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

Pan, Tsung-Yi; Lai, Jihn‐Sung; Chang, Tsang‐Jung; Chang, Hao-Chen; Chang, Kai-Shiun; Tan, Yih–Chi

doi:10.5194/nhess-11-771-2011

Cited by 26 publications

(14 citation statements)

References 42 publications

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“…On the other hand, when the test case is removed from the training set, the prediction is still better than the original TRCM prediction, in that the amount of rainfall as well as the multiple peak of rainfall is predicted. The performances of original TRCM prediction and TRCM prediction with ANNSME are further evaluated by root mean square error (RMSE), error of cumulative rainfall (ECR), and error of the time for peak to arrive (ET p ) in Table 5 (Pan et al 2011). We found that the RMSE and ECR are improved through TRCM prediction with ANNSME in all cases except the extreme rainfall case at ALiShan in Typhoon Morakot.…”

Section: The Annsme Resultsmentioning

confidence: 92%

Improvement of Statistical Typhoon Rainfall Forecasting with ANN-Based Southwest Monsoon Enhancement

Pan¹,

Yang²,

Kuo³

et al. 2011

Terr. Atmos. Ocean. Sci.

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Typhoon Morakot 2009, with significant southwest monsoon flow, produced a record-breaking rainfall of 2361 mm in 48 hours. This study hopes to improve a statistical typhoon rainfall forecasting method used over the mountain region of Taiwan via an artificial neural network based southwest monsoon enhancement (ANNSME) model. Rainfall data collected at two mountain weather stations, ALiShan and YuShan, are analyzed to establish the relation to the southwest monsoon moisture flux which is calculated at a designated sea area southwest of Taiwan. The results show that the moisture flux, with southwest monsoon flow, transported water vapor during the landfall periods of Typhoons Mindulle, Bilis, Fungwong, Kalmaegi, Haitaing and Morakot. Based on the moisture flux, a linear regression is used to identify an effective value of moisture flux as the threshold flux which can enhance mountain rainfall in southwestern Taiwan. In particular, a feedforward neural network (FNN) is applied to estimate the residuals from the linear model to the differences between simulated rainfalls by a typhoon rainfall climatology model (TRCM) and observations. Consequently, the ANNSME model integrates the effective moisture flux, linear rainfall model and the FNN for residuals. Even with very limited training cases, our results indicate that the ANNSME model is robust and suitable for improvement of TRCM rainfall prediction. The improved prediction of the total rainfall and of the multiple rainfall peaks is important for emergency operation.

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Section: The Annsme Resultsmentioning

confidence: 92%

Improvement of Statistical Typhoon Rainfall Forecasting with ANN-Based Southwest Monsoon Enhancement

Pan¹,

Yang²,

Kuo³

et al. 2011

Terr. Atmos. Ocean. Sci.

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“…Leedal et al (2010) proposed twodimensional (2-D) real-time probabilistic inundation maps based on a modified Kalman filter model coupled into 2-D hydrodynamic model to compute the maximum multistepahead inundation extent. Pan et al (2011) used hybrid ANNs in rainfall-inundation forecasting to estimate 1-h-ahead inundation depths.…”

Section: Introductionmentioning

confidence: 99%

Online multistep-ahead inundation depth forecasts by recurrent NARX networks

Shen

Chang

2013

Hydrol. Earth Syst. Sci.

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Abstract. Various types of artificial neural networks (ANNs)have been successfully applied in hydrological fields, but relatively scant on multistep-ahead flood inundation forecasting, which is very difficult to achieve, especially when dealing with forecasts without regular observed data. This study proposes a recurrent configuration of nonlinear autoregressive with exogenous inputs (NARX) network, called R-NARX, to forecast multistep-ahead inundation depths in an inundation area. The proposed R-NARX is constructed based on the recurrent neural network (RNN), which is commonly used for modeling nonlinear dynamical systems. The models were trained and tested based on a large number of inundation data generated by a well validated two-dimensional simulation model at thirteen inundation-prone sites in Yilan County, Taiwan. We demonstrate that the R-NARX model can effectively inhibit error growth and accumulation when being applied to online multistep-ahead inundation forecasts over a long lasting forecast period. For comparison, a feedforward time-delay and an online feedback configuration of NARX networks (T-NARX and O-NARX) were performed. The results show that (1) T-NARX networks cannot make online forecasts due to unavailable inputs in the constructed networks even though they provide the best performances for reference only; and (2) R-NARX networks consistently outperform O-NARX networks and can be adequately applied to online multistep-ahead forecasts of inundation depths in the study area during typhoon events.

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“…Artificial neural networks (ANNs) are a kind of information processing system with great flexib ility in modeling nonlinear systems. As to inundation forecasting, applications of NNs have been presented (Chang et al [3]; Pan et al [7]). The ASCE Task Co mmittee [1,2] and Maier and Dandy [6] have presented comprehensive reviews of the applications of ANNs in hydrology.…”

Section: Introductionmentioning

confidence: 99%

Effective real-time forecasting of inundation maps for early warning systems during typhoons

2018

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Abstract. Accurate forecasts of hourly inundation depths are essential for inundation warning and mitigation during typhoons. In this paper, an effective forecasting model is proposed to yield 1-to 6-h leadtime inundation maps for early warning systems during typhoons. The proposed model based on Support Vector M achine (SVM ) is composed of two modules, point forecasting and spatial expansion. In the first module, the rainfall intensity, inundation depth, cumulative rainfall and forecasted inundation depths are considered as model input for point forecasting. In the second module, the geographic information of inundation grids and the inundation forecasts of reference points are used to yield inundation maps for spatial expansion. The results show that the proposed model is able to provide accurate point forecasts at each inundation point. M oreover, the spatial expansion module is capable of producing accurate spatial inundation forecasts. Obviously, the proposed model provides reasonable spatial inundation forecasts, and is able to deal with the nonlinear relationships between inputs and desired output. In conclusion, the proposed model is suitable and useful for inundation forecasting.

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

Cited by 26 publications

References 42 publications

Improvement of Statistical Typhoon Rainfall Forecasting with ANN-Based Southwest Monsoon Enhancement

Improvement of Statistical Typhoon Rainfall Forecasting with ANN-Based Southwest Monsoon Enhancement

Online multistep-ahead inundation depth forecasts by recurrent NARX networks

Effective real-time forecasting of inundation maps for early warning systems during typhoons

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