Modeling and Short-Term Prediction of Air Pollution by a Revised GMDH
-
- TAMURA Hiroyuki
- Faculty of Engineering, Osaka University
-
- KONDO Tadashi
- Faculty of Engineering, Osaka University
Bibliographic Information
- Other Title
-
- 改良形GMDHによる大気汚染のモデリングと短期予測
- 改良形GMDMによる大気汚染のモデリングと短期予測
- カイリョウガタ GMDM ニヨル タイキ オセン ノ モデリング ト タンキ
Search this article
Abstract
This paper deals with nonlinear statistical modeling of air pollution concentration for short-term prediction. The method used for modeling is a revised version of the basic GMDH (Group Method of Data Handling) algorithm. By using the time series data of the SO2 concentration, the wind velocity and the wind direction in Tokushima, Japan, we intend to find a suitable model for predicting SO2 concentration a few hours in advance. Firstly, the suitable data length for modeling air pollution in Tokushima is investigated. Secondly, three different prediction models obtained by the revised GMDH are compared to find a suitable structure and suitable input variables in the model. The predicted results obtained by the revised GMDH model are compared with the results obtained by a linear regression model, a linear autoregressive model and a basic GMDH model. It is shown that the revised GMDH model developed in this paper gives a better result and compares favorably with linear models and the basic GMDH model. It is also shown that the revised GMDH model obtained is much simpler than the basic GMDH model.
Journal
-
- Transactions of the Society of Instrument and Control Engineers
-
Transactions of the Society of Instrument and Control Engineers 15 (5), 622-627, 1979
The Society of Instrument and Control Engineers
- Tweet
Details 詳細情報について
-
- CRID
- 1390001204502595840
-
- NII Article ID
- 130003789449
-
- NII Book ID
- AN00072392
-
- ISSN
- 18838189
- 04534654
-
- NDL BIB ID
- 2062756
-
- Data Source
-
- JaLC
- NDL
- Crossref
- CiNii Articles
-
- Abstract License Flag
- Disallowed