Multi-objective automatic data assimilation of a hydrological model based on classification of initial hydrologic states

里约热内卢Tinto铝业公司（RTA）的电力业务部门的任务是管理两个大型水资源系统，即quacimbec的Lac-Saint-Jean系统和不列颠哥伦比亚省的Nechako系统。**为了有效和安全地管理水资源系统，我们**生成了多种入流预测情景，并将其输入水资源管理优化模型。预测的流入量质量至关重要，因为它可以充分降低洪水风险，并在给定水量下最大限度地提高水电发电量。**水文模型用于产生入流预测，这些模型首先是在过去的数据上进行训练的，然后通过使用预测输入（如降水和温度）驱动模型来产生入流预测。模型的初始状态是获得准确预测所需的关键参数。为了在开始预测模拟之前提供最好的初始状态，专家分析师将模型状态与当前观察结果进行比较，并在必要时手动纠正这些状态。这个程序产生可靠的短期预测。然而，长期预测的技巧很差，因为人工**变化在复杂系统中长期的传播不明确。本研究旨在利用先进的灵敏度分析和分类算法实现初始状态更新过程的自动化。自动**程序会根据集水区的初始情况，寻找最佳修正，以达致可靠的短期及长期预报**。这将有助于RTA利用可持续的水力发电优化其运营。

The Power Operations division of Rio Tinto Aluminium (RTA) has the mandate to manage two large water**resources systems, namely the Lac-Saint-Jean system in Québec and the Nechako system in British-Columbia.**To manage the water resources systems efficiently and safely, multiple scenarios of inflow predictions are**produced and are fed into water resources management optimization models. The quality of the forecasted**inflows is crucial as it allows adequately mitigating flooding risks as well as maximizing hydropower**generation for a given volume of water.**Hydrologic models are used to produce inflow predictions and these are first trained on past data and then used**to produce inflow forecasts by driving the model with forecasted inputs like precipitation and temperature. The**initial states of the model are key parameters required to obtain an accurate forecast. To provide the best**possible initial states before starting the forecast simulations, an expert analyst compares the model states with**current observations and corrects these states manually if necessary. This procedure yields reliable short-term**forecasts. The skill of long-term forecasts, however, is poor due to the unclear propagation of the manual**changes through the complex system over a long horizon. This research is intended to automatize the process**of initial state updating using advanced sensitivity analysis and classification algorithms. The automatic**procedure will search for the optimal corrections to achieve both reliable short-term and long-term performance**of the forecasts based on the initial conditions of the catchment. This will help RTA to optimize their**operations using sustainable hydroelectricity.