Emulating Rainfall-Runoff-Inundation Model using Deep Neural Network with Dimensionality Reduction
Emulating Rainfall-Runoff-Inundation Model using Deep Neural Network with Dimensionality Reduction
复制标题
使用降维深度神经网络模拟降雨-径流-洪水模型
DOI：
10.1175/aies-d-22-0036.1
复制
发表时间：
2023
期刊：
影响因子：
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通讯作者：
Abe Shiori
中科院分区：
文献类型：
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作者：
Momoi Masahiro;Kotsuki Shunji;Kikuchi Ryota;Watanabe Satoshi;Yamada Masafumi;Abe Shiori
最大淹没深度（deep - map）的空间分布预测对于缓解极端降水引起的水文灾害具有重要意义。然而，基于物理的降雨-径流-淹没（RRI）模型在日本用于实际预测水文灾害，需要大量的计算资源进行数值模拟。在这里，我们的目标是开发一种模拟RRI模型的计算成本低廉的深度学习模型（Rain2Depth）。本研究以日本秋田县小野河为研究对象，利用单个事件的时空降水数据预测深度- map。Rain2Depth是基于卷积神经网络（CNN）开发的，根据盆地13个雨量站连续7天的每小时降雨量预测深度- map。为了训练Rain2Depth，我们使用RRI模式模拟深度- map，该模式由50个大集合天气/气候预测的30年数据组成。我们没有直接使用输入和输出数据，而是在训练网络之前使用两维降维技术[主成分分析（PCA）和CNN方法]从输入和输出数据中提取重要特征。这种降维的目的是为了避免训练数据不足导致的过拟合。在特征提取方面，非线性CNN方法优于线性PCA方法。最后，通过神经网络连接提取的输入和输出数据之间的特征，构建Rain2Depth体系结构。基于rain2depth的预测比我们之前的模型（K20）的预测更准确，后者使用了特定站点的多个正则化回归的集成学习。K20只能在站点上预测最大淹没深度，而我们的研究仅通过训练单一模式Rain2Depth来实现深度- map预测。
Predicting the spatial distribution of maximum inundation depth (depth-MAP) is important for the mitigation of hydrological disasters induced by extreme precipitation. However, physics-based rainfall–runoff-inundation (RRI) models, which are used operationally to predict hydrological disasters in Japan, require massive computational resources for numerical simulations. Here, we aimed at developing a computationally inexpensive deep learning model (Rain2Depth) that emulates an RRI model. Our study focused on the Omono River (Akita Prefecture, Japan) and predicted the depth-MAP from spatial and temporal rainfall data for individual events. Rain2Depth was developed based on a convolutional neural network (CNN) and predicts depth-MAP from 7-day successive hourly rainfall at 13 rain gauge stations in the basin. For training the Rain2Depth, we simulated the depth-MAP by the RRI model forced by 50 ensembles of 30-yr data from large-ensemble weather/climate predictions. Instead of using the input and output data directly, we extracted important features from input and output data with two dimensionality reduction techniques [principal component analysis (PCA) and the CNN approach] prior to training the network. This dimensionality reduction aimed to avoid overfitting caused by insufficient training data. The nonlinear CNN approach was superior to the linear PCA for extracting features. Finally, the Rain2Depth architecture was built by connecting the extracted features between input and output data through a neural network. Rain2Depth-based predictions were more accurate than predictions from our previous model (K20), which used ensemble learning of multiple regularized regressions for a specific station. Whereas the K20 can predict maximum inundation depth only at stations, our study achieved depth-MAP prediction by training only the single model Rain2Depth.