Forecasting Monsoon Precipitation Using Artificial Neural Networks

Wu, Xiaodan; Cao, Hai-Xia; Flitman, Andrew; Wei, Fengying; Feng, Guolin

doi:10.1007/bf03403515

Cited by 16 publications

(2 citation statements)

References 3 publications

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“…Each input set of attribute values may comprise lagged rainfall values, or other lagged climate-related values, for example, the Southern Oscillation Index (SOI) or a combination of both. Wu et al (2001) generated forecasts for monsoon rainfall in China up to 10 years in advance using only historical rainfall data as input. Philip and Joseph (2003) forecast monthly rainfall for Kerala State in the southern part of the Indian Peninsula with only historical monthly rainfall data.…”

Section: Function Modelsmentioning

confidence: 99%

Application of artificial neural networks to rainfall forecasting in Queensland, Australia

2012

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In this study, the application of artificial intelligence to monthly and seasonal rainfall forecasting in Queensland, Australia, was assessed by inputting recognized climate indices, monthly historical rainfall data, and atmospheric temperatures into a prototype stand-alone, dynamic, recurrent, time-delay, artificial neural network. Outputs, as monthly rainfall forecasts 3 months in advance for the period 1993 to 2009, were compared with observed rainfall data using time-series plots, root mean squared error (RMSE), and Pearson correlation coefficients. A comparison of RMSE values with forecasts generated by the Australian Bureau of Meteorology's Predictive Ocean Atmosphere Model for Australia (POAMA)-1.5 general circulation model (GCM) indicated that the prototype achieved a lower RMSE for 16 of the 17 sites compared. The application of artificial neural networks to rainfall forecasting was reviewed. The prototype design is considered preliminary, with potential for significant improvement such as inclusion of output from GCMs and experimentation with other input attributes.

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Section: Function Modelsmentioning

confidence: 99%

Application of artificial neural networks to rainfall forecasting in Queensland, Australia

2012

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“…as input variables in Korea and Australia, respectively. In China, several works based on ANNs have been developed using long-term historical datasets [3,36,37]. Moreover, similar models have been applied in different Indian regions [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

Estévez

Bellido-Jiménez

Liu

et al. 2020

Water

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Accurate forecast of hydrological data such as precipitation is critical in order to provide useful information for water resources management, playing a key role in different sectors. Traditional forecasting methods present many limitations due to the high-stochastic property of precipitation and its strong variability in time and space: not identifying non-linear dynamics or not solving the instability of local weather situations. In this work, several alternative models based on the combination of wavelet analysis (multiscalar decomposition) with artificial neural networks have been developed and evaluated at sixteen locations in Southern Spain (semiarid region of Andalusia), representative of different climatic and geographical conditions. Based on the capability of wavelets to describe non-linear signals, ten wavelet neural network models (WNN) have been applied to predict monthly precipitation by using short-term thermo-pluviometric time series. Overall, the forecasting results show differences between the ten models, although an effective performance (i.e., correlation coefficients ranged from 0.76 to 0.90 and Root Mean Square Error values ranged from 6.79 to 29.82 mm) was obtained at each of the locations assessed. The most appropriate input variables to obtain the best forecasts are analyzed, according to the geo-climatic characteristics of the sixteen sites studied.

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Predicting summer rainfall in the Yangtze River basin with neural networks

Hartmann

Becker

King

2007

Intl Journal of Climatology

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Summer rainfall in the Yangtze River basin is predicted using neural network techniques. Input variables (predictors) for the neural network are the Southern Oscillation Index (SOI), the East Atlantic/Western Russia (EA/WR) pattern, the Scandinavia (SCA) pattern, the Polar/Eurasia (POL) pattern and several indices calculated from sea surface temperatures (SST), sea level pressures (SLP) and snow data from December to April for the period from 1993 to 2002. The output variable of the neural network is rainfall from May to September for the period from 1994 to 2002, which was previously classified into six different regions by means of a principal component analysis (PCA). Rainfall is predicted from May to September 2002.The winter SST and SLP indices are identified to be the most important predictors of summer rainfall in the Yangtze River basin. The Tibetan Plateau snow depth, the SOI and the other teleconnection indices seem to be of minor importance for an accurate prediction. This may be the result of the length of the available time series, which does not allow a deeper analysis of the impact of multi‐annual oscillations.The neural network algorithms proved to be capable of explaining most of the rainfall variability in the Yangtze River basin. For five out of six regions, our predictions explain at least 77% of the total variance of the measured rainfall. Copyright © 2007 Royal Meteorological Society

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Forecasting Monsoon Precipitation Using Artificial Neural Networks

Cited by 16 publications

References 3 publications

Application of artificial neural networks to rainfall forecasting in Queensland, Australia

Application of artificial neural networks to rainfall forecasting in Queensland, Australia

Monthly Precipitation Forecasts Using Wavelet Neural Networks Models in a Semiarid Environment

Predicting summer rainfall in the Yangtze River basin with neural networks

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