Abstract:[Abstract] In view of the complex and diverse factors that cause debris flow in mountainous environments,cause influencing factors are easily coupled to each other,and light gradient boosting machine (LightGBM) is easy to fall into local optimal problems when a debris flow prediction model is preformed,this paper proposed kernel linear discriminant analysis (KLDA) and the LightGBM prediction model that was optimized by cuckoo search (CS).Firstly,the raw data collected by the sensor was cleaned , then it was sended through KLDA for dimensional degradation processing,and the influence factors with low correlation and high factor contribution rate was obtained as the predictors.the data after the degradation was planned by random sampling method, 70% of the data is selected for model training, and others of the data is used to validate the model.After that,The training data is used as input, and the optimal prediction model is trained based on CS-LightGBM algorithm. Finally, the experimental simulation is carried out with the monitoring data of Exianggou. Experiments show that this method can reduce the complex debris flow influence factors to the predictors of modeling, and provide the prediction model with good prediction accuracy, which can provide new idea for the research of debris flow disaster prediction.