Accurate and reliable forecasting of reservoir inflows is crucial for efficient reservoir operation to decide the quantity of the water to be released for various purposes. In this paper, an artificial neural network (ANN) model has been developed to forecast the weekly reservoir inflows along with its uncertainty, which was quantified through accounting the model’s input and parameter uncertainties. Further, to investigate how the effect of uncertainty is translated in the process of decision making, an integrated simulation–optimization framework that consists of (i) inflow forecasting model; (ii) reservoir operation model; and (iii) crop simulation model was developed to assess the impacts of uncertainty in forecasted inflow on the irrigation scheduling and total crop yield from the irrigation system. A genetic algorithm was used to derive the optimal reservoir releases for irrigation and the area of irrigation. The proposed modeling framework has been demonstrated through a case example, Chittar river basin, India. The upper, lower, and mean of forecasted inflow from the ANN model were used to arrive at the prediction interval of the depth of irrigation, total crop yield, and area of irrigation. From the analysis, the ANN model forecast error of ± 69% to the mean inflow was estimated. However, the error to mean value of simulation for total irrigation, total yield, and area of irrigation was ± 13.3%, ± 6.5%, and ± 4.6%, respectively. The optimizer mainly contributed to the reduction in the errors (i.e., maximizing the total production with the optimal water releases from the reservoir irrespective of inflow to the reservoir). The results from this study suggested that the information on the uncertainty quantification helps in better understanding the reliability of the systems and for effective decision making.

准确，可靠地预测水库流量对于有效的水库运营，以决定出于各种目的要释放的水量至关重要。本文建立了一个人工神经网络（ANN）模型来预测每周水库入库量及其不确定性，并通过考虑模型的输入和参数不确定性对其进行了量化。此外，为了研究在决策过程中不确定性的影响是如何转化的，一个综合的模拟优化框架包括：（i）流量预测模型；（ii）水库运作模式；（iii）建立了作物模拟模型，以评估预报的入水不确定性对灌溉计划和灌溉系统总作物产量的影响。使用遗传算法来得出灌溉和灌溉面积的最佳水库释放量。拟议的建模框架已通过印度Chittar流域的案例进行了论证。ANN模型的预测流入量的上，下和均值用于得出灌溉深度，作物总产量和灌溉面积的预测间隔。通过分析，得出的ANN模型预测误差为平均流入量的±69％。但是，总灌溉，总产量和灌溉面积模拟平均值的误差分别为±13.3％，±6.5％和±4.6％。优化器主要有助于减少误差（即，使总产量最大化，而从储层中释放出的最佳水量却不受流入储层的影响）。