Rapid forecasting of urban waterlogging based on K-nearest neighbor and hydrodynamic model

被引：0

作者：

Pan X. ^{[1
]}

Hou J. ^{[1
]}

Chen G. ^{[1
]}

Zhou N. ^{[1
]}

Lyu J. ^{[1
]}

Liang X. ^{[1
]}

Tang J. ^{[2
]}

Zhang S. ^{[3
]}

机构：

[1] State Key Laboratory of Eco-hydraulics in Northwest Arid Region of China, Xi’ an University of Technology, Xi’ an

[2] China Academy of Urban Planning & Design, Beijing

[3] School of Hydraulic Engineering, Sichuan Water Conservancy College, Chengdu

来源：

Water Resources Protection | 2023年 / 39卷 / 03期

关键词：

Hydrodynamic model; K-nearest neighbor algorithm; Machine learning model; Qinhan New City; Rapid forecast; Urban waterlogging;

D O I：

10.3880/j.issn.1004-6933.2023.03.011

中图分类号：

学科分类号：

摘要：

The evolution process of urban waterlogging under different levels of rainfall was simulated using a hydrodynamic model, and then the waterlogging evolution process data was used as the training set of the K-nearest neighbor algorithm machine learning model for model training. The rainfall predicted by the atmospheric numerical model was used to drive the trained K-nearest neighbor machine learning model for rapid urban waterlogging forecasting. Taking Qinhan New City in Shaanxi Province as an example, the predictive performance of the model was tested through three measured rainfall events. The results show that the model can quickly predict urban waterlogging within 17 seconds, with an average error of no more than 8% for the predicted waterlogging area, and no more than 15% for the average error of waterlogging amount and depth. The model has good predictive performance and can enhance urban disaster prevention and reduction capabilities, effectively reduce life and property losses. © 2023, Editorial Board of Water Resources Protection. All rights reserved.

引用

页码：91 / 100

页数：9

共 31 条

[11]

GUO Yuan, LI Yuling, WANG Huiliang, Et al., Research on the meteorological and hydrological coupling models on early warning of urban waterlogging in Zhengzhou [J], Journal of China Hydrology, 42, 4, pp. 61-67, (2022)

[12]

GOMEZ M, SHARMA S, REED S, Et al., Skill of ensemble flood inundation forecasts at short-to medium-range timescales, Journal of Hydrology, 568, pp. 207-220, (2018)

[13]

HE Wanbing, HE Junjie, WANG Ru, Et al., Machine learning applications in nuclear physics [J], Scientia Sinica(Physica,Mechanica & Astronomica), 52, 5, pp. 38-50, (2022)

[14]

YANG Zhiyun, WANG Hui, HE Yi, Et al., A new perspective on the application of cardiac magnetic resonance in cardiomyopathy: Artificial intelligence and machine learning [J], Journal of Cardiovascular and Pulmonary Diseases, 41, 3, pp. 321-323, (2022)

[15]

KAN Guangyuan, HONG Yang, LIANG Ke, Research on the flood forecasting based on coupled machine learning model [J], China Rural Water and Hydropower, 10, pp. 165-169, (2018)

[16]

LIU Yuanyuan, LIU Yesen, ZHENG Jingwei, Et al., Research on urban waterlogging prediction method combining BP neural network and numerical model [J], Journal of Hydraulic Engineering, 53, 3, pp. 284-295, (2022)

[17]

MEI Song, CHENG Weiping, LIU Guohua, Preliminary discussion on flood forecast model based on support vector machine, China Rural Water and Hydropower, 3, pp. 34-36, (2005)

[18]

YI Xiao, ZENG Rui, CAO Xinying, Asynchronous anti-bias track association algorithm by using K-nearest neighbors interval distance [J], Systems Engineering and Electronics, 44, 5, pp. 1475-1482, (2022)

[19]

SUN Lin, QIN Xiaoying, XU Jiucheng, Et al., Density peak clustering algorithm based on K-nearest neighbor and optimal allocation strategy [J], Journal of Software, 33, 4, pp. 1390-1411, (2022)

[20]

CHEN Zhong, WANG Jiegui, TANG Xiwen, Et al., A DOA estimation method based on K-nearest neighbor algorithm [J], Journal of Detection & Control, 44, 1, pp. 24-28, (2022)

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