NDLI: Spatial and temporal monthly precipitation forecasting using wavelet transform and neural networks, Qara-Qum catchment, Iran

Content Provider	Springer Nature Link
Author	Arab Amiri, Mohammad Amerian, Yazdan Mesgari, Mohammad Saadi
Copyright Year	2016
Abstract	This paper aims to provide a spatial and temporal analysis to prediction of monthly precipitation data which are measured at irregularly spaced synoptic stations at discrete time points. In the present study, the rainfall data were used which were observed at four stations over the Qara-Qum catchment, located in the northeast of Iran. Several models can be used to spatially and temporally predict the precipitation data. For temporal analysis, the wavelet transform with artificial neural network (WTANN) framework combines with the wavelet transform, and an artificial neural network (ANN) is used to analyze the nonstationary precipitation time-series. The time series of dew point, temperature, and wind speed are also considered as ancillary variables in temporal prediction. Furthermore, an artificial neural network model was used for comparing the results of the WTANN model. Therefore, four models were developed, including WTANN and ANN with and without ancillary data. Several statistical methods were used for comparing the results of the temporal analysis. It was evident that at three of the four stations, the WTANN models were more effective than the ANN models, and only at one station, the ANN model with ancillary data had better performance than the WTANN model without ancillary data. The values of correlation coefficient and RMSE for WTANN model with ancillary data for the validation period at Mashhad station which showed the best results were equal to 0.787 and 13.525 mm, respectively. Finally, an artificial neural network model was used as an alternative interpolating technique for spatial analysis.
Starting Page	1
Ending Page	18
Page Count	18
File Format	PDF
ISSN	18667511
Journal	Arabian Journal of Geosciences
Volume Number	9
Issue Number	5
e-ISSN	18667538
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-05-03
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Precipitation Time series Wavelet transform Artificial neural network GIS Earth Sciences
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Environmental Science

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