| Scopus记录号: | 2-s2.0-85047189382
|
| 论文题名: | Rainfall prediction methodology with binary multilayer perceptron neural networks |
| 作者: | Esteves J.T.; de Souza Rolim G.; Ferraudo A.S.
|
| 刊名: | Climate Dynamics
|
| ISSN: | 9307575
|
| 出版年: | 2019
|
| 卷: | 52, 期:2019-03-04 | | 起始页码: | 2319
|
| 结束页码: | 2331
|
| 语种: | 英语
|
| 英文关键词: | Artificial neural networks
; Multilayer perceptron
; Rainfall forecasting
|
| 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 softcomputing technique to forecast the occurrence of rainfall in short ranges of time by artificial neural networks (ANNs) in accumulated periods from 3 to 7 days 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 weredeveloped 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 60 years 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 effectof 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. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. |
| 资源类型: | 期刊论文
|
| 标识符: | http://119.78.100.158/handle/2HF3EXSE/122404
|
| Appears in Collections: | 气候变化事实与影响
|
|
There are no files associated with this item.
|
|
作者单位: | UNESP, Jaboticabal, Brazil; Departamento de Ciências Exatas Via de Acesso Prof. Paulo Donato Castellane s/n, Jaboticabal, SP 14884-900, Brazil
|
| Recommended Citation: | |
Esteves J.T.,de Souza Rolim G.,Ferraudo A.S.. Rainfall prediction methodology with binary multilayer perceptron neural networks[J]. Climate Dynamics,2019-01-01,52(2019-03-04)
|
|
|