Prediction of daily precipitation using wavelet—neural networks

Partal, Turgay; Ciğizoğlu, H. Kerem

doi:10.1623/hysj.54.2.234

Cited by 84 publications

(32 citation statements)

References 29 publications

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“…They recommended further researches on investigating the method for reasonable selection of resolution level, which is an important parameter in WNM. Partal & Cigizoglu (2009) predict the daily precipitation from meteorological data from Turkey using the wavelet-neural network method. The new approach in estimating the peak values showed a noticeably high positive effect on the performance evaluation criteria.…”

Section: Introductionmentioning

confidence: 99%

A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Tiwari

Chatterjee

2010

Journal of Hydroinformatics

128

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A new hybrid model, the wavelet-bootstrap-ANN (WBANN), for daily discharge forecasting is proposed in this study. The study explores the potential of wavelet and bootstrapping techniques to develop an accurate and reliable ANN model. The performance of the WBANN model is also compared with three more models: traditional ANN, wavelet-based ANN (WANN) and bootstrapbased ANN (BANN). Input vectors are decomposed into discrete wavelet components (DWCs) using discrete wavelet transformation (DWT) and then appropriate DWCs sub-series are used as inputs to the ANN model to develop the WANN model. The BANN model is an ensemble of several ANNs built using bootstrap resamples of raw datasets, whereas the WBANN model is an ensemble of several ANNs built using bootstrap resamples of DWCs instead of raw datasets. The results showed that the hybrid models WBANN and WANN produced significantly better results than the traditional ANN and BANN, whereas the BANN model is found to be more reliable and consistent. The WBANN and WANN models simulated the peak discharges better than the ANN and BANN models, whereas the overall performance of WBANN, which uses the capabilities of both bootstrap and wavelet techniques, is found to be more accurate and reliable than the remaining three models.

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Section: Introductionmentioning

confidence: 99%

A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Tiwari

Chatterjee

2010

Journal of Hydroinformatics

128

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“…Many investigators have proved that ANN worked better than statistical model or even ANN (Partal and Cigizoglu 2009;Deka and Prahlada 2012). Also NWT models were found to remove the phase lag in univariate time series forecasts which is highly advantageous over similar ANN or other data driven models.…”

Section: Resultsmentioning

confidence: 99%

Review of Applications of Neuro-Wavelet Techniques in Water Flows

Dixit

Londhe²,

Deo

2016

INAE Lett

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The flow of water over land and oceans is governed by the natural process of hydrological cycle and hence it is highly random and complex. An estimation or prediction of behavior of such flows is thus very difficult and requires continuous research with latest tools of analysis and forecasting. The methods adopted for this purpose range from earlier empirical equations and numerical methods to modern data driven techniques which follow advances in computer technology. The latter coupled with progress in neuro-science had given rise to the method of artificial neural networks (ANNs) which has been applied abundantly since late 1980s to carry out system modeling and prediction type of exercises in water resources and ocean engineering. Despite wide spread applications of ANNs issues such as non-linearity, non-homogenity and non-stationarity of given information continue to elude high accuracy in the results and thus have prompted extension of conventional ANNs to their combination with other pre-or post-processing tools. The wavelet neural network or neuro-wavelet transform (NWT) is a recent effort in this regard to accurately model or predict different types of parameters characterizing river or ocean processes. This paper presents a review of applications of NWT in hydrologic and Oceanic modeling.

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“…The precipitation inter-annual fluctuation had a complex periodicity, while the wavelet analysis method had an extremely advantage in explaining the multiple-scale periodicity [35,36]. As seen from the results of analyzing the time scale, the common aspects of precipitation change in different regions of Horqin region are as follows.…”

Section: Discussionmentioning

confidence: 99%

Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region

et al. 2017

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Abstract:Precipitation is the main water source for vegetation survival in arid and semi-arid areas. However, previous studies always focus on the effects of precipitation in different time scales, but ignore the effects of precipitation in different spatial scales. To further study the effects of precipitation fluctuation in different spatial scales, we used the wavelet analysis method to analyze its temporal and spatial change based on data from eighteen meteorological stations during 1961-2015 in Horqin region. Results showed that: (1) from the overall tendency of precipitation changes, the precipitation inter-annual variations in Horqin region had the tendency of gradually decreasing from the southeast (District IV) to the northwest; (2) the precipitation anomalies of District I-IV between 1960 and 1980 were small and approximate to the normal value; (3) in the time scale of 23-32 years, the cyclical fluctuations were very significant and the annual precipitation underwent two cyclical fluctuations from a period of low precipitation to a period of high precipitation; and (4) as results of analyzing the spatial wavelet variance of sub-region, the main cycle of precipitation in District I, District II and District III was between 10 and 11 years, while the main cycle of precipitation in District IV was 25 years. The main conclusions include the following. (1) This region tended to be arid, and the precipitation gradually decreased from the southeast (District IV) to northwest (District I). (2) The influence of spatial differentiation characteristics on precipitation fluctuation in this region was cyclical fluctuation, which gradually decreased from the southeast to the northwest. The length of the cyclical change period gradually shortened. In the first main cycle, whose annual precipitation changes were most significant, the changing characteristic was District IV and District I decreased from 25 years to 10 years. (3) Predicated from the cyclical changing law that the annual precipitation decreases from high to low, the Horqin region will remain in a period of low precipitation between 2016 and 2020.

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Prediction of daily precipitation using wavelet—neural networks

Cited by 84 publications

References 29 publications

A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

A new wavelet–bootstrap–ANN hybrid model for daily discharge forecasting

Review of Applications of Neuro-Wavelet Techniques in Water Flows

Annual Precipitation Fluctuation and Spatial Differentiation Characteristics of the Horqin Region

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