Rainfall forecasting by technological machine learning models.

*(English)*Zbl 1164.86025Summary: Accurate forecasting of rainfall has been one of the most important issues in hydrological research. Due to rainfall forecasting involves a rather complex nonlinear data pattern; there are lots of novel forecasting approaches to improve the forecasting accuracy. Recurrent artificial neural networks (RNNS) have played a crucial role in forecasting rainfall data. Meanwhile, support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. This investigation elucidates the feasibility of hybrid model of RNNs and SVMs, namely RSVR, to forecast rainfall depth values. Moreover, chaotic particle swarm optimization algorithm (CPSO) is employed to choose the parameters of a SVR model. Subsequently, example of rainfall values during typhoon periods from Northern Taiwan is used to illustrate the proposed RSVRCPSO model. The empirical results reveal that the proposed model yields well forecasting performance, RSVRCPSO model provides a promising alternative for forecasting rainfall values.

##### MSC:

86A32 | Geostatistics |

62M10 | Time series, auto-correlation, regression, etc. in statistics (GARCH) |

68T05 | Learning and adaptive systems in artificial intelligence |

90C59 | Approximation methods and heuristics in mathematical programming |

##### Keywords:

rainfall forecasting; support vector regression (SVR); chaotic particle swarm optimization algorithm (CPSO); recurrent SVR; machine learning
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\textit{W.-C. Hong}, Appl. Math. Comput. 200, No. 1, 41--57 (2008; Zbl 1164.86025)

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