Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model †
Abstract
:1. Introduction
2. Methodology
2.1. Data
2.2. Takagi-Sugeno Fuzzy Rule-Based Model
2.3. Simple Average of the Models for Ensembling
3. Results
4. Conclusions
- The analysis shows that the performance, in terms of corr and RMSE, of the EA is better, compared to the individual RCMs for both MSs. However, the PBias values of the best-performing RCM are much better than those of SEM and TS RFB;
- The nonlinear TS FRB model has very similar prediction skills to the simple SEM model. So, when the effort to select the best cc and combination is taken into account, SEM is more efficient, compared to the TS FRB model for ensembling;
- Both models failed to predict peak precipitation events.
Acknowledgments
Conflicts of Interest
References
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General Circulation Models (GCM) | Regional Climate Models (RCMs) | Model Number | µ/σ (~17190) | µ/σ (~17320) |
---|---|---|---|---|
CNRM-CM5 (CNRM-CERFACS) | CCLM4-8-17 | RCM1 | 1.85/1.20 | 2.89/3.38 |
ALADIN53 | RCM2 | 2.50/1.63 | 1.65/1.89 | |
EC-EARTH (ICHEC) | CCLM4-8-17 | RCM3 | 1.34/1.12 | 2.26/2.82 |
RACMO22E | RCM4 | 0.95/0.71 | 1.52/1.75 | |
HIRHAM5 | RCM5 | 1.14/0.95 | 2.01/2.59 | |
CM5A-MR (IPSL) | WRF331F | RCM6 | 2.21/1.63 | 1.98/2.52 |
HadGEM2-ES (MOHC) | CCLM4-8-17 | RCM7 | 1.51/1.28 | 3.33/3.90 |
RACMO22E | RCM8 | 1.19/0.92 | 2.50/2.64 |
Meteological Stations (MS) | Criteria | Best RCM | Worst RCM | Takagi-Sugeno (TS) Fuzzy Rule-Based (FRB) | MLR | SEM | ||
---|---|---|---|---|---|---|---|---|
Model | Value | Model | Value | |||||
17190 | Correlation (corr) | RCM3 | 0.29 1 | RCM2 | 0.12 | 0.40 1 | 0.40 1 | 0.40 1 |
Root mean square error (RMSE) | RCM4 | 0.96 | RCM2 | 2.21 | 0.78 1 | 0.80 | 0.85 | |
Percent bias (PBias) | RCM8 | −2.88 1 | RCM2 | −116 | 10.16 | 7.16 | 10.34 | |
17320 | corr | RCM5 | 0.53 | RCM2 | 0.26 | 0.65 1 | 0.62 | 0.62 |
RMSE | RCM8 | 3.12 | RCM7 | 3.86 | 2.67 1 | 2.81 | 2.81 | |
PBias | RCM8 | 3.12 1 | RCM7 | 3.86 | 17.7 | 23.91 | 16.88 |
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Akgun, O.B.; Kentel, E. Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model. Eng. Proc. 2021, 5, 48. https://doi.org/10.3390/engproc2021005048
Akgun OB, Kentel E. Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model. Engineering Proceedings. 2021; 5(1):48. https://doi.org/10.3390/engproc2021005048
Chicago/Turabian StyleAkgun, O. Burak, and Elcin Kentel. 2021. "Ensemble Precipitation Estimation Using a Fuzzy Rule-Based Model" Engineering Proceedings 5, no. 1: 48. https://doi.org/10.3390/engproc2021005048