Title :
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
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;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939028
