Daily rainfall forecasting using an ensemble technique based on singular spectrum analysis

Author

Masulli, Francesco ; Baratta, Daniela ; Cicioni, Giovambattista ; Studer, Léonard

Author_Institution

Dipartimento di Inf. e Sci. dell´´Inf., Genoa Univ., Italy

Volume

1

fYear

2001

fDate

2001

Firstpage

263

Abstract

Studer and Masulli (1995), Masulli, Parenti, and Studer (1999), and Masulli, Cicione, and Studer (2000) proposed a constructive methodology for temporal data learning supported by results and prescriptions related to the Takens-Mane theorem and using the singular spectrum analysis in order to reduce the effects of the possible discontinuity of the signal. In this paper we present some new results concerning the application of this approach to the forecasting of the individual rainfall intensities series collected by 135 stations distributed in the Tiber basin

Keywords

learning (artificial intelligence); multilayer perceptrons; rain; weather forecasting; Takens-Mane theorem; Tiber basin; daily rainfall forecasting; ensemble technique; individual rainfall intensities series; singular spectrum analysis; temporal data learning; Delay; Fuzzy neural networks; Inverse problems; Machine learning; Mutual information; Rain; Rivers; Signal analysis; Signal design; Signal mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939028

Filename

939028