Applicability Analysis of Machine Learning Model in Hydrological Forecasting in Karst Areas - ZHAO Zejin; SUN Wei; ZHOU Bin; ZHANG Xuan; WANG Gaoxu; WU Wei; LI Wenjie; YAO Ye (Renmin Zhujiang)
For hydrological forecasting in karst areas,existing research mainly uses hydrological models based on physical mechanisms,while rare research focuses on machine learning models.To explore the applicability of machine learning models in karst areas, this paper utilizes the LSTM model and random forest model to simulate the daily runoff and field floods at Tangdian hydrological station,using the Shadian River basin in Yunnan Province as the study area.The modified Xin'anjiang model for karst areas is taken as a reference.The results show that both the machine learning model and the modified Xin'anjiang model have achieved good results in simulating the daily runoff process, with the LSTM model showing better simulation results.In the simulation of floods,the modified Xin'anjiang model achieves Class A forecast accuracy.The machine learning models have better forecast results for the 6-hour forecasting period than the modified Xin'anjiang model,while the forecast results for the 24-hour forecasting period do not meet the accuracy requirements of the forecast operation.The study provides a reference for hydrological forecasting in karst areas by studying the characteristics and forecasting accuracy of two machine learning models and a hydrologic model.