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Feature analysis and prediction of ice regime in the source region of the Yellow River

du, Yiheng LU ; Hao, Zhenchun and Ju, Qin (2013)
Abstract
The Yellow River is a river where serious ice disasters frequently take place in winter. In recent years, the stable frozen period has decreased and the frequency of intermittent freeze periods has increased. After analysing the main factors influencing the ice regime, the prediction factors can be selected. Using multiple linear regression (MLR) and artificial neural network (ANN) methods, this paper sets up two models for the freeze-up and break-up date prediction. In the MLR model, stepwise regression analysis is used to select the highly-related factors into the prediction equation. In the ANN model, a multilayer preceptor in SPSS, a statistical analysis software named Statistical Product and Service Solutions, is used to set up... (More)
The Yellow River is a river where serious ice disasters frequently take place in winter. In recent years, the stable frozen period has decreased and the frequency of intermittent freeze periods has increased. After analysing the main factors influencing the ice regime, the prediction factors can be selected. Using multiple linear regression (MLR) and artificial neural network (ANN) methods, this paper sets up two models for the freeze-up and break-up date prediction. In the MLR model, stepwise regression analysis is used to select the highly-related factors into the prediction equation. In the ANN model, a multilayer preceptor in SPSS, a statistical analysis software named Statistical Product and Service Solutions, is used to set up topology between input factors and output date. In conclusion, a comparison is made between the results of the two different methods. The ANN model performs better than the MLR model. (Less)
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Cold and mountain region hydrological systems under climate change: towards improved projections : Proceedings of H02, IAHS -IAPSO -IASPEI Assembly - Proceedings of H02, IAHS -IAPSO -IASPEI Assembly
pages
7 pages
publisher
IAHS
external identifiers
  • scopus:84900419434
language
English
LU publication?
no
id
269431df-c76b-4f0f-bd6e-e49febb1072a
date added to LUP
2019-09-24 12:01:05
date last changed
2022-02-01 01:02:18
@inproceedings{269431df-c76b-4f0f-bd6e-e49febb1072a,
  abstract     = {{The Yellow River is a river where serious ice disasters frequently take place in winter. In recent years, the stable frozen period has decreased and the frequency of intermittent freeze periods has increased. After analysing the main factors influencing the ice regime, the prediction factors can be selected. Using multiple linear regression (MLR) and artificial neural network (ANN) methods, this paper sets up two models for the freeze-up and break-up date prediction. In the MLR model, stepwise regression analysis is used to select the highly-related factors into the prediction equation. In the ANN model, a multilayer preceptor in SPSS, a statistical analysis software named Statistical Product and Service Solutions, is used to set up topology between input factors and output date. In conclusion, a comparison is made between the results of the two different methods. The ANN model performs better than the MLR model.}},
  author       = {{du, Yiheng and Hao, Zhenchun and Ju, Qin}},
  booktitle    = {{Cold and mountain region hydrological systems under climate change: towards improved projections : Proceedings of H02, IAHS -IAPSO -IASPEI Assembly}},
  language     = {{eng}},
  publisher    = {{IAHS}},
  title        = {{Feature analysis and prediction of ice regime in the source region of the Yellow River}},
  year         = {{2013}},
}