Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia

Bodri, L.; Čermák, Vladimír

doi:10.1016/s0965-9978(99)00063-0

Cited by 81 publications

(38 citation statements)

References 21 publications

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“…The ANN type of modelling is relatively easy and is able to reproduce statistical characteristics of observed precipitation sequences. Bodri and Cermak (2000) adopted a time-delay artificial neural network model in which precipitation in a given month was forecasted depending on the previous two months of the current year and on the precipitation of the given month in two previous years. A similar study was later extended to six Czech and four Hungarian meteorological stations by Bodri and Cermak (2001).…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Dahamsheh

Aksoy

2009

Meteorological Applications

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Forecasting monthly precipitation in arid regions is investigated by means of feed forward back propagation (FFBP) artificial neural networks (ANNs) and compared to the linear regression technique with multiple inputs (MLR). Four meteorological stations from different geographical regions in Jordan are selected. The ANNs and MLR processes are analysed based on the mean square error, relative/absolute error, determination coefficient as well as the central statistical moments such as mean, standard deviation, and minimum and maximum values. It is found that whilst on one hand the ANNs are slightly better than the MLR in forecasting the monthly total precipitation, on the other hand, both are found with to have limitations which should be improved by means of either changing the type and architecture of the ANNs or incorporating modelling tools such as Markov chains into the forecast model.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Dahamsheh

Aksoy

2009

Meteorological Applications

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“…ANNs provide one of the nonlinear non-stationary alternative models for drought forecasting and as stipulated by Mishra, Desai (2006). ANNs have several advantages to this end; they can be used to solve problems with nonlinear/unknown multivariate and less controlled environments (Bodri, Cermak 2001 Mishra, Desai (2006) observed that most ANNs' implementations adopted the multilayer feed-forward neural network and were based on BPN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

ITIKI: bridge between African indigenous knowledge and modern science of drought prediction

Masinde

2011

Knowledge Management for Development Journal

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“…Pan et al: Hybrid neural networks in inundation forecasting Artificial neural networks (ANNs) have become an attractive inductive approach in hydrological forecasting because of their flexibility and data-driven learning in building models, as well as their tolerance of inputs with error and time-saving calculation in real-time models (Thirumalaiah and Deo, 1998;Kisi and Kerem Cigizoglu, 2007). Although many studies have applied different ANNs to achieve the prediction and forecasting of various water resource aspects (Maier and Dandy, 2000;Toth et al, 2000;Bodria andČermák, 2000;Kim and Barros, 2001;Wei et al, 2002;Pan and Wang, 2004;Kerh and Lee, 2006;Dawson et al, 2006;Kisi and Kerem Cigizoglu, 2007;Chau, 2007;Chen and Yu, 2007;Goswami and O'Connor, 2007;Pan et al, 2008), few investigations have utilized ANNs to achieve rainfall-inundation forecasting, which is essential to providing real-time flood warning information in emergency responses, as stated previously. An algorithm must be developed to perform realtime calculations for inundation forecasting as fast as it receives the observed rainfall records.…”

Section: Introductionmentioning

confidence: 99%

Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database

Pan

Lai

Chang

et al. 2011

Nat. Hazards Earth Syst. Sci.

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Abstract. This study attempts to achieve real-time rainfallinundation forecasting in lowland regions, based on a synthetic potential inundation database. With the principal component analysis and a feed-forward neural network, a rainfall-inundation hybrid neural network (RiHNN) is proposed to forecast 1-h-ahead inundation depth as hydrographs at specific representative locations using spatial rainfall intensities and accumulations. A systematic procedure is presented to construct the RiHNN, which combines the merits of detailed hydraulic modeling in flood-prone lowlands via a two-dimensional overland-flow model and time-saving calculation in a real-time rainfall-inundation forecasting via ANN model. Analytical results from the RiHNNs with various principal components indicate that the RiHNNs with fewer weights can have about the same performance as a feed-forward neural network. The RiHNNs evaluated through four types of real/synthetic rainfall events also show to fit inundation-depth hydrographs well with high rainfall. Moreover, the results of real-time rainfall-inundation forecasting help the emergency manager set operational responses, which are beneficial for flood warning preparations.

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Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia

Cited by 81 publications

References 21 publications

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

Artificial neural network models for forecasting intermittent monthly precipitation in arid regions

ITIKI: bridge between African indigenous knowledge and modern science of drought prediction

Hybrid neural networks in rainfall-inundation forecasting based on a synthetic potential inundation database

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