Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
Advanced water level prediction for a large-scale river-lake system using hybrid soft computing approach: a case study in Dongting Lake, China
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基于混合软计算方法的大尺度河湖系统高级水位预测——以洞庭湖为例
DOI：
10.1007/s12145-021-00665-8
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发表时间：
2021-07-12
影响因子：
2.8
通讯作者：
Chin, Ren Jie
中科院分区：
文献类型：
--
作者：
Deng, Bin;Lai, Sai Hin;Chin, Ren Jie
水位预测对于水利基础设施的可持续概念设计、防洪抗旱等方面具有重要意义。然而，由于流域内的复杂性和许多其他相互关联的影响因素，水位预测仍然是一项具有挑战性的任务。需要一种可靠的方法，能够有效地提取各种参数的非线性行为，从而在计算时间和精度方面提高建模能力。因此，本文选择了中国洞庭湖这一大型河湖系统作为研究对象。主要目的是为洪水预警提供一种实用的洞庭湖下游出口水位超前预报方法。这项研究的新奇在于采用了一种基于最低输入要求的软计算建模方法，以减少对过多输入的依赖，这可能会限制其未来的功能。结果表明，该模型能较准确地预报洞庭湖的逐时水位，误差为1.2%。该方法能够提供比时间步提前21 h的水位预报，适用于周边乡镇人口密集地区的洪水预警。
Water level prediction is vital in developing a sustainable conceptual design of water infrastructures, providing flood and drought control measures, etc. However, due to the complexity and many other inter-related influencing factors within a catchment, water level prediction remains a challenging task. A reliable method that is able to extract the non-linear behaviors of various parameters effectively, and thus enhances the modelling capability in terms of computation time and accuracy is required. Therefore, the Dongting Lake of China, a large-scale river-lake system has been selected for this study. The main aim is to provide a practical method for advanced water level prediction at the downstream outlet of Dongting Lake for flood warning purposes. The novelty of this study is the adoption of a soft computing modelling approach, based on minimum input requirements to reduce its dependency on too many inputs which might limit its functionality in the future. The results obtained show that the model developed can predict the hourly water level in Dongting Lake accurately with an error of 1.2%. It is able to provide an advanced water level prediction of 21 h ahead of the time step, and thus applicable for early flood warning to the surrounding area with densely populated townships.