Discharge coefficient for vertical sluice gate under submerged condition using contraction and energy loss coefficients

A novel method is suggested for the determination of flow discharge in vertical sluice gates with considerably small bias. First, in order to derive an equation for the discharge coefficient, energy-momentum equations are implemented to define the physical realization of the phenomenon. Afterward, the discharge coefficient is presented in terms of contraction and energy loss coefficients. Subsequently, discharge coefficient, contraction, and energy loss coefficients were determined through an implicit optimization technique on the data. Data analysis illustrated that there is a meaningful power relationship between the contraction and energy loss coefficients. Thereafter, dimensional analysis is performed and an explicit best-fit regression equation is developed for defining the energy loss coefficient. The obtained equations for contraction and energy loss coefficients were then used in the computation of the discharge coefficient and determination of the flow discharge in the vertical sluice gate. The performance of the developed approach is validated against the selected benchmarks existing in the literature

Multiple kernel fusion: a novel approach for lake water depth modeling

Multiple kernel fusion (MKF) refers to the task of combining multiple sources of information in the Hilbert space for improved performance. Very often the combined kernel is formed as a linear composition of multiple base kernels where the combination weights are learned from the data. As the first application of an MKF approach in hydrological modeling, lake water depth as one of the pivot factors in the reservoir analysis is simulated by considering different hydro-meteorological variables. The role of each individual input parameter is initially investigated by applying a kernel regression approach. We then illustrate the utility of an MKF formalism which learns kernel combination of weights to yield an optimal composition for kernel regression. A set of 40-year data collected from 27 groundwater and streamflow stations and 7 meteorological stations for precipitation and evaporation parameters in the vicinity of Lake Urmia are utilized for model development. Both visual and quantitative statistical performance criteria illustrate a superior performance for the MKF approach compared to kernel ridge regression (KRR), the support vector regression (SVR), back propagation neural network (BPNN) and auto regressive (AR) models. More specifically, while each individual input parameter fails to provide an ac curate prediction for lake water depth modeling, an optimal combination of all input parameters incorporating the groundwater level, streamflow, precipitation and evaporation via a multiple kernel learning approach en hances the predictive performance of the model accuracy in the multiple scenarios. The promising results (RMSE= 0.098 m; R2 = 0.987; NSE = 0.986) may motivate the application of a MKF approach towards solving alternative and complex hydrological problems

Fast multi-output relevance vector regression for joint groundwater and lake water depth modeling

Fast multi-output relevance vector regression (FMRVR) algorithm is developed for simultaneous estimation of groundwater and lake water depth for the first time in this study. The FMRVR is a multi-output regression analysis technique which can simultaneously predict multiple outputs for a multi-dimensional input. The data used in this study is collected from 34 stations located in the lake Urmia basin over a 40-year time period. The performance of the FMRVR model is examined in contrast to the support vector regression (SVR) and multi-linear regression (MLR) benchmarks. Results reveal that FMRVR is able to generate more accurate estimation for groundwater and lake water depth with coefficient of determination (R2) of 0.856 and 0.992 and root mean square error (RMSE) of 0.857 and 0.083, respectively. The outperformance of FMRVR can be linked to its capability for a joint estimation of multiple relevant outputs by taking into account possible correlations among the outputs

Decision Tree for Measuring the Interaction of Hyper-Saline Lake and Coastal Aquifer in Lake Urmia

© 2015 ASCE.Lake Urmia is located in the North West of Iran. The hyper saline lake is drying up very fast and more than seventy percent of the water in the lake has vanished in recent years. In this research, the West and South banks of the lake's basin which is known as the West Azerbaijan province of Iran are studied. During the period from March 2001 to August 2011, six pilot stations for ground water near the lake shore were monitored. Correlation, cross-correlation, distribution, and regression analysis were done for lake and pilot stations. Several decision trees were fitted to the model and the most proper one was selected to test the hypothesis. Results show that the North West of the basin is the most interactive part of the ground water and the fitted decision tree model with randomly selected data is performing well