The applicability of ASCS_LSTM_ATT model for water level prediction in small- and medium-sized basins in China

DOI PDF 被引用文献2件
  • Ke Li
    College of Computer and Information, Hohai University, Nanjing 211100, China
  • Dingsheng Wan
    College of Computer and Information, Hohai University, Nanjing 211100, China
  • Yuelong Zhu
    College of Computer and Information, Hohai University, Nanjing 211100, China
  • Cheng Yao
    College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
  • Yufeng Yu
    College of Computer and Information, Hohai University, Nanjing 211100, China
  • Cunyou Si
    Jiangsu Hydrological and Water Resources Survey Bureau, Nanjing 211100, China
  • Xiangchao Ruan
    Fiberhome Telecommunication Technologies Co., Ltd, Nanjing 210000, China

抄録

<jats:title>Abstract</jats:title> <jats:p>Water level prediction of small- and medium-sized rivers plays an important role in water resource management and flood control. Such a prediction is concentrated in the flood season because of the frequent occurrence of flood disasters in the plain area. Moreover, the flood in mountainous areas suddenly rises and falls, and the slope is steep. Thus, establishing a hydrological prediction model for small- and medium-sized rivers with high accuracy and different topographic features, that is, plains and mountains, is an urgent problem. A prediction method based on ASCS_LSTM_ATT is proposed to solve this problem. First, the important parameters are optimized by improving the cuckoo search algorithm. Second, different methods are used to determine the forecast factors according to various topographic features. Finally, the model is combined with the self-attention mechanism to extract significant information. Experiments demonstrate that the proposed model has the ability to effectively improve the water level prediction accuracy and parameter optimization efficiency.</jats:p>

収録刊行物

被引用文献 (2)*注記

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ