Optimum Design and Construction of Hydraulic Sections of Parabolic Water Transmitting Channels using the Harris Hawks Optimization Algorithm

Channels have various types of cross-sectional shapes, including trapezoidal, rectangular, semi-circular, parabolic, chain-curved, semi-cubic parabolic, egg-shaped, and circular as the most common shapes. A channel designer has many design options in different conditions, including hydraulic, economic, and hydrological conditions, leakage, etc. Among the above-mentioned sections, the first two have a horizontal bottom while the other sections are curve-shaped with bottom curvature. The primary goal in the design of hydraulic channels is to achieve the maximum flow capacity considering the minimum channel construction cost. A variety of studies has been conducted on the different types of hydraulic channels so far, each dealing with the subject from a certain perspective. However, most of the studies have focused on circular, rectangular and trapezoidal channels. This study has focused on the parabolic channel. Genetic algorithm (GA) and particle swarm optimization (PSO) or GRG algorithms and their combination are usually used for optimization. However, this research adopts a novel and updated meta-heuristic algorithm, namely the Harris Hawks Optimization (HHO) algorithm, to optimize the parabolic channel with a fixed roughness coefficient and determine the optimal dimensions of the channel with different flow rates. This channel uses different flow rates, namely 50, 100, 150, 200, 250, and 300 m3/s to solve the optimization problem. Finally, it was found that the lowest construction cost and the highest efficiency for water supply is achieved with a roughness coefficient of 0.015 and a flow rate of 100 m3/s

3D Estimation of Metal Elements in Sediments of Caspian Sea with Moving Least Square and Radial Basis Function Interpolation Methods

Spatially continuous data is important in modeling, numerical and computational works. Since sampling points are not continuous, interpolation methods should be used to estimate data at unsampled points. In this paper, radial basis function (RBF) and Moving least square (MLS) interpolation methods are applied to estimate concentration of Nickel, Mercury, Lead, Copper, and Chromium in the Caspian Sea by programming. Cross validation results are also obtained by RBF and MLS methods and have been compared for Lindane, Total DDT, Total HCH, Total Hydrocarbons and Total PAH elements. Input data for MLS and RBF are longitudinal, latitude and depth (3D interpolation) at any point. Output of MLS and RBF is concentration of an element at any point. A new method is introduced for defining constant parameter in RBF. The number of sampling points for calibration and verification tests is analyzed with the values of root mean square error (RMSE) in pollutant parameters. Optimum selection of MLS parameters are used in this paper. The results of concentrations estimation of metal elements in sediments of Caspian Sea by MLS and RBF show that RBF method yields more accurate results than MLS method

A hybrid approach based on simulation, optimization, and estimation of conjunctive use of surface water and groundwater resources

Due to limited groundwater resources in arid and semi-arid areas, conjunctive use of surface water and groundwater is becoming increasingly important. In view of this, there are needs to improve the methods for conjunctive use of surface and groundwater. Using numerical models, optimization algorithms, and machine learning, we created a new comprehensive methodological structure for optimal allocation of surface and groundwater resources and optimal extraction of groundwater. The surface and groundwater system was simulated by MODFLOW to reflect groundwater transport and aquifer conditions. The important Marvdasht aquifer in the south of Iran was used as an experimental study area to test the methodology. In this context, we developed an optimal conjunctive exploitation model for dry and wet years using two new evolutionary algorithms, i.e., whale optimization algorithm (WOA) and firefly algorithm (FA). These were used in combination with the group method of data handling (GMDH) and least squares support vector machine (LS-SVM) to estimate sustainable groundwater withdrawal. The results show that the FA is more efficient in calculating optimal conjunctive water supply so that about 61% of water needs were met in the worst scenario for surface water resources, while it was 52% using the WOA. By applying the optimal conjunctive model during the simulation period, the groundwater level increased by about 0.4 and 0.55 m using the WOA and FA, respectively. The results of Taylor’s diagram, box plot diagram, and rock diagram with error evaluation criteria, i.e., root mean square error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), showed that the GMDH (RMSE = 6.04 MCM, MAE = 3.89 MCM, and NSE = 0.99) was slightly better than LS-SVM (RMSE = 6.36 MCM, MAE = 4.50 MCM, and NSE = 0.98) to estimate optimal groundwater use. The results show that machine learning models are cost- and time-effective solutions to estimate optimal exploitation of groundwater resources in complex combined surface and groundwater supply problems. The methodology can be used to better estimate sustainable exploitation of groundwater resources by water resources managers