Mesut Çimen

Precipitation forecasting by using wavelet-support vector machine conjunction model

A new wavelet-support vector machine conjunction model for daily precipitation forecast is proposed in this study. The conjunction method combining two methods, discrete wavelet transform and support vector machine, is compared with the single support vector machine for one-day-ahead precipitation forecasting. Daily precipitation data from Izmir and Afyon stations in Turkey are used in the study. The root mean square errors (RMSE), mean absolute errors (MAE), and correlation coefficient (R) statistics are used for the comparing criteria. The comparison results indicate that the conjunction method could increase the forecast accuracy and perform better than the single support vector machine. For the Izmir and Afyon stations, it is found that the conjunction models with RMSE=46.5mm, MAE=13.6mm, R=0.782 and RMSE=21.4mm, MAE=9.0mm, R=0.815 in test period is superior in forecasting daily precipitations than the best accurate support vector regression models with RMSE=71.6mm, MAE=19.6mm, R=0.276 and RMSE=38.7mm, MAE=14.2mm, R=0.103, respectively. The ANN method was also employed for the same data set and found that there is a slight difference between ANN and SVR methods.

Estimation of daily suspended sédiments using support vector machines

Abstract The use of support vector machines—a new regression procedure in water resources—was investigated for predicting suspended sediment concentration/load in rivers. The method was applied to the observed streamflow and suspended sediment data of two rivers in the USA, which have already been used in earlier studies using soft computing techniques. The estimated suspended sediment values were found to be in good agreement with the observed ones. Negative sediment estimates, which were encountered in the soft computing calculations, are not produced by this method. The results indicate that this approach may give better performance than those described in the literature using different methodologies.

Effective Procedure for Determination of Aquifer Parameters from Late Time-Drawdown Data

In this study an effective procedure is developed to evaluate the aquifer parameters from the late time-drawdown data observed both at pumping and observation wells in confined aquifers. For this aim, the time-drawdown data graph is considered by means of the MS Excel spreadsheets and later, its regression line and equation for the late time-drawdown data are obtained. The data after the minimum pumping duration criterion valid for both pumping and observation wells, in the case of flow toward a large-diameter well, by M. Cimen in 2006 and after the some early drawdown for observation wells, in the absence of the pumping well data, are considered in the calculations. It is observed that this late time-drawdown data yield the straight line in the logarithmic function. The transmissivity of the confined aquifer is then obtained from the slope value of the regression equation derived for late time data. The storage coefficient is also calculated with the Cooper–Jacob equation. In the Cooper–Jacob equation, t...

A Straight-Line Method for Analyzing Residual Drawdowns at an Observation Well

Determination of the hydraulic parameters (transmissivity and storage coefficients) of a confined aquifer is important for effective groundwater resources. For this purpose, the residual drawdowns have been in use to estimate the aquifer parameters by the classical Theis recovery method. The proposed method of this paper depends on a straight-line through the field data and it helps to calculate the parameters quickly without any need for long-term pumping data. It is based on the expansion series of the Theis well function by consideration of three terms, and this approach is valid for the dimensionless time factor . The method can be applied reliably to extensive and homogeneous confined aquifers resulting in different storage coefficients during the pumping and recovery periods . It presents a strength methodology for the parameters decision making from the residual data in the groundwater field of civil engineering.

Reply to the Discussion of “Evapotranspiration modelling using support vector machines” by R. J. Abrahart et al.

The authors would like to express their gratitude for the interest shown by the discussers (Abrahart et al., 2010) in the paper and for their comments on the subject. We have tried to clarify all t...