JWSS - Journal of Water and Soil Science

Statistical analysis and forecast discharge data play an important role in management and development of water systems. The most fundamental issues of statistical analysis and forecast discharge in Iran are lack of data in long term period and lack of stream flow data in gauging stations. Considering the issues mentioned in this study, we tried to estimate the daily data flow (runoff) of Santeh gauging station in Kordestan province using the nearby hydrometric and meteorological stations data. This estimation occurred based on the sixteen different input combinations, including data of daily flow of hydrometric stations Safakhaneh and Polanian and daily runoff in Santeh precipitation gauging station. In this research, the daily flow estimation of the Santeh station in each of the months of the year was evaluated for sixteen different combinations and artificial neural network models and multiple linear regressions. The performance of each model was evaluated with the indicators RMSE, CC, NS and t-student statistic. The results showed good performance of both models but the performance of the artificial neural network model was better than the regression model in estimation of the daily runoff in the most months of the year. Mean error of artificial neural network and multiple linear regression models was respectively estimated as 6.31 and 8.07 m³/s in the months of the year. It should be noted that the artificial neural network, for each sixteen combination used, had better result than the regression model.

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.