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A Review on Localization in Wireless Sensor Networks for Static and Mobile Applications
Published in Rajshree Srivastava, Sandeep Kautish, Rajeev Tiwari, Green Information and Communication Systems for a Sustainable Future, 2020
P. Singh, Nitin Mittal, Rajshree Srivastava, Sandeep Kautish, Rajeev Tiwari
Mean Absolute Error (MAE): MAE is basically calculated with respect to the continuous variables and it is an important parameter for finding out the accuracy of a localization algorithm used in a specified application. The equation for MAE is Eq. (1.5), where (xt, yt, zt) is the current position, (xe, ye, ze) is the calculated position, and Nt represents the total number of sensor nodes deployed. AbsoluteError=∑t=1Ni(xt−xe)2+(yt−ye)2+(zt−ze)2Nt
Effect of proportion of wash load to suspended load on river erosion and deposition
Published in Silke Wieprecht, Stefan Haun, Karolin Weber, Markus Noack, Kristina Terheiden, River Sedimentation, 2016
C.T. Liao, K.C. Yeh, G.H. Liu, K.W. Wu
The comparison of simulated and measured bed changes in calibration and validation are shown in Figure 16 and Figure 17. The mean absolute error, MAE is selected to calculate the errors. It is expressed as follows: () MAE=∑i=1n|Z1(i)−Z2(i)|N
Prediction of Hydraulic Blockage at Culverts using Lab Scale Simulated Hydraulic Data
Published in Urban Water Journal, 2022
Umair Iqbal, Muhammad Zain Bin Riaz, Johan Barthelemy, Pascal Perez
where denotes the total number of data samples, denotes the actual output and denotes the predicted output. Mean Absolute Error (MAE) is identical to MSE except that it calculates the sum of the error’s absolute values rather than its square values. It calculates the mean error without taking direction into account. It is optimal for situations in which the training data may contain outliers. Equation 4 demonstrates how to calculate MAE mathematically.The score is a widely used metric for assessing the performance of regression models. It expresses a model’s capacity to account for the variability of the dependent variable and is calculated by squaring the Correlation Coefficient (). It is calculated mathematically by dividing the sum of the squares of the prediction error by the sum of the squares of all the predictions. Equation 5 contains the mathematical formula for calculating .
Non-negative variance component estimation for the partial EIV model by the expectation maximization algorithm
Published in Geomatics, Natural Hazards and Risk, 2020
and are the variance component estimates for the observations and coefficient matrix errors, respectively. indicates the norm between the parameter estimate and the true value. MAE is the mean absolute error and describes the precision of the model-prediction. The weighted form of mean absolute error is where represents the individual model-prediction error and is the individual weight of observations. As seen from Table 3, there is a considerable difference for the parameters estimated depending on whether the VCE methods is considered. The parameters based on the VCE are closer to the true values. Additionally, the MAE of estimation results by the VCE methods is also smaller in comparison to the methods of LS and TLS. Regarding the number of iterations, Scheme 4 has 38, while Scheme 3 and 5 have 43. Regarding the two proposed approaches, the convergence of Scheme 6 is relatively slow and the iteration number is 121. However, Scheme 7 requires only 20 iterations, which is more efficient. The convergence of the VCE for Schemes 3, 6 and 7 is given in Figure 2. According to the results of this example, the EM-VCE and the modified EM-NN-VCE accord with the three previous VCE methods.
Advanced filtration in greywater treatment: a modelling approach with water reuse perspectives
Published in Urban Water Journal, 2020
Their performance was evaluated in terms of mean square error (MSE) and mean absolute error (MAE) for a given input and model-predicted output. The optimized ANN model architectures with the highest performance, i.e. lowest MAE values, were considered as the final model (Unit Model) in the process of water quality predictions. The performance parameters MSE and MAE are defined in Equations 1 and 2, respectively. The MSE is defined as the average of the square of the errors (both positive and the negative error), and the mean absolute error (MAE) is defined as the average of the errors (both positive and negative error).