants were adults, aged 18 and older, who were engaged in the practice of dentistry. At least 75% of them had to be free from musculoskeletal pain at baseline. We only included studies that measured at least one of our primary outcomes; i.e. physician diagnosed WMSD, self-reported pain, or work functioning. Three authors independently screened and selected 20 potentially eligible references from 946 relevant references identified from the search results. Based on the full-text screening, we included two studies, excluded 16 studies, and two are awaiting classification. Four review authors independently extracted data, and two authors assessed the risk of bias. We calculated the mean difference (MD) with 95% confidence intervals (CI) for continuous outcomes and risk ratios (RR) with 95% confidence intervals for dichotomous outcomes. We assessed the quality of the evidence for each outcome using the GRADE approach. We included two RCTs (212 participants), one of which was a cluster-randomised trial. Adjusting for the design effect from clustering, reduced the total sample size to 210. Both studies were carried out in dental clinics and assessed ergonomic interventions in the physical domain, one by evaluating a multi-faceted ergonomic intervention, which consisted of imparting knowledge and training about ergonomics, work station modification, training and surveying ergonomics at the work station, and a regular exercise program; the other by studying the effectiveness of two different types of instrument used for scaling in preventing WMSDs. We were unable to combine the results from the two studies because of the diversity of interventions and outcomes.Physical ergonomic interventions. Based on one study, there is very low-quality evidence that a multi-faceted intervention has no clear effect on dentists' risk of WMSD in the thighs (RR 0.57, 95% CI 0.23 to 1.42; 102 participants), or feet (RR 0.64, 95% CI 0.29 to 1.41; 102 participants) when compared to no intervention over a six-month period. Based on one study, there is low-quality evidence of no clear difference in elbow pain (MD -0.14, 95% CI -0.39 to 0.11; 110 participants), or shoulder pain (MD -0.32, 95% CI -0.75 to 0.11; 110 participants) in participants who used light weight curettes with wider handles or heavier curettes with narrow handles for scaling over a 16-week period.Cognitive ergonomic interventions. We found no studies evaluating the effectiveness of cognitive ergonomic interventions.Organisational ergonomic interventions. We found no studies evaluating the effectiveness of organisational ergonomic interventions. There is very low-quality evidence from one study showing that a multi-faceted intervention has no clear effect on dentists' risk of WMSD in the thighs or feet when compared to no intervention over a six-month period. This was a poorly conducted study with several shortcomings and errors in statistical analysis of data. There is low-quality evidence from one study showing no clear difference in elbow pain or shoulder pain in participants using light weight, wider handled curettes or heavier and narrow handled curettes for scaling over a 16-week period.We did not find any studies evaluating the effectiveness of cognitive ergonomic interventions or organisational ergonomic interventions.Our ability to draw definitive conclusions is restricted by the paucity of suitable studies available to us, and the high risk of bias of the studies that are available. This review highlights the need for well-designed, conducted, and reported RCTs, with long-term follow-up that assess prevention strategies for WMSDs among dental care practitioners.

A neural network (CSA-LSSVM) model for the estimation of surface tension of branched alkanes

ABSTRACTThe current study highlights the application of a model based on least square support vector machine (LSSVM) for prediction of surface tension of branched alkanes. An optimization algorithm, namely, coupled simulated annealing (CSA) was applied to the model. Surface tensions of alkanes show a specific interaction between adjacent molecules of the branched alkanes which affects the anisotropic dispersion force component of the surface energy. In this paper, surface tension of branched alkanes was studied in temperature range of 283.15 and 448.15 K. To evaluate the performance and accuracy of this model, statistical and graphical error analyses have been used simultaneously. By applying CSA-LSSVM on 600 data points and finding optimum parameters, the estimated values of surface tension of branched alkanes were compared with experimental data which showed a reasonable agreement with the experimental results. Results demonstrate that the model is precise and viable for prediction of solubility data. The model shows an overall R2 and AARD% estimations of 0.9921 and 0.89%, respectively.

Prediction of depth-averaged velocity in an open channel flow

This paper presents a new methodology to predict the depth-averaged velocity along the lateral direction in an open channel flow. The novelty of this work is to determine the point velocity and estimate the discharge capacity by knowing the geometrical parameters at a section of an open channel flow. Experimental investigations have been undertaken in trapezoidal and rectangular channels to observe the variation of local velocities along both the vertical and transverse directions at testing sections. For different geometry, hydraulic and roughness conditions, the measurements are taken for several flow conditions. Multi-variable regression analysis has been adopted to develop five models to predict the point velocities in terms of non-dimensional geometric and flow parameters at any desired location. The present method is favourably compared with the analytical method of Shiono and Knight with reasonable accuracy. The performance of mathematical model is also validated with two natural river data sets. Further, statistical error analysis is carried out to know the degree of accuracy of the present models.

Experimental investigations of SDS adsorption on the Algerian rock reservoir: chemical enhanced oil recovery case

The adsorption of surfactant on reservoir rocks or sands is one of the main factors that can significantly reduce the effectiveness of surfactant flooding for light oil recovery. The present work consists of study of the interaction between the surfactant sodium dodecyl sulfate (SDS) and the characteristics of Hassi Messaoud rock established using scanning electron microscopy, Fourier transform infrared analysis, Brunauer–Emmett–Teller surface area analysis and optical microscopy. Parametric study of the adsorption tests was carried out at a temperature between 25 and 80 °C. The conductivity method was used to measure the critical micellar concentration of the SDS surfactant in aqueous solutions. The adsorption data were adjusted using five isotherms and two kinetics of the anionic surfactant. The equilibrium conditions for crushed rock samples were obtained after 11 h of adsorption. According to the experimental results, the equilibrium data were well fitted by the Langmuir isotherm for sandstone sample at all temperatures. The pseudo-second-order model best describes the kinetics of the adsorption process. Furthermore, the adsorption process is exothermic. Several statistical error analyses were applied to evaluate the performance and accuracy of the proposed experiment. A systematic investigation is very useful for selecting an appropriate surfactant for enhanced oil recovery application and reservoir stimulation in the petroleum industry.

Estimating phase behavior of the asphaltene precipitation by GA-ANFIS approach

This study implements an adaptive neuro-fuzzy inference system (ANFIS) approach to predict the precipitation amount of the asphaltene using temperature (T), dilution ratio (R v), and molecular weight of different n-alkanes. Results are then evaluated using graphical and statistical error analysis methods, confirming the model’s great ability for appropriate prediction of the precipitation amount. Mean squared error and determination coefficient (R 2) values of 0.036 and 0.995, respectively are obtained for the proposed ANFIS model. Results are then compared to those from previously reported correlations revealing the better performance of the proposed model.

Application of hybrid support vector regression artificial bee colony for prediction of MMP in CO2-EOR process

Minimum miscibility pressure (MMP) is a key parameter in the successful design of miscible gases injection such as CO2 flooding for enhanced oil recovery process (EOR). MMP is generally determined through experimental tests such as slim tube and rising bubble apparatus (RBA). As these tests are time-consuming and their cost is very expensive, several correlations have been developed. However, and although the simplicity of these correlations, they suffer from inaccuracies and bad generalization due to the limitation of their ranges of application. This paper aims to establish a global model to predict MMP in both pure and impure CO2-crude oil in EOR process by combining support vector regression (SVR) with artificial bee colony (ABC). ABC is used to find best SVR hyper-parameters. 201 data collected from authenticated published literature and covering a wide range of variables are considered to develop SVR-ABC pure/impure CO2-crude oil MMP model with following inputs: reservoir temperature (TR), critical temperature of the injection gas (Tc), molecular weight of pentane plus fraction of crude oil (MWC5+) and the ratio of volatile components to intermediate components in crude oil (xvol/xint). Statistical indicators and graphical error analyses show that SVR-ABC MMP model yields excellent results with a low mean absolute percentage error (3.24%) and root mean square error (0.79) and a high coefficient of determination (0.9868). Furthermore, the results reveal that SVR-ABC outperforms either ordinary SVR with trial and error approach or all existing methods considered in this work in the prediction of pure and impure CO2-crude oil MMP. Finally, the Leverage approach (Williams plot) is done to investigate the realm of prediction capability of the new model and to detect any probable erroneous data points.

Prediction of heat transfer of two different types of roughened solar air heater using Artificial Neural Network technique

In the present article, the Feed Forward Neural Network (FFNN) model has been used to predict the heat transfer from roughened absorber plate to air passing through the ducts of solar air heater and compare with actual experimental data. One side and three sided roughened absorber plates have been taken up for experimental study of solar air heater (SAH) heat transfer analysis. Total 50 data sample have been used in the present neural model. Artificial Neural Network (ANN) model developed with feed forward back-propagation (FFBP) multi-layer perceptron using five parameters (number of rough surfaces side, relative roughness height, relative roughness pitch, roughness size and Reynolds Number) and one parameter (Nusselt number) have been used in input layer and output layer respectively. Levenberg-Marquardt (LM) algorithm with 6–12 neurons has been used to find out the optimal model. The 10 numbers of neurons in hidden layer model has been found as optimal on the basis of statistical error analysis. The 5-10-1 neural model predict the heat transfer characteristics as Nusselt number with higher value of R2 gives satisfactory results. The values of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) were found 0.89202, 0.66261 and 0.99532 respectively during training stage. Similarly for testing stage these values were 0.55094, 0.31683 and 0.99791 respectively. The traditional statistical multiple linear regression (MLR) model has also been used in prediction of heat transfer. The MLR model has been compared with ANN model. The Performance criteria show that the ANN model performs better as compared the MLR model. The average value of coefficient of determination for the ANN model was higher by 1.79% than for the MLR model. The results indicated that the proposed ANN model successfully predicts the heat transfer analysis of roughened solar air heater.

Evolving robust intelligent model based on group method of data handling technique optimized by genetic algorithm to predict asphaltene precipitation

Precipitation of asphaltene during primary production of hydrocarbon reservoirs leads to formation damage and well bore plugging. Therefore, proposing an accurate model to estimate asphaltene precipitation under various operating and thermodynamic conditions are crucial. In this study, a new mathematical model based on the integrating group method of data handling (GMDH) with genetic algorithm has been developed to predict asphaltene precipitation as a function of reservoir pressure and temperature, crude oil API, bubble point pressure, Saturated-Aromatic-Resin-Asphaltene (SARA) fractions and mole percent of non-hydrocarbon gases. Genetic algorithm technique has been applied to optimize the most appropriate network structure of GMDH model. In order to accomplish modeling, asphaltene precipitation of different crude oils from a number of Iranian reservoirs at wide ranges of operating conditions have been measured experimentally and applied for network construction. The accuracy of developed model has been evaluated by both statistical and graphical error analysis techniques. The average absolute relative deviation of the proposed model is 3.65%, which indicates model predictions are in excellent agreement with experimental data. Also, the comparison of developed GMDH model with scaling equation and least squares support vector machine (LSSVM) reveals the superiority of the proposed GMDH structure in prediction of asphaltene precipitation over scaling equation and LSSVM technique. In addition, the Leverage approach has been applied to detect suspected data. The results show that all experimental data are reliable and located within the applicable domain of developed model. Finally, a comprehensive sensitivity analysis based on the relevancy factor has been carried out which shows that percentages of resin and saturated components have the largest direct and inverse impacts on asphaltene precipitation, respectively.

Development of Feed-Forward Back-Propagation Neural Model to Predict the Energy and Exergy Analysis of Solar Air Heater

In the present work, Artificial Neural Network (ANN) model has been developed to predict the energy and exergy efficiency of a roughened solar air heater (SAH). Total fifty data sets of samples, obtained by conducting experiments on SAHs with three different specification of wire-rib roughness on the absorber plates, have been used in this work. These experimental data and calculated values of thermal efficiency and exergy efficiency have been used to develop an ANN model. Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) learning algorithm were used to train the proposed ANN model. Six numbers of neurons were found with LM learning algorithm in the hidden layer as the optimal value on the basis of statistical error analysis. In the input layer, the time of experiments, mass flow rate, ambient temperature, mean temperature of air, absorber plate temperature and solar radiation intensity have been taken as input parameters; and energy efficiency and exergy efficiency have been taken as output parameters in the output layer. The 6-6-2 neural model has been obtained as the optimal model for prediction. Performance predictions using ANN were compared with the experimental data and a close agreement was observed. Statistical error analysis was used to evaluate the results. Citation:  Ghritlahre, H. K. (2018). Development of feed-forward back-propagation neural model to predict the energy and exergy analysis of solar air heater. Trends in Renewable Energy, 4, 213-235. DOI: 10.17737/tre.2018.4.2.0078

Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique

In the present study three different types of neural models: multi-layer perceptron (MLP), generalized regression neural network (GRNN) and radial basis function (RBF) has been used to predict the exergetic efficiency of roughened solar air heater. The experiments were conducted at NIT Jamshedpur, India, using two different types of absorber plate: arc shape wire rib roughened with relative roughness height 0.0395, relative roughness pitch 10 and angle of attack 60°, and smooth absorber plates for 7 days. Total 210 data sets were collected from the experiments. Mass flow rate, relative humidity, wind speed, ambient air temperature, inlet air temperature, mean air temperature, average plate temperature and solar intensity were selected as input parameters in input layer to estimate the exergetic efficiency. In the first part of study, MLP model has been used. In this model 10–20 neurons with LM learning algorithm were used in hidden layer for optimal model selection. It has been found that LM-18 is an optimal model. In second part, GRNN model was used. The GRNN model was simulated experimentally at different spread constants and found that keeping spread constant as 1.5, optimal results have been obtained. In the third part, RBF model was used. For optimal model, 1–5 spread constant at interval of 0.5 have been used. It has been found that by taking spread constant 3.5, best results are obtained. In the last part of the study, all neural models are compared on the basis of statistical error analysis. It has been found that RBF model is better than GRNN and MLP models due to lowest value of RMSE and MAE and highest value of R2 and ME. After RBF model, GRNN model performs better results as compared to MLP model. It has been found that the values of RMSE, MAE and R2 were 0.001652, 2.86E-04 and 0.99999 respectively for RBF model.

Performance Comparison of NAMI DANCE and FLOW-3D® Models in Tsunami Propagation, Inundation and Currents using NTHMP Benchmark Problems

Field observations provide valuable data regarding nearshore tsunami impact, yet only in inundation areas where tsunami waves have already flooded. Therefore, tsunami modeling is essential to understand tsunami behavior and prepare for tsunami inundation. It is necessary that all numerical models used in tsunami emergency planning be subject to benchmark tests for validation and verification. This study focuses on two numerical codes, NAMI DANCE and FLOW-3D®, for validation and performance comparison. NAMI DANCE is an in-house tsunami numerical model developed by the Ocean Engineering Research Center of Middle East Technical University, Turkey and Laboratory of Special Research Bureau for Automation of Marine Research, Russia. FLOW-3D® is a general purpose computational fluid dynamics software, which was developed by scientists who pioneered in the design of the Volume-of-Fluid technique. The codes are validated and their performances are compared via analytical, experimental and field benchmark problems, which are documented in the ‘‘Proceedings and Results of the 2011 National Tsunami Hazard Mitigation Program (NTHMP) Model Benchmarking Workshop’’ and the ‘‘Proceedings and Results of the NTHMP 2015 Tsunami Current Modeling Workshop”. The variations between the numerical solutions of these two models are evaluated through statistical error analysis.