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Application of artificial neural networks in typhoon surge forecasting

机译：人工神经网络在台风潮预报中的应用

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A typhoon-surge forecasting model was developed with a back-propagation neural network (BPN) in the present paper. The typhoon's characteristics, local meteorological conditions and typhoon surges at a considered tidal station at time t—1 and t were used as input data of the model to forecast typhoon surges at the following time. For the selection of a better forecasting model, four models (Models A-D) were tested and compared under the different composition of the above-mentioned input factors. A general evaluation index that is a composition of four performance indexes was proposed to evaluate the model's overall performance. The result of typhoon-surge forecasting was classified into five grades: A (excellent), B (good), C (fair), D (poor) and E (bad), according to the value of the general evaluation index. Sixteen typhoon events and their corresponding typhoon surges and local meteorological conditions at Ken-fang Tidal Station in the coast of north-eastern Taiwan between 1993 and 2000 were collected, 12 of them were used in model's calibration while the other four were used in model's verification. The analysis of typhoon-surge forecasting results at Ken-fang tidal station show that the Model D composing 18 input factors has better performance, and that it is a suitable BPN-based model in typhoon-surge forecasting. The Model D was also applied to typhoon-surge forecasting at Cheng-kung Tidal Station in south-eastern coast of Taiwan and at Tung-shih Tidal Station in the coast of south-western Taiwan. Results show that the application of Model D in typhoon-surge forecasting at Cheng-kung Tidal Station has better performance than that at Tung-shih Tidal Station.
机译：本文利用反向传播神经网络（BPN）建立了台风潮预报模型。在t-1和t时刻，在某个潮汐站的台风特征，当地气象条件和台风潮被用作模型的输入数据，以预测随后的台风潮。为了选择更好的预测模型，测试了四个模型（模型A-D），并在上述输入因素的不同构成下进行了比较。提出了由四个性能指标组成的通用评估指标，以评估模型的整体性能。根据综合评价指标的值，将台风潮预报的结果分为五个等级：A（优秀），B（良好），C（一般），D（差）和E（差）。收集了1993年至2000年台湾东北沿海垦芳潮汐站的16次台风事件及其相应的台风潮汐和当地气象条件，其中12次用于模型定标，另外4个用于模型验证。 。对垦房潮汐站台风潮预报结果的分析表明，由18个输入因子组成的D型模型具有较好的性能，是基于BPN的台风潮预报模型。 D型还用于台湾东南沿海的郑宫潮汐站和台湾西南沿海的东石潮汐站的台风潮预报。结果表明，D型模型在郑公滩站台风潮预报中的应用要好于东石滩站。

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