The Research on Application of Artificial Neural Networks in Real-time Typhoon Rainfall Forecasting
Date Issued
2010
Date
2010
Author(s)
Lo, Chun-Wen
Abstract
Taiwan is located in northwest side of the Pacific Ocean within the moving path of typhoons, and the slope of rivers is steep. Therefore the typhoons often cause a lot of damages of lives and properties. It is an important subject to provide information about typhoon rainfall forecasting for the flood warning and the flood control of reservoir operation system. The rainfalls in the typhoon period are uncertain and chaotic phenomena. Adaptive Network-based Fuzzy Inference System has the ability of learning and fuzzy logic reasoning, and it may be useful for the stochastic relationship between rainfalls forecasting and activities of the atmosphere. Therefore this study invents the best way to optimize ANFIS’s parameter and structures to forecast the typhoon rainfalls of 1 to 6 hours for future periods, and then the results are compared with the values of Back Propagation Neural Networks method. This study used a variety of correlation between parameters and rainfalls to determine the most appropriate input values of neural network. In order to improve the precision of rainfall forecasting, this study uses two methods: the first method is a single-model established with the smallest error; the second method is a dual-model contains higher and lower rainfall forecasting models, and then the superiority between these two models are discussed. In order to reform the efficiency and accuracy of ANFIS rainfall forecasting model, the tabu search and subtractive clustering method are used to determine the best ANFIS structure, and the solutions of tabu search method are expected better than the trial and error method. In addition, the coupling methods are embedded in the rainfall forecasting model to improve the accuracy of long-term forecasting. Finally, the frameworks of these four models, BPN coupling, BPN non-coupling, ANFIS coupling, ANFIS non-coupling with long-term and short-term forecasting are compared with each others.
In this research, the study area are Shihmen Reservoir, and periods are from AD 2001 to 2009. To forecasting rainfall at Yufeng and Siayun rainfall stations ahead from 1 hour to 6 hours. Results show that combined with tabu search and subtractive clustering to optimal parameters ANFIS structure, dual-model and coupling methods for the typhoon rainfall forecasting , receives the most accurate, fastest and stable prediction results.
Subjects
typhoon rainfall forecasting
Adaptive Network-based Fuzzy Inference System
Back Propagation Neural Networks
optimization of parameter and structure
tabu search
subtractive clustering
File(s)![Thumbnail Image]()
Loading...
Name
ntu-99-R97521318-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):3b3ffcff303209c52cdc46c13da45d0a