- Tytuł:
- A rainfall forecasting method using machine learning models and its application to the Fukuoka city case
- Autorzy:
-
Sumi, S. M.
Zaman, M. F.
Hirose, H. - Powiązania:
- https://bibliotekanauki.pl/articles/331290.pdf
- Data publikacji:
- 2012
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
maszyna ucząca się
metoda wielomodelowa
przetwarzanie wstępne
rainfall forecasting
machine learning
multi model method
preprocessing
model ranking - Opis:
- In the present article, an attempt is made to derive optimal data-driven machine learning methods for forecasting an average daily and monthly rainfall of the Fukuoka city in Japan. This comparative study is conducted concentrating on three aspects: modelling inputs, modelling methods and pre-processing techniques. A comparison between linear correlation analysis and average mutual information is made to find an optimal input technique. For the modelling of the rainfall, a novel hybrid multi-model method is proposed and compared with its constituent models. The models include the artificial neural network, multivariate adaptive regression splines, the k-nearest neighbour, and radial basis support vector regression. Each of these methods is applied to model the daily and monthly rainfall, coupled with a pre-processing technique including moving average and principal component analysis. In the first stage of the hybrid method, sub-models from each of the above methods are constructed with different parameter settings. In the second stage, the sub-models are ranked with a variable selection technique and the higher ranked models are selected based on the leave-one-out cross-validation error. The forecasting of the hybrid model is performed by the weighted combination of the finally selected models.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 841-854
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł