Runoff Forecasting Using Machine-Learning Methods: Case Study in the Middle Reaches of Xijiang River

被引:10
|
作者
Xiao, Lu [1 ]
Zhong, Ming [1 ,2 ]
Zha, Dawei [3 ]
机构
[1] Sun Yat Sen Univ, Sch Geog & Planning, Dept Land Resources & Environm, Guangzhou, Peoples R China
[2] Southern Marine Sci & Engn Guangdong Lab Zhuhai, Zhuhai, Peoples R China
[3] Pearl River Water Resources Res Inst, Guangzhou, Peoples R China
来源
FRONTIERS IN BIG DATA | 2022年 / 4卷
关键词
streamflow; water level; forecast; machine learning; wavelet neural network (WNN); generalized regression neural network (GRNN); ARTIFICIAL NEURAL-NETWORK; STREAM-FLOW; REGRESSION; PREDICTION; RAINFALL; MODELS;
D O I
10.3389/fdata.2021.752406
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Runoff forecasting is useful for flood early warning and water resource management. In this study, backpropagation (BP) neural network, generalized regression neural network (GRNN), extreme learning machine (ELM), and wavelet neural network (WNN) models were employed, and a high-accuracy runoff forecasting model was developed at Wuzhou station in the middle reaches of Xijiang River. The GRNN model was selected as the optimal runoff forecasting model and was also used to predict the streamflow and water level by considering the flood propagation time. Results show that (1) the GRNN presents the best performance in the 7-day lead time of streamflow; (2) the WNN model shows the highest accuracy in the 7-day lead time of water level; (3) the GRNN model performs well in runoff forecasting by considering flood propagation time, increasing the Qualification Rate (QR) of mean streamflow and water level forecast to 98.36 and 82.74%, respectively, and illustrates scientifically of the peak underestimation in streamflow and water level. This research proposes a high-accuracy runoff forecasting model using machine learning, which would improve the early warning capabilities of floods and droughts, the results also lay an important foundation for the mid-long-term runoff forecasting.
引用
收藏
页数:11
相关论文
共 50 条
  • [41] Streamflow forecasting in Tocantins river basins using machine learning
    Rodrigues Duarte, Victor Braga
    Viola, Marcelo Ribeiro
    Giongo, Marcos
    Uliana, Eduardo Morgan
    de Mello, Carlos Rogerio
    [J]. WATER SUPPLY, 2022, 22 (07) : 6230 - 6244
  • [42] Forecasting the Bearing Capacity of the Driven Piles Using Advanced Machine-Learning Techniques
    Benbouras, Mohammed Amin
    Petrisor, Alexandru-Ionut
    Zedira, Hamma
    Ghelani, Laala
    Lefilef, Lina
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (22):
  • [43] Estimation of daily global solar radiation using empirical and machine-learning methods: A case study of five Moroccan locations
    Bounoua, Zineb
    Chahidi, Laila Ouazzani
    Mechaqrane, Abdellah
    [J]. SUSTAINABLE MATERIALS AND TECHNOLOGIES, 2021, 28
  • [44] A study of time series forecasting using statistical methods, machine learning methods and deep learning: historical aspects
    Kitov, V. V.
    Mishustina, M., V
    Ustyuzhanin, A. O.
    [J]. VOPROSY ISTORII, 2022, 4 (02) : 201 - 218
  • [45] A Case Study on Customer Segmentation by using Machine Learning Methods
    Ozan, Sukru
    [J]. 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND DATA PROCESSING (IDAP), 2018,
  • [46] Quantitatively Calculating the Contribution of Vegetation Variation to Runoff in the Middle Reaches of Yellow River Using an Adjusted Budyko Formula
    Ji, Guangxing
    Huang, Junchang
    Guo, Yulong
    Yan, Dan
    [J]. LAND, 2022, 11 (04)
  • [47] Classifying "kinase inhibitor-likeness" by using machine-learning methods
    Briem, H
    Günther, J
    [J]. CHEMBIOCHEM, 2005, 6 (03) : 558 - 566
  • [48] Toward Detecting Illegal Transactions on Bitcoin Using Machine-Learning Methods
    Lee, Chaehyeon
    Maharjan, Sajan
    Ko, Kyungchan
    Hong, James Won-Ki
    [J]. BLOCKCHAIN AND TRUSTWORTHY SYSTEMS, BLOCKSYS 2019, 2020, 1156 : 520 - 533
  • [49] Predictive Analytics: A Case Study in Machine-Learning and Claims Databases
    Kvancz, David A.
    Sredzinski, Marcus N.
    Tadlock, Celynda G.
    [J]. AMERICAN JOURNAL OF PHARMACY BENEFITS, 2016, 8 (06) : 214 - 219
  • [50] Machine-Learning Methods for Material Identification Using mmWave Radar Sensor
    Skaria, Sruthy
    Hendy, Nermine
    Al-Hourani, Akram
    [J]. IEEE SENSORS JOURNAL, 2023, 23 (02) : 1471 - 1478