An ISaDE algorithm combined with support vector regression for estimating discharge coefficient of W-planform weirs

Various shapes of weirs, such as rectangular, trapezoidal, circular, and triangular plan forms, are used to adjust and measure the flow rate in irrigation networks. The discharge coefficient (Cd) of weirs, as the key hydraulic parameter, involves the combined effects of the geometric and hydraulic parameters. It is used to compute the flow rate over the weirs. For this purpose, a hybrid ISADE-SVR method is proposed as a hybrid model to estimate the Cd of sharp-crested W-planform weirs. ISaDE is a high-performance algorithm among other optimization algorithms in estimating the nonlinear parameters in different phenomena. ISaDE algorithm is used to improve the performance of SVR by finding optimal values for SVR's parameters. To test and validate the proposed model, the experimental dataset of Kumar et al. and Ghodsian were utilized. Six different input scenarios are presented to estimate the Cd. Based on the modeling results, the proposed hybrid method estimates the Cd in terms of the H/P, Lw/Wmc, and Lc/Wc. For the superior method, R2, RMSE, MAPE, and δ are obtained as 0.982, 0.006, 0.612, and 0.843, respectively. The amount of improvement in compared with GMDH, ANFIS and SVR is 3.6%, 1.2%, 1.5% in terms of R2.

Optimal architecture for artificial neural networks as pressure estimator

ABSTRACT The knowledge of hydraulic parameters in water distribution networks can indicate problems in real time, such as pipe bursts, small leakages, increase in pipe roughness and illegal connections. However, an accurate indication relies on the quantity and quality of the data acquired, i.e., the number of sensors used to monitor the network and their location. It is not economic feasible have a great number of sensors, thus, the use of artificial intelligence, such as Artificial Neural Networks (ANNs) can reduce the lack of information necessary to identify problems, estimating hydraulic parameter through the few information collected. The reliability of ANNs depends on its architecture, so, in this paper, different conditions are tested for ANN training to identify which are the most relevant parameters to be adjusted when the ANN is used for pressure estimation.