Estimation of Missing Daily Precipitation and Runoff Using Self-Organizing Map (A Case Study: Mazandaran Province)

Abstract

Expert aquatic designers face many problems; among these, in hydrology, defective occurrences in time-series can cause errors in the ultimate results of the study. This more often happens in the regions where the number of hydrometric and rain gauge stations is limited. In addition, assessing, developing and maintaining the use of water resources require accessible long-term and high-quality quality hydrological time-series. Thus, this necessitates correcting the statistical flaws and magnifies the importance of how to deal with the problems in the hydrological analyses. Statistical methods are, currently, used to infill data and statistical gaps. In this study, in order to introduce a multivariate method for estimating the missing data on rainfall and runoff, in a hydrologic homogeneous region in the Mazandaran province, self-organizing map methods were examined under two scenarios and some reliable estimates were obtained. In this regard, the correlation coefficients between the observational data and the model output were calculated for the precipitation data up to 0.92 and up to 0.95 for the runoff data. Therefore, to avoid the reduction of uncertainty caused by the inadequate data in water resource management, this method could be used.

Keywords

• unsupervised neural network
• infilling time series
• missing data