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首页> 外文期刊>Natural Hazards >Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China
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Improving the forecast precision of river stage spatial and temporal distribution using drain pipeline knowledge coupled with BP artificial neural networks: a case study of Panlong River, Kunming, China

机译：利用排水管知识结合BP人工神经网络提高河段时空分布预测精度-以昆明市盘龙河为例

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摘要

Artificial neural network technologies are frequently used in flood disaster simulations to aid regional disaster analyses. However, despite being an important factor that affects urban waterlogging, urban underground pipeline knowledge is seldom coupled with artificial neural networks or applied to urban waterlogging simulations. This article presents a simulation of urban waterlogging that utilises professional knowledge of urban underground drain pipelines coupled with BP artificial neural networks. Using this method, actual input weights are computed to simulate the river stage variations in the Panlong River of Kunming, China, for 35 consecutive hours during a heavy rainstorm that took place on 19 July 2013. The artificial neural network is coupled with drain pipeline knowledge, and river stage variations during this heavy rainfall are successfully simulated. The study results indicate that, in comparison with traditional BP neural network simulation methods, the use of knowledge of urban drain pipelines coupled with artificial neural networks yields more precise forecasting results for the urban river stage, with 85.7 % of all simulated river stage values corresponding closely with observed values. To support decision-making based on urban waterlogging forecasts, a map showing the impact distribution of the maximum river stage of Panlong River on the day of field study is provided. The results of the simulations show that the predicted locations of river water overflow were similar to the observed locations.
机译：人工神经网络技术经常用于洪水灾害模拟中，以帮助进行区域灾害分析。然而，尽管是影响城市涝灾的重要因素，但城市地下管道知识很少与人工神经网络结合，也很少应用于城市涝灾模拟中。本文介绍了利用城市地下排水管道的专业知识与BP人工神经网络相结合的城市涝灾模拟。使用此方法，可以计算实际输入权重，以模拟中国昆明市盘龙河在2013年7月19日暴雨期间连续35个小时的河段变化。人工神经网络与排水管道知识相结合，并且成功模拟了这场大雨期间的河段变化。研究结果表明，与传统的BP神经网络模拟方法相比，结合城市排水管道知识和人工神经网络可对城市河段进行更精确的预测，所有模拟河段值的85.7％对应与观察值紧密相关。为了支持基于城市涝灾预报的决策，提供了一张地图，显示了实地考察当天盘龙河最大河段的影响分布。模拟结果表明，河水溢流的预测位置与观测位置相似。

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