Reservoirs provide rural and municipal water supply for various purposes such as drinking water, irrigation, hydropower, industrial purposes and recreational activities. Supplying these demands depends strongly on the dam reservoir capacity. Hence, reservoir storage capacity prediction is a determining factor in water resources planning and management, drought risk management, flood risk assessment and management. In the present study, imperialist competitive algorithm as a relatively new socio-political-based global search technique introduced for solving different optimization problems employed to predict reservoir storage capacity of Shaharchay dam located in the Urmia lake basin in northwest of Iran. The high convergence rate of imperialist competitive algorithm along with its capability in finding global optimal is striking aspect of the algorithm. The results obtained from this algorithm were compared with those of Artificial Neural Network. The comparison of the results with the measured ones by means of error measures indicates the superiority of imperialist competitive algorithm over Artificial Neural Network.

中文翻译：

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帝国主义竞争算法与人工神经网络在储层储量估算中的比较评价

水库为各种目的提供农村和市政供水，例如饮用水，灌溉，水力发电，工业目的和娱乐活动。满足这些需求在很大程度上取决于大坝水库的容量。因此，水库蓄水量预测是水资源规划与管理，干旱风险管理，洪水风险评估与管理的决定因素。在本研究中，帝国主义竞争算法是一种相对较新的基于社会政治的全球搜索技术，用于解决预测伊朗伊斯坦堡乌尔米亚湖盆地Shaharchay大坝储水容量的不同优化问题。帝国主义竞争算法的高收敛速度以及寻找全局最优的能力是该算法的显着方面。从该算法获得的结果与人工神经网络的结果进行了比较。结果与误差测量结果的比较表明，帝国主义竞争算法优于人工神经网络。