Spatial and temporal monthly precipitation forecasting using wavelet transform and neural networks, Qara-Qum catchment,
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ORIGINAL PAPER
Spatial and temporal monthly precipitation forecasting using wavelet transform and neural networks, Qara-Qum catchment, Iran Mohammad Arab Amiri 1 & Yazdan Amerian 2 & Mohammad Saadi Mesgari 1
Received: 28 July 2015 / Accepted: 28 March 2016 # Saudi Society for Geosciences 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
* Mohammad Arab Amiri [email protected] Yazdan Amerian [email protected] Mohammad Saadi Mesgari [email protected]
1
Department of Geographic Information System, Faculty of Geodesy and Geomatics Engineering and Center of Excellence in Geospatial Information Technology (CEGIT), K.N.Toosi University of Technology, Tehran, Iran
2
Department of Geodesy, Faculty of Geodesy and Geomatics Engineering, K.N.Toosi University of Technology, Tehran, Iran
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. Keywords Precipitation . Time series . Wavelet transform . Artificial neural network . GIS
Introduction Rainfall is a principal component of hydrological studies. Rainfall is also a complex process, which varies in time and space. Therefore, rainfall prediction in time and space is of great importance. Moreover, the behavior of rainfall time series can be analyzed in different scales of time and space. There are many approaches to predict meteorological parameters; for instance, Sharifi and Souri (2015) utilized a hybrid model to predict time series of precipitable water vapor derived from GPS measurements. Haktanir et al. (2013) analyzed the maximum daily rainf
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