Department of Civil Engineering, York University, 4700 Keele Street, Toronto, ON, Canada M3J 1P3 E-mail:
Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada T2N 1N4.
Water Sci Technol. 2017 Apr;2017(1):238-247. doi: 10.2166/wst.2018.107.
Urban floods are one of the most devastating natural disasters globally and improved flood prediction is essential for better flood management. Today, high-resolution real-time datasets for flood-related variables are widely available. These data can be used to create data-driven models for improved real-time flood prediction. However, data-driven models have uncertainty stemming from a number of issues: the selection of input data, the optimisation of model architecture, estimation of model parameters, and model output. Addressing these sources of uncertainty will improve flood prediction. In this research, a fuzzy neural network is proposed to predict peak flow in an urban river. The network uses fuzzy numbers to account for the uncertainty in the output and model parameters. An algorithm that uses possibility theory is used to train the network. An adaptation of the automated neural pathway strength feature selection (ANPSFS) method is used to select the input features. A search and optimisation algorithm is used to select the network architecture. Data for the Bow River in Calgary, Canada are used to train and test the network.
城市洪水是全球最具破坏性的自然灾害之一，因此提高洪水预测的准确性对于更好地进行洪水管理至关重要。如今，与洪水相关的变量的高分辨率实时数据集已经广泛可用。这些数据可用于创建数据驱动的模型，以实现更准确的实时洪水预测。然而，数据驱动的模型存在不确定性，其主要来源于以下几个方面：输入数据的选择、模型结构的优化、模型参数的估计以及模型输出。解决这些不确定性来源将提高洪水预测的准确性。在本研究中，提出了一种模糊神经网络来预测城市河流的洪峰流量。该网络使用模糊数来考虑输出和模型参数的不确定性。使用可能性理论的算法用于训练该网络。采用自适应神经路径强度特征选择（ANPSFS）方法来选择输入特征。使用搜索和优化算法来选择网络结构。该网络使用来自加拿大卡尔加里的 Bow 河的数据进行训练和测试。
