Application of an artificial neural network to typhoon rainfall forecasting

Lin, Gwo‐Fong; Chen, Lu-Hsien

doi:10.1002/hyp.5638

Cited by 67 publications

(49 citation statements)

References 13 publications

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“…Olsson et al (2004) applied wind speed and humidity data reproduced by atmospheric numerical modeling to generate regional rainfall using an ANN approach. For typhoon rainfall forecasting, Lin and Chen (2005) and Lin et al (2009) wind as BPNN's inputs to forecast one-hour-ahead typhoon rainfall. In this study, we improve the TRCM rainfall prediction with the SW monsoon enhancement based on the ANN algorithm.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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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“…These three models were developed using multilayer feed forward neural network, Elman partial recurrent neural network and time delay neural network. Lin et al in used a neural network with two hidden layers to predict the storm rainfall which produced decent results [11]. A rainfall prediction model for long monsoons in Kerela was developed by Krishnakutty which produced results better than the statistical methods used [12].…”

Section: Previous Workmentioning

confidence: 99%

Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Dubey¹

2015

IJCA

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Rainfall forecasting plays an important role in catchment management applications, the flood warning system being one of them. Rainfall forecasting is one of the most difficult tasks given the variability of space, time and other given conditions change rapidly. Over the years, with the evolution of the intelligent computing methods, many rainfall prediction methods have been proposed, Artificial Neural Network being one of the most prominent. Since the last decade, many researchers have proposed different artificial neural network models in order to create accurate rainfall prediction models. In this paper, different artificial neural networks have been created for the rainfall prediction of Pondicherry, a coastal region in India. These ANN models were created using three different training algorithms namely, feed-forward back propagation algorithm, layer recurrent algorithm and feedforward distributed time delay algorithm. The number of neurons for all the models was kept at 20. The mean squared error was measured for each model and the best accuracy was obtained by feed-forward distributed time delay algorithm with MSE value as low as .0083.

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“…The ANN is an alternative modelling and simulation tool, especially for dynamic nonlinear systems (Coppola et al, 2003a(Coppola et al, ,b, 2005(Coppola et al, , 2007Becker et al, 2006;Feng et al, 2008). Recently, many researchers have successfully applied ANN models in hydrologic modelling, such as typhoon rainfall forecasting (Lin and Chen, 2005), the determination of aquifer parameters (Samani et al, 2007), and regional ground water levels simulation (Coppola et al, 2003a(Coppola et al, ,b, 2005Feng et al, 2008). A number of studies combine the optimization model with ANN (Rogers and Dowla, 1994;Rogers et al, 1995;Johnson and Rogers, 2001;Rao et al, 2003Rao et al, , 2005.…”

Section: Introductionmentioning

confidence: 98%

Optimal control algorithm and neural network for dynamic groundwater management

Chu¹,

Chang²

2009

Hydrological Processes

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Abstract:Researchers have found that obtaining optimal solutions for groundwater resource-planning problems, while simultaneously considering time-varying pumping rates, is a challenging task. This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) as simulation-optimization model, called ANN-CDDP. Optimal solutions for a groundwater resource-planning problem are determined while simultaneously considering time-varying pumping rates. A trained ANN is used as the transition function to predict ground water table under variable pumping conditions. The results show that the ANN-CDDP reduces computational time by as much as 94Ð5% when compared to the time required by the conventional model. The proposed optimization model saves a considerable amount of computational time for solving large-scale problems.

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Application of an artificial neural network to typhoon rainfall forecasting

Cited by 67 publications

References 13 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

Artificial Neural Network Models for Rainfall Prediction in Pondicherry

Optimal control algorithm and neural network for dynamic groundwater management

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