Statistical aspects of hydrology in the Amazon river basin / Corina da Costa Freitas Yanasse.
Detalhes da publicação: Massachusetts 1979Notas: 103 f. : il., mapaAssunto(s): Análise de séries temporais | Hidrologia -- Amazonas, Rio, Bacia -- Modelos matemáticos | Hidrologia -- Negro, Rio, Bacia -- Modelos matemáticosClassificação Decimal de Dewey: 551.48 Nota de dissertação: Tese (M.S.) Massachusetts Institute of Technology, 1979 Sumário: The prediction of the Negro River height at Manaus, Brasil, has very important economical interests, mainly for the people living in that region. This work is an attempt to find a model which explains the heights of the Negro River. The Negro River height data is checked for nonstationarities over the record lengyh. The procedure used to find the model involves an adjustment of the series by the seasonal means and standard deviations. Some models were proposed and comparisons for predicting river heights for one, two and threes steps ahead were done. The chosen model was an autoregressive model in which the coefficients vary with each month. The seasonal autoregressive model is compared with other suggested models in the literature.| Tipo de material | Biblioteca atual | Setor | Classificação | Situação | Previsão de devolução | Código de barras |
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Dissertação | T 551.48 Y21s (Percorrer estante(Abre abaixo)) | Disponível | 00-0399 |
Tese (M.S.) Massachusetts Institute of Technology, 1979
The prediction of the Negro River height at Manaus, Brasil, has very important economical interests, mainly for the people living in that region. This work is an attempt to find a model which explains the heights of the Negro River. The Negro River height data is checked for nonstationarities over the record lengyh. The procedure used to find the model involves an adjustment of the series by the seasonal means and standard deviations. Some models were proposed and comparisons for predicting river heights for one, two and threes steps ahead were done. The chosen model was an autoregressive model in which the coefficients vary with each month. The seasonal autoregressive model is compared with other suggested models in the literature.
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