Search

This paper reports the effect of straight furrow (SF) and meandering furrow (MF) irrigation strategies, as well as inflow rate, on infiltration and hydraulic parameters including advance time, recession time, and runoff hydrograph. The performance of SF and MF irrigation in terms of runoff ratio, deep percolation, and application efficiency was evaluated in 6 furrow fields at Shahid Bahonar University of Kerman, Iran. The required data were collected from the farm, consisting of free drainage furrows with length 70 m, top width 0.8 m, depth 0.25 m, and slope 0.2%. The advance and recession times were significantly longer in MF than SF irrigation. The infiltration was estimated by Lewis-Kostiakov equation. The infiltration coefficients were calculated: The values of k were higher and of a were lower in MF furrows than in SF furrows. The average runoff ratio and application efficiency for the SF irrigation events were 50.53% and 49.07%, respectively, while those of the MF irrigation events were 7.04% and 52.94%, respectively. Based on the results, the velocity of water advance in MF irrigation is decreased and, thus, the runoff, erosion losses, mass of fertilizer lost and surface water contamination were reduced. Using a lower inflow rate and appropriate irrigation time leads to better management outcomes in irrigation systems. [ABSTRACT FROM AUTHOR]

Chlorination, the basic treatment utilized for drinking water sources, is widely used for water disinfection and pathogen elimination in water distribution networks. Thereafter, the proper prediction of chlorine consumption is of great importance in water distribution network performance. In this respect, data mining techniques-which have the ability to discover the relationship between dependent variable(s) and independent variables-can be considered as alternative approaches in comparison to conventional methods (e.g., numerical methods). This study examines the applicability of three key methods, based on the data mining approach, for predicting chlorine levels in four water distribution networks. ANNs (artificial neural networks, including the multi-layer perceptron neural network, MLPNN, and radial basis function neural network, RBFNN), SVM (support vector machine), and CART (classification and regression tree) methods were used to estimate the concentration of residual chlorine in distribution networks for three villages in Kerman Province, Iran. Produced water (flow), chlorine consumption, and residual chlorine were collected daily for 3 years. An assessment of the studied models using several statistical criteria (NSC, RMSE, R 2 , and SEP) indicated that, in general, MLPNN has the greatest capability for predicting chlorine levels followed by CART, SVM, and RBF-ANN. Weaker performance of the data-driven methods in the water distribution networks, in some cases, could be attributed to improper chlorination management rather than the methods' capability.