Some unique characteristics of atmospheric interannual variability in rainfall time series over India and the United Kingdom

Selvam, A. M.; Pethkar, J. S.; Kulkarni, M. K.

doi:10.1007/bf02656987

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Selvam et al, 1995;Sivakumar, 2001;Dix and Hunt, 2007;Chattopadhyay and Chattopadhyay, 2008a) and the suitability of an ANN in dealing with chaotic time series is well established (Prinicipe et al, 1992). In the present study, the ANN approach has been adopted to forecast precipitation in a time step ahead.…”

Section: Introductionmentioning

confidence: 98%

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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