應用自主性演算法與適應性模糊推論系統評估未來降雨趨勢

洪哲縺

doi:10.6840/cycu201500920

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應用自主性演算法與適應性模糊推論系統評估未來降雨趨勢

Applying Group Method of Data Handling and Adaptive Network-Based Fuzzy Inference System to Evaluate the Tendency of Future Rainfall

洪哲縺 , Masters　　Advisor：林旭信　　

繁體中文 DOI： 10.6840/cycu201500920

統計降尺度；主成份分析；全球環流模式；適應性網路模糊推論系統；自組性網路架構； Statistical downscaling ； Principal component analysis ； General circulation model ； Adaptive network fuzzy inference system ； Group method of data handling

Abstract │ Reference (35) │ Altmetrics

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This study develops ASDM, statistical downscaling model based on adaptive network fuzzy inference system (ANFIS), and GSDM, statistical downscaling model based on Group Method of Data Handling (GMDH) to simulate the tendency of future rainfall in Tamsui, Taichung, Kaohsiung, and Hualien, respectively. The input variables of ASDM are determined by using principal component analysis (PCA) for four historical scenario data of global circulation models (GCM); by applying the characteristics of heuristic self-organization of GMDH, the optimal input variables of GSDM are selected automatically as well. The future tendency of rainfall in short term (2020-2039), medium term (2050-2069) and long term (2080-2099) are assessed by applying four GCMs and historical data in Tamsui, Taichung, Kaohsiung, and Hualien station.
The simulated results show that the variation percentage of CGMR is 85.925% by selecting 3 principal components (PC); that of CSMK3 is 84.544% by selecting 4 PC; that of GFCM2.1 is 83.763% by selecting 5 PC; that of MRCGCM is 86.156% by selecting 6 PC. The computation time and complexity of the models can be reduced. For all the models, the performance of GSDM is better than ASDM. The correlation coefficient (CC) of GSDM, which is the best between the models, based on historical scenario 20C3M of CGMR is about 0.72 in Kaohsiung. It also show that CC of rainfall in Kaohsiung are 0.68, 0.61 and 0.57 and the percentage change of average are -24.61%, -27.81% and -28.5% in the short, medium and long-term, respectively. The better percentage changes of average rainfall in Tamsui are 30.2%, 26.71% and 18.26% in summer season and 40.46%, 54.99% and 55.44% in winter season.

Reference ( 35 ) 〈TOP〉

Chu, J. L., Kang, H., Tam, C. T., C. K., Chen, C. T., 2008, “Seasonal Forecast for Local Precipitation over Northern Taiwan Using Statistical Downscaling,” Journal of Geophysical Research Atmospheres, Vol. 113(D12118).
連結：
Ekstro ̈m, M., Fowler, H. J., Kilsby, C. G., and Jones, P. D., 2005, “New Estimates of Future Changes in Extreme Rainfall Across the UK Using Regional Climate Model Integrations,” Journal of Hydrology, Vol. 300 , pp.234-251.
連結：
Hanel, M., Mrkvicková, M., Máca, P., Vizina, A., and Pech, P.: Eval-uation of Simple Statistical Downscaling Methods for Monthly Regional Climate Model Simulations with Respect to the Estimated Changes in Runoff in the Czech Republic, Water Resour. Manag, 27, 5261–5279, doi: 10. 1007/s11269-013-0466-1, 2013.
連結：
IPCC, Climate Change 2007: The Physical Scientific Basis. Contribution of Working GroupⅠto the Fourth Assessment Report of the Integergovernmental Panel on Climate Change, 2007.
連結：
Jacob, T., Usman, U. A., Bemdoo, S., Susan, A. A., 2015, “Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network.” Journal of Electrical and Electronic Engineering, Vol. 3(3), pp.42-47.
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