A hybrid multi-model approach to river level forecasting
作者:
LINDA SEE,
STAN OPENSHAW,
期刊:
Hydrological Sciences Journal
(Taylor Available online 2000)
卷期:
Volume 45,
issue 4
页码: 523-536
ISSN:0262-6667
年代: 2000
DOI:10.1080/02626660009492354
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
This paper presents four different approaches for integrating conventional and AI-based forecasting models to provide a hybridized solution to the continuous river level and flood prediction problem. Individual forecasting models were developed on a stand alone basis using historical time series data from the River Ouse in northern England. These include a hybrid neural network, a simple rule-based fuzzy logic model, an ARMA model and naive predictions (which use the current value as the forecast). The individual models were then integrated via four different approaches: calculation of an average, a Bayesian approach, and two fuzzy logic models, the first based purely on current and past river flow conditions and the second, a fuzzification of the crisp Bayesian method. Model performance was assessed using global statistics and a more specific flood related evaluation measure. The addition of fuzzy logic to the crisp Bayesian model yielded overall results that were superior to the other individual and integrated approaches.
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