Furthermore, through the reasonable selection of validation observation and the weighted fusion strategy, the adverse influence caused by observation deviation consistency on filtering precision is improved.
Aiming at the observation deviation consistency problem, caused by random observation noise and external disturbance, appearing in the process of data assimilation of Ensemble Kalman filter, a novel multi-sensor Ensemble Kalman filtering algorithm based on observation fuzzy support degree fusion is proposed in this paper.
The level of difference in the 80 kVp data again exceeds the 100 and 150 kVp data sets, and their combined respective magnitudes with those seen in Fig. 3 correlate with the observation deviation between the experimental and predicted 80 kVp data seen in Fig. 2.
The Z score measures an observation's deviation from the mean in standard deviations, the R score measures an observation's deviation from the median in median absolute deviations, and the (weighted) outlying degree is the (weighted) sum of the k smallest absolute deviations involving a particular observation (where k is the OD tuning parameter).
Kriging and IDW were the two methods that produced superior results than other applied techniques in terms of Nash-Sutcliff Efficiency (NSE), RMSE-observations standard deviation ratio (RSR) Percentage bias (PBIAS) and Peak percent threshold statistics (PPTS).
Multi-objective function statistics: P-factor (23%, 12%), R-factor (0.63, 0.40), Nash Sutcliffe efficiency, NSE (0.55, 0.53), root mean square error-observations standard deviation ratio (RSR) (0.67, 0.69), coefficient of determination, R2 (0.52, 0.52), and percent bias, PBIAS (− 14.6%, 0.8%) for calibration and validation, respectively were obtained.
Using the aforementioned datasets, the performance of the hydrological model in estimating ETa was improved using both calibration techniques by achieving Nash-Sutcliffe efficiency (NSE) values >0.5 (0.73–0.85), percent bias (PBIAS) values within ±25% (±21.73%), and root mean squared error – observations standard deviation ratio (RSR) values <0.7 (0.39–0.52).
We expect the observation of deviations of this quantity away from that expected in a random network to be indicative of local structure within the network and also suggestive of the degree to which the network contains useful information in its correlated, clustered connections.
Note: N, SD, Min and Max means observations, standard deviation, minimum and maximum, respectively.
Descriptive statistics (mean, median, number of observations, standard deviation, standard error, 95% confidence intervals - minimum and maximum) of all primary and other variables will be tabulated.
In addition to standard regression parameters, model evaluation statistics for calibrated grassland types included Nash Sutcliffe efficiency (NSE), percent BIAS, root mean square error (RMSE) and the RMSE observations standard deviation ratio (RSR).
