A deterministic linearized recurrent neural network for recognizing the transition of rainfall–runoff processes

Pan, Tsung-Yi; Wang, Ru-Yih; Lai, Jihn‐Sung

doi:10.1016/j.advwatres.2007.02.009

Cited by 15 publications

(12 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tsoi and Back (1997) classified ANNs into two categories, feedforward neural networks (FNNs) and recurrent neural networks (RNNs). Although Pan et al (2007) showed that RNNs perform superiorly for dynamical systems, FNNs is still one of the most popular forms due to its ability of simplifying calculation and enhancing the adaptability. Based on the learning algorithms of ANNs, unsupervised ANNs are applied in classification or clustering while supervised ANNs are adopted as a function approximator.…”

Section: Methodsmentioning

confidence: 99%

“…The connections among neurons determine the dynamics of a network, like data flow. Because ANNs are trained by data-driven learning algorithms, and the data-driven learning algorithms always develop full connections between neurons in different layers (Pan et al 2007), the dynamics of a network could be accounted as the strengths of connections, like the weights in Fig 2. Therefore, the input net of a neuron in the hidden and output layers are described as Eqs. (3) and (4), respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Pan¹,

Yang²,

Kuo³

et al. 2011

Terr. Atmos. Ocean. Sci.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Pan¹,

Yang²,

Kuo³

et al. 2011

Terr. Atmos. Ocean. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The averages of absolute criteria of the DLRNN and FNNs to simulate the rest 28 events (Pan et al, 2007). Table 4 shows the averages of the absolute criteria of the DLRNN and the FNNs in which FNN(1-8-1) is the only neural network without any feedback connection.…”

Section: Input Layermentioning

confidence: 99%

Application of Recurrent Neural Networks to Rainfall-runoff Processes

Pan¹,

Wang²,

Lai³

et al. 2008

Recurrent Neural Networks

Self Cite

View full text Add to dashboard Cite

“…According to the manner of the adjustment to a synaptic weight by various data-driven learning algorithms, ANNs are classified into supervised and unsupervised neural networks. Based on the structures of the connections between neurons, ANNs are grouped into feedforward and recursive neural networks (Pan et al, 2007). As shown in Fig.…”

Section: Hybrid Neural Networkmentioning

confidence: 99%

“…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.

View full text Add to dashboard Cite

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.

show abstract

A deterministic linearized recurrent neural network for recognizing the transition of rainfall–runoff processes

Cited by 15 publications

References 38 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

Application of Recurrent Neural Networks to Rainfall-runoff Processes

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

Contact Info

Product

Resources

About