Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-018-4252-x |
Rainfall prediction methodology with binary multilayer perceptron neural networks | |
Esteves, Joao Trevizoli1; Rolim, Glauco de Souza2; Ferraudo, Antonio Sergio2 | |
2019-02-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 52页码:2319-2331 |
文章类型 | Article |
语种 | 英语 |
国家 | Brazil |
英文摘要 | Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This paper presents a soft computing technique to forecast the occurrence of rainfall in short ranges of time by artificial neural networks (ANNs) in accumulated periods from 3 to 7days for each climatic season, mitigating the necessity of predicting its amount. With this premise it is intended to reduce the variance, rise the bias of data and lower the responsibility of the model acting as a filter for quantitative models by removing subsequent occurrences of zeros values of rainfall which leads to bias the and reduces its performance. The model were developed with time series from ten agriculturally relevant regions in Brazil, these places are the ones with the longest available weather time series and and more deficient in accurate climate predictions, it was available 60years of daily mean air temperature and accumulated precipitation which were used to estimate the potential evapotranspiration and water balance; these were the variables used as inputs for the ANNs models. The mean accuracy of the model for all the accumulated periods were 78% on summer, 71% on winter 62% on spring and 56% on autumn, it was identified that the effect of continentality, the effect of altitude and the volume of normal precipitation, have an direct impact on the accuracy of the ANNs. The models have peak performance in well defined seasons, but looses its accuracy in transitional seasons and places under influence of macro-climatic and mesoclimatic effects, which indicates that this technique can be used to indicate the eminence of rainfall with some limitations. |
英文关键词 | Artificial neural networks Rainfall forecasting Multilayer perceptron |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000460902200059 |
WOS关键词 | PRECIPITATION ; MODEL ; RADIATION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/35595 |
专题 | 气候变化 |
作者单位 | 1.UNESP, Jaboticabal, Brazil; 2.Dept Ciencias Exatas, Via Acesso Prof Paulo Donato Castellane S-N, BR-14884900 Jaboticabal, SP, Brazil |
推荐引用方式 GB/T 7714 | Esteves, Joao Trevizoli,Rolim, Glauco de Souza,Ferraudo, Antonio Sergio. Rainfall prediction methodology with binary multilayer perceptron neural networks[J]. CLIMATE DYNAMICS,2019,52:2319-2331. |
APA | Esteves, Joao Trevizoli,Rolim, Glauco de Souza,&Ferraudo, Antonio Sergio.(2019).Rainfall prediction methodology with binary multilayer perceptron neural networks.CLIMATE DYNAMICS,52,2319-2331. |
MLA | Esteves, Joao Trevizoli,et al."Rainfall prediction methodology with binary multilayer perceptron neural networks".CLIMATE DYNAMICS 52(2019):2319-2331. |
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