Investigation of factors affecting rural drinking water consumption using intelligent hybrid models

Identifying the factors affecting drinking water consumption is essential to the rational management of water resources and effective environment protection. In this study, the effects of the factors on rural drinking water demand were studied using the adaptive neuro-fuzzy inference system (ANFIS) and hybrid models, such as the ANFIS-genetic algorithm (GA), ANFIS-particle swarm optimization (PSO), and support vector machine (SVM)-simulated annealing (SA). The rural areas of Hamadan Province in Iran were selected for the case study. Five drinking water consumption factors were selected for the assessment according to the literature, data availability, and the characteristics of the study area (such as precipitation, relative humidity, temperature, the number of subscribers, and water price). The results showed that the standard errors of ANFIS, ANFIS-GA, ANFIS-PSO, and SVM-SA were 0.669, 0.619, 0.705, and 0.578, respectively. Therefore, the hybrid model SVM-SA outperformed other models. The sensitivity analysis showed that of the parameters affecting drinking water consumption, the number of subscribers significantly affected the water consumption rate, while the average temperature was the least significant factor. Water price was a factor that could be easily controlled, but it was always one of the least effective parameters due to the low water fee.     

Evaluating the Effect of Downstream Channel Width Variation on Hydraulic Performance of Arched Plan Stepped Spillways

In the present study, a 3D physical model with a scale of 1:50 was developed based on the Germi-Chay dam. The model was used to assess the impact of downstream channel width variation on the efficiency of arched plan stepped spillways. Four ratios of downstream channel width to spillway crest width (Wch/W) were considered which range from 0.214 to 0.286. The hydraulic parameters such as water surface profile, static pressure, discharge coefficient, energy dissipation, and stage-discharge were assessed for each model. Results of the experiments indicated that the values of the discharge coefficient increased with increasing the upstream head values before the submergence state occurrence. However, in this state, the downstream channel width variations had no significant impact on the discharge coefficient. On the other hand, with spillway submergence, influence on discharge coefficient was observed when width variations occurred. Observations showed that an increase in the width ratio caused a decrease in static pressure. In addition, the results of the submerged state clearly showed the effect of the width ratio variations on energy dissipation. For a particular discharge, the highest width ratio led to minimal energy dissipation. Results revealed that the most efficient model was when Wch/W?=?0.286. This model was the only one that successfully passed the probable maximum flood discharge in the maximum allowable height (5 m).