Study on the Optimization and Stability of Machine Learning Runoff Prediction Models in the Karst Area

Applied Sciences

Xingbi Lei¹  Chongxun Mo¹  Shufeng Lai¹  Zhenxiang Xing¹  Guangming Liu¹  Yuli Ruan¹  Mingshan Zhang² 

[1] College of Architecture and Civil Engineering, Guangxi University, Nanning 530004, China;Shandong Hydrological and Water Resources Bureau of the Yellow River Conservancy Commission, Dongying 257000, China;

关键词: support vector machine;    Elman neural network;    multi-model mean model;    model stability;    runoff prediction;    Chengbi River Karst Basin;   

DOI  :  10.3390/app12104979

来源: DOAJ

【 摘 要 】

Runoff prediction plays an extremely important role in flood prevention, mitigation, and the efficient use of water resources. Machine learning runoff prediction models have become popular due to their high computational efficiency. To select a model with a better runoff simulation and to validate the stability of the model, the following studies were done. Firstly, the support vector machine Model (SVM), the Elman Neural Network Model (ENN), and the multi-model mean model (MMM) were used for the runoff prediction, with the monthly runoff data from 1963–2007 recorded by the Pingtang hydrological station in the Chengbi River Karst Basin, China. Secondly, the comprehensive rating index method was applied to select the best model. Thirdly, the indicators of the hydrologic alteration–range of variability approach (IHA-RVA) was introduced to measure the model stability with different data structure inputs. According to the comprehensive rating index method, the SVM model outperformed the other models and was the best runoff prediction model with a score of 0.53. The overall change of the optimal model was 10.52%, which was in high stability.

【 授权许可】

Unknown