Sewerage Stormwater Inflow Prediction by Using System Identification Method with Hammerstein Type of Nonlinear Model

Yamanaka, Osamu; Nagaiwa, Akihiro; Matsubara, Shinichiro; Nakada, Masajiro; Yamada, Fumio

doi:10.1541/ieejias.120.566

Cited by 1 publication

(2 citation statements)

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“…The structure of prediction model is based on the Hammerstein model, which consists of a static nonlinearity followed by a linear transfer function. The advantage of adopting the Hammerstein model is that the model can represent the nonlinear phenomena of the rainfall-runoff process in comparison with linear transfer function models such as Auto Regressive model (AR) and/or Auto Regressive with eXogenous (ARX) model that are usually adopted as a black box model (Yamanaka et al 2000). Figure 3 shows the Hammerstein model.…”

Section: Stormwater Inflow Prediction Based On the Hammerstein Modelmentioning

confidence: 99%

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Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

Katayama

Kimijima

Yamanaka

et al. 2006

Water Practice and Technology

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This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

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Section: Stormwater Inflow Prediction Based On the Hammerstein Modelmentioning

confidence: 99%

“…Figure 2 shows the stormwater inflow prediction system using the method proposed in this paper. A prediction model based on the Hammerstein type of nonlinear model is constructed off-line with system identification (Yamanaka et al, 2000). PCA is performed in order to reduce the number of variables of radar rainfall data.…”

Section: Introductionmentioning

confidence: 99%