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.
Different methods are proposed to determine the forecast factors according to different topographic features.
The self-attention mechanism is combined with LSTM.
An improved CS algorithm is proposed to optimize the parameters of ASCS_LSTM_ATT.